default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID40th ICML 2023: Honolulu, HI, USA
- Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett:
International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research 202, PMLR 2023 - Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. 1-18 - Ahmed Abbas, Paul Swoboda:
ClusterFuG: Clustering Fully connected Graphs by Multicut. 19-30 - Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk:
Generalization on the Unseen, Logic Reasoning and Degree Curriculum. 31-60 - Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit:
Toward Large Kernel Models. 61-78 - Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. 79-90 - Naoufal Acharki, Ramiro Lugo, Antoine Bertoncello, Josselin Garnier:
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects. 91-132 - Steven Adams, Andrea Patane, Morteza Lahijanian, Luca Laurenti:
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming. 133-151 - Atish Agarwala, Yann N. Dauphin:
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. 152-168 - Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. 169-195 - Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee:
Global optimality of Elman-type RNNs in the mean-field regime. 196-227 - Pranjal Aggarwal, Ameet Deshpande, Karthik R. Narasimhan:
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification. 228-247 - Mehran Aghabozorgi, Shichong Peng, Ke Li:
Adaptive IMLE for Few-shot Pretraining-free Generative Modelling. 248-264 - Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer:
Scaling Laws for Generative Mixed-Modal Language Models. 265-279 - Anass Aghbalou, Guillaume Staerman:
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. 280-303 - Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa:
Constrained Causal Bayesian Optimization. 304-321 - Elisabeth Agoritsas, Giovanni Catania, Aurélien Decelle, Beatriz Seoane:
Explaining the effects of non-convergent MCMC in the training of Energy-Based Models. 322-336 - Gati V. Aher, Rosa I. Arriaga, Adam Tauman Kalai:
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies. 337-371 - Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio:
Interventional Causal Representation Learning. 372-407 - Elisabeth Ailer, Jason S. Hartford, Niki Kilbertus:
Sequential Underspecified Instrument Selection for Cause-Effect Estimation. 408-420 - Matthew Aitchison, Penny Sweetser, Marcus Hutter:
Atari-5: Distilling the Arcade Learning Environment down to Five Games. 421-438 - Naveed Akhtar, Mohammad A. A. K. Jalwana:
Towards credible visual model interpretation with path attribution. 439-457 - Ahmet Alacaoglu, Hanbaek Lyu:
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data. 458-489 - Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Recasting Self-Attention with Holographic Reduced Representations. 490-507 - Wael Alghamdi, Juan Felipe Gómez, Shahab Asoodeh, Flávio P. Calmon, Oliver Kosut, Lalitha Sankar:
The Saddle-Point Method in Differential Privacy. 508-528 - Christian H. X. Ali Mehmeti-Göpel, Jan Disselhoff:
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think. 529-546 - James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. 547-568 - Youssef Allouah
, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
On the Privacy-Robustness-Utility Trilemma in Distributed Learning. 569-626 - Baris Alparslan, Sinan Yildirim, S. Ilker Birbil:
Differentially Private Distributed Bayesian Linear Regression with MCMC. 627-641 - Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch:
Robust and Scalable Bayesian Online Changepoint Detection. 642-663 - Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl:
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels. 664-690 - Sanae Amani, Tor Lattimore, András György, Lin Yang:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. 691-717 - Alan Nawzad Amin, Eli N. Weinstein, Debora Susan Marks:
A Kernelized Stein Discrepancy for Biological Sequences. 718-767 - Philip Amortila, Nan Jiang, Csaba Szepesvári:
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. 768-790 - Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko:
Meta Optimal Transport. 791-813 - Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm:
Near-Optimal Φ-Regret Learning in Extensive-Form Games. 814-839 - Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion:
A Modern Look at the Relationship between Sharpness and Generalization. 840-902 - Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with Large Step Sizes Learns Sparse Features. 903-925 - Abdul Fatir Ansari, Alvin Heng, Andre Lim, Harold Soh:
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series. 926-951 - Antonios Antoniadis, Joan Boyar, Marek Eliás, Lene Monrad Favrholdt, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon:
Paging with Succinct Predictions. 952-968 - Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, Bertrand Simon:
Mixing Predictions for Online Metric Algorithms. 969-983 - Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Exponential Smoothing for Off-Policy Learning. 984-1017 - Jamil Arbas, Hassan Ashtiani, Christopher Liaw:
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. 1018-1040 - Sohei Arisaka, Qianxiao Li:
Principled Acceleration of Iterative Numerical Methods Using Machine Learning. 1041-1059 - Raman Arora, Raef Bassily, Tomás González, Cristóbal Guzmán, Michael Menart, Enayat Ullah:
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization. 1060-1092 - Nader Asadi, MohammadReza Davari, Sudhir P. Mudur, Rahaf Aljundi, Eugene Belilovsky:
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning. 1093-1106 - Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. 1107-1120 - Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. 1121-1146 - Amit Attia, Tomer Koren:
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance. 1147-1171 - Idan Attias, Steve Hanneke:
Adversarially Robust PAC Learnability of Real-Valued Functions. 1172-1199 - Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas:
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning. 1200-1217 - Yuval Atzmon, Eli A. Meirom, Shie Mannor, Gal Chechik:
Learning to Initiate and Reason in Event-Driven Cascading Processes. 1218-1243 - Julien Aubert, Luc Lehéricy, Patricia Reynaud-Bouret:
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm. 1244-1275 - Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, Jian Zhou:
Dirichlet Diffusion Score Model for Biological Sequence Generation. 1276-1301 - Kyriakos Axiotis, Maxim Sviridenko:
Gradient Descent Converges Linearly for Logistic Regression on Separable Data. 1302-1319 - Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Naive imputation implicitly regularizes high-dimensional linear models. 1320-1340 - Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer:
Half-Hop: A graph upsampling approach for slowing down message passing. 1341-1360 - Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica:
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning. 1361-1395 - Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang:
Personalized Subgraph Federated Learning. 1396-1415 - Alexei Baevski, Arun Babu, Wei-Ning Hsu, Michael Auli:
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language. 1416-1429 - Charlotte Baey, Maud Delattre, Estelle Kuhn, Jean-Benoist Leger, Sarah Lemler:
Efficient preconditioned stochastic gradient descent for estimation in latent variable models. 1430-1453 - Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li:
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. 1454-1471 - Yushi Bai, Xin Lv, Juanzi Li, Lei Hou:
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization. 1472-1491 - Yikun Bai, Ivan Vladimir Medri, Rocio Diaz Martin, Rana Muhammad Shahroz Khan, Soheil Kolouri:
Linear optimal partial transport embedding. 1492-1520 - Justin M. Baker, Qingsong Wang, Cory D. Hauck, Bao Wang:
Implicit Graph Neural Networks: A Monotone Operator Viewpoint. 1521-1548 - Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau:
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. 1549-1563 - Oleg Balabanov, Matthias Beaupère, Laura Grigori, Victor Lederer:
Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures. 1564-1576 - Philip J. Ball, Laura M. Smith, Ilya Kostrikov, Sergey Levine:
Efficient Online Reinforcement Learning with Offline Data. 1577-1594 - Marin Ballu, Quentin Berthet:
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes. 1595-1613 - András Balogh, Márk Jelasity:
On the Functional Similarity of Robust and Non-Robust Neural Representations. 1614-1635 - Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Robust Budget Pacing with a Single Sample. 1636-1659 - Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh:
Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time. 1660-1691 - Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu:
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. 1692-1717 - Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He:
Optimizing the Collaboration Structure in Cross-Silo Federated Learning. 1718-1736 - Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel:
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation. 1737-1752 - Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. 1753-1800 - Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frédéric Precioso, Mateja Jamnik, Giuseppe Marra:
Interpretable Neural-Symbolic Concept Reasoning. 1801-1825 - Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina:
Moccasin: Efficient Tensor Rematerialization for Neural Networks. 1826-1837 - Raef Bassily, Ziteng Sun:
User-level Private Stochastic Convex Optimization with Optimal Rates. 1838-1851 - Soumya Basu, Ankit Singh Rawat, Manzil Zaheer:
A Statistical Perspective on Retrieval-Based Models. 1852-1886 - Jakob Bauer, Kate Baumli, Feryal M. P. Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M. Zhang:
Human-Timescale Adaptation in an Open-Ended Task Space. 1887-1935 - Jerome Baum, Heishiro Kanagawa, Arthur Gretton:
A Kernel Stein Test of Goodness of Fit for Sequential Models. 1936-1953 - Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. 1954-1977 - Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. 1978-2002 - Daniel Beechey, Thomas M. S. Smith, Özgür Simsek:
Explaining Reinforcement Learning with Shapley Values. 2003-2014 - Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov:
TIDE: Time Derivative Diffusion for Deep Learning on Graphs. 2015-2030 - Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder:
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. 2031-2049 - Christopher M. Bender, Yifeng Shi, Marc Niethammer, Junier Oliva:
Continuously Parameterized Mixture Models. 2050-2062 - Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny:
Controllable Neural Symbolic Regression. 2063-2077 - Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. 2078-2091 - M. Amine Bennouna, Ryan Lucas, Bart P. G. Van Parys:
Certified Robust Neural Networks: Generalization and Corruption Resistance. 2092-2112 - Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick:
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. 2113-2163 - Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Francesco Trovò, Nicola Gatti:
Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion. 2164-2183 - Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò, Nicola Gatti:
Constrained Phi-Equilibria. 2184-2205 - Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. 2206-2233 - Arturs Berzins:
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision. 2234-2244 - Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis:
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks. 2245-2271 - Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic:
Neural Algorithmic Reasoning with Causal Regularisation. 2272-2288 - Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol:
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. 2289-2312 - Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Bandit Online Linear Optimization with Hints and Queries. 2313-2336 - Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai:
Improved Online Conformal Prediction via Strongly Adaptive Online Learning. 2337-2363 - Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri:
Data-Copying in Generative Models: A Formal Framework. 2364-2396 - Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, Oskar van der Wal:
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling. 2397-2430 - Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan:
StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes. 2431-2451 - Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. 2452-2470 - Julian Bitterwolf, Maximilian Müller, Matthias Hein:
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. 2471-2506 - Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Thomas Kipf:
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames. 2507-2527 - Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang:
Understanding Oversquashing in GNNs through the Lens of Effective Resistance. 2528-2547 - Charlie Blake, Douglas Orr, Carlo Luschi:
Unit Scaling: Out-of-the-Box Low-Precision Training. 2548-2576 - Matthieu Blanke, Marc Lelarge:
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems. 2577-2591 - Markus Bläser:
Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits. 2592-2602 - Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux:
Learning the Dynamics of Sparsely Observed Interacting Systems. 2603-2640 - Niclas Boehmer, L. Elisa Celis, Lingxiao Huang, Anay Mehrotra, Nisheeth K. Vishnoi:
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions. 2641-2688 - Niclas Boehmer, Piotr Faliszewski, Sonja Kraiczy:
Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist. 2689-2711 - David Boetius, Stefan Leue, Tobias Sutter:
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks. 2712-2737 - Simone Bombari, Shayan Kiyani, Marco Mondelli:
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels. 2738-2776 - Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. 2777-2805 - Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar:
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. 2806-2823 - Victor Boone, Bruno Gaujal:
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning. 2824-2856 - Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete:
Model-agnostic Measure of Generalization Difficulty. 2857-2884 - Shahine Bouabid, Jake Fawkes, Dino Sejdinovic:
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. 2885-2913 - Malik Boudiaf, Tom Denton, Bart van Merrienboer, Vincent Dumoulin, Eleni Triantafillou:
In Search for a Generalizable Method for Source Free Domain Adaptation. 2914-2931 - Adam Bouland, Yosheb M. Getachew, Yujia Jin, Aaron Sidford, Kevin Tian:
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. 2932-2952 - Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, Thomas Serre:
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? 2953-3002 - Michael Bowling, John D. Martin, David Abel, Will Dabney:
Settling the Reward Hypothesis. 3003-3020 - Manuel Brack, Patrick Schramowski, Björn Deiseroth, Kristian Kersting:
ILLUME: Rationalizing Vision-Language Models through Human Interactions. 3021-3037 - Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. 3038-3062 - Gecia Bravo Hermsdorff:
Quantifying Human Priors over Social and Navigation Networks. 3063-3105 - Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar:
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss. 3106-3147 - Trenton Bricken, Rylan Schaeffer, Bruno A. Olshausen, Gabriel Kreiman:
Emergence of Sparse Representations from Noise. 3148-3191 - Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. 3192-3218 - Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao:
Machine Learning Force Fields with Data Cost Aware Training. 3219-3232 - Róbert Istvan Busa-Fekete, Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii:
Label differential privacy and private training data release. 3233-3251 - Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti:
The SSL Interplay: Augmentations, Inductive Bias, and Generalization. 3252-3298 - Federico Cacciamani, Matteo Castiglioni, Nicola Gatti:
Online Mechanism Design for Information Acquisition. 3299-3326 - Vittorio Caggiano, Sudeep Dasari, Vikash Kumar:
MyoDex: A Generalizable Prior for Dexterous Manipulation. 3327-3346 - Francesco Cagnetta, Alessandro Favero, Matthieu Wyart:
What Can Be Learnt With Wide Convolutional Neural Networks? 3347-3379 - Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang:
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. 3380-3407 - Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang:
On the Connection Between MPNN and Graph Transformer. 3408-3430 - Dongqi Cai, Yangyuxuan Kang, Anbang Yao, Yurong Chen:
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition. 3431-3441 - Yuchao Cai, Yuheng Ma, Yiwei Dong, Hanfang Yang:
Extrapolated Random Tree for Regression. 3442-3468 - Xufeng Cai, Chaobing Song, Stephen J. Wright, Jelena Diakonikolas:
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization. 3469-3494 - Ruisi Cai, Zhenyu Zhang, Zhangyang Wang:
Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights? 3495-3506 - Yang Cai, Weiqiang Zheng:
Doubly Optimal No-Regret Learning in Monotone Games. 3507-3524 - Mine Melodi Caliskan, Francesco Chini, Setareh Maghsudi:
Multi-Agent Learning from Learners. 3525-3540 - Yadi Cao, Menglei Chai, Minchen Li, Chenfanfu Jiang:
Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network. 3541-3558 - Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Tobias Schaefer, Matthias Katzfuss:
Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization. 3559-3576 - Shengcao Cao, Mengtian Li, James Hays, Deva Ramanan, Yu-Xiong Wang, Liangyan Gui:
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation. 3577-3598 - Steven Cao, Percy Liang, Gregory Valiant:
One-sided Matrix Completion from Two Observations Per Row. 3599-3624 - Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson:
State and parameter learning with PARIS particle Gibbs. 3625-3675 - Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, Pierre-Yves Oudeyer:
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning. 3676-3713 - Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier:
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning. 3714-3731 - Alberto Castellini, Federico Bianchi, Edoardo Zorzi, Thiago D. Simão, Alessandro Farinelli, Matthijs T. J. Spaan:
Scalable Safe Policy Improvement via Monte Carlo Tree Search. 3732-3756 - Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson:
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. 3757-3781 - Rémi Catellier, Samuel Vaiter, Damien Garreau:
On the Robustness of Text Vectorizers. 3782-3814 - Juan Cerviño, Luiz F. O. Chamon, Benjamin David Haeffele, René Vidal, Alejandro Ribeiro:
Learning Globally Smooth Functions on Manifolds. 3815-3854 - Jaeyoung Cha, Jaewook Lee, Chulhee Yun:
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond. 3855-3912 - Jaehoon Cha, Jeyan Thiyagalingam:
Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations. 3913-3948 - Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. 3949-3978 - Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari:
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits. 3979-4008 - Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa:
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. 4009-4034 - Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan:
Memory-Based Dual Gaussian Processes for Sequential Learning. 4035-4054 - Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan:
Muse: Text-To-Image Generation via Masked Generative Transformers. 4055-4075 - Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee:
On Investigating the Conservative Property of Score-Based Generative Models. 4076-4095 - Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni:
Robust and private stochastic linear bandits. 4096-4115 - Anamay Chaturvedi, Huy L. Nguyen, Thy Dinh Nguyen:
Streaming Submodular Maximization with Differential Privacy. 4116-4143 - Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz:
Why does Throwing Away Data Improve Worst-Group Error? 4144-4188 - Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant:
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits. 4189-4217 - Fengdi Che, Gautham Vasan, A. Rupam Mahmood:
Correcting discount-factor mismatch in on-policy policy gradient methods. 4218-4240 - Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, Jun Huan:
Fast Federated Machine Unlearning with Nonlinear Functional Theory. 4241-4268 - David Cheikhi, Daniel Russo:
On the Statistical Benefits of Temporal Difference Learning. 4269-4293 - Zhengdao Chen:
Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces. 4294-4329 - Lei Chen, Joan Bruna:
Beyond the Edge of Stability via Two-step Gradient Updates. 4330-4391 - Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Ting-Wei Chen, Tien-Hao Chang:
Trompt: Towards a Better Deep Neural Network for Tabular Data. 4392-4434 - Du Chen, Geoffrey A. Chua:
Differentially Private Stochastic Convex Optimization under a Quantile Loss Function. 4435-4461 - Sitan Chen, Giannis Daras, Alex Dimakis:
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. 4462-4484 - Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka:
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. 4485-4513 - Siyuan Chen, Pratik Pramod Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines. 4514-4528 - Yanzhi Chen, Michael U. Gutmann, Adrian Weller:
Is Learning Summary Statistics Necessary for Likelihood-free Inference? 4529-4544 - Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang:
Subequivariant Graph Reinforcement Learning in 3D Environments. 4545-4565 - Hanxiao Chen, Meng Hao, Hongwei Li, Kangjie Chen, Guowen Xu, Tianwei Zhang, Xilin Zhang:
GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. 4566-4584 - Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu:
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. 4585-4610 - Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang:
Evolving Semantic Prototype Improves Generative Zero-Shot Learning. 4611-4622 - Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, Zhi-Ming Ma:
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization. 4623-4640 - Xuxing Chen, Minhui Huang, Shiqian Ma, Krishna Balasubramanian:
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity. 4641-4671 - Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang:
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. 4672-4712 - Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Sample Complexity of Probability Divergences under Group Symmetry. 4713-4734 - Hongrui Chen, Holden Lee, Jianfeng Lu:
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions. 4735-4763 - Yineng Chen, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao:
Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers. 4764-4803 - Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang:
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. 4804-4825 - Keyi Chen, Francesco Orabona:
Generalized Implicit Follow-The-Regularized-Leader. 4826-4838 - Dexiong Chen, Paolo Pellizzoni, Karsten M. Borgwardt:
Fisher Information Embedding for Node and Graph Learning. 4839-4855 - Jiaxuan Chen, Yu Qi, Gang Pan:
Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues. 4856-4866 - Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha:
Stratified Adversarial Robustness with Rejection. 4867-4894 - Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal:
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning. 4895-4920 - Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu:
Model Transferability with Responsive Decision Subjects. 4921-4952 - Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta:
Layered State Discovery for Incremental Autonomous Exploration. 4953-5001 - Sijia Chen, Wei-Wei Tu, Peng Zhao, Lijun Zhang:
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization. 5002-5035 - Xuxi Chen, Nelson Vadori, Tianlong Chen, Zhangyang Wang:
Learning to Optimize Differentiable Games. 5036-5051 - Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen, Xiang Yan, Xiaotie Deng:
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets. 5052-5086 - Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan:
Semi-Offline Reinforcement Learning for Optimized Text Generation. 5087-5103 - Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai:
Lower Bounds for Learning in Revealing POMDPs. 5104-5161 - Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen:
Implicit Neural Spatial Representations for Time-dependent PDEs. 5162-5177 - Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Wanxiang Che, Xiangzhan Yu, Furu Wei:
BEATs: Audio Pre-Training with Acoustic Tokenizers. 5178-5193 - Siyu Chen, Jibang Wu, Yifan Wu, Zhuoran Yang:
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model. 5194-5218 - Lesi Chen, Jing Xu, Luo Luo:
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. 5219-5233 - Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li:
Efficient Personalized Federated Learning via Sparse Model-Adaptation. 5234-5256 - Yifan Chen, Rentian Yao, Yun Yang, Jie Chen:
A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening. 5257-5281 - Dangxing Chen, Weicheng Ye:
How to address monotonicity for model risk management? 5282-5295 - Xin Chen, Yicheng Zeng, Siyue Yang, Qiang Sun:
Sketched Ridgeless Linear Regression: The Role of Downsampling. 5296-5326 - Dingyang Chen, Qi Zhang:
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning. 5327-5350 - Can Chen, Yingxue Zhang, Xue Liu, Mark Coates:
Bidirectional Learning for Offline Model-based Biological Sequence Design. 5351-5366 - Tianqi Chen, Mingyuan Zhou:
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling. 5367-5382 - Wuyang Chen, Yanqi Zhou, Nan Du, Yanping Huang, James Laudon, Zhifeng Chen, Claire Cui:
Lifelong Language Pretraining with Distribution-Specialized Experts. 5383-5395 - Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu:
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. 5396-5427 - Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng:
Weakly Supervised Regression with Interval Targets. 5428-5448 - Chin-Yi Cheng, Forrest Huang, Gang Li, Yang Li:
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion. 5449-5471 - Minhao Cheng, Rui Min, Haochen Sun, Pin-Yu Chen:
Identification of the Adversary from a Single Adversarial Example. 5472-5484 - Xiaotong Cheng, Cheng Pan, Setareh Maghsudi:
Parallel Online Clustering of Bandits via Hedonic Game. 5485-5503 - Yong Cheng, Yu Zhang, Melvin Johnson, Wolfgang Macherey, Ankur Bapna:
Mu2SLAM: Multitask, Multilingual Speech and Language Models. 5504-5520 - Duo Cheng, Xingyu Zhou, Bo Ji:
Understanding the Role of Feedback in Online Learning with Switching Costs. 5521-5543 - David Chiang, Peter Cholak, Anand Pillay:
Tighter Bounds on the Expressivity of Transformer Encoders. 5544-5562 - Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge:
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. 5563-5599 - Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge:
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. 5600-5615 - Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. 5616-5630 - Hong-Ming Chiu, Richard Y. Zhang:
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations. 5631-5660 - Cheol Jun Cho, Edward F. Chang, Gopala Krishna Anumanchipalli:
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data. 5661-5676 - Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang:
On the Convergence of Federated Averaging with Cyclic Client Participation. 5677-5721 - Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho:
GREAD: Graph Neural Reaction-Diffusion Networks. 5722-5747 - Hee Min Choi, Hyoa Kang, Dokwan Oh:
Is Overfitting Necessary for Implicit Video Representation? 5748-5770 - Young-Geun Choi, Gi-Soo Kim, Yunseo Choi, Wooseong Cho, Myunghee Cho Paik, Min-hwan Oh:
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model. 5771-5786 - Jaemoo Choi, Yesom Park, Myungjoo Kang:
Restoration based Generative Models. 5787-5816 - Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash:
Concept-based Explanations for Out-of-Distribution Detectors. 5817-5837 - Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya:
Active causal structure learning with advice. 5838-5867 - Davin Choo, Kirankumar Shiragur:
New metrics and search algorithms for weighted causal DAGs. 5868-5903 - Nicolas Chopin, Andras Fulop, Jeremy Heng, Alexandre H. Thiery:
Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions. 5904-5923 - Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta:
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. 5924-5963 - Krzysztof Marcin Choromanski:
Taming graph kernels with random features. 5964-5977 - Krzysztof Marcin Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Deepali Jain, Tamás Sarlós, Snigdha Chaturvedi, Adrian Weller:
Efficient Graph Field Integrators Meet Point Clouds. 5978-6004 - Era Choshen, Aviv Tamar:
ContraBAR: Contrastive Bayes-Adaptive Deep RL. 6005-6027 - Rishav Chourasia, Neil Shah:
Forget Unlearning: Towards True Data-Deletion in Machine Learning. 6028-6073 - Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen:
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. 6074-6114 - Rhea Chowers, Yair Weiss:
What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective. 6115-6139 - Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica:
Unifying Molecular and Textual Representations via Multi-task Language Modelling. 6140-6157 - Xu Chu, Yujie Jin, Xin Wang, Shanghang Zhang, Yasha Wang, Wenwu Zhu, Hong Mei:
Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks. 6158-6184 - Yu-Min Chu, Chieh Liu, Ting-I Hsieh, Hwann-Tzong Chen, Tyng-Luh Liu:
Shape-Guided Dual-Memory Learning for 3D Anomaly Detection. 6185-6194 - Jianing Chu, Shu Yang, Wenbin Lu:
Multiply Robust Off-policy Evaluation and Learning under Truncation by Death. 6195-6227 - Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis:
InfoOT: Information Maximizing Optimal Transport. 6228-6242 - Bilal Chughtai, Lawrence Chan, Neel Nanda:
A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations. 6243-6267 - Jase Clarkson:
Distribution Free Prediction Sets for Node Classification. 6268-6278 - Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani:
Sequential Strategic Screening. 6279-6295 - David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon:
Few-Sample Feature Selection via Feature Manifold Learning. 6296-6319 - Elijah Cole, Grant Van Horn, Christian Lange, Alexander Shepard, Patrick Leary, Pietro Perona, Scott Loarie, Oisin Mac Aodha:
Spatial Implicit Neural Representations for Global-Scale Species Mapping. 6320-6342 - Andrea Coletta, Svitlana Vyetrenko, Tucker Balch:
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs. 6343-6363 - Armand Comas Massague, Yilun Du, Christian Fernandez Lopez, Sandesh Ghimire, Mario Sznaier, Joshua B. Tenenbaum, Octavia I. Camps:
Inferring Relational Potentials in Interacting Systems. 6364-6383 - Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye:
Task-specific experimental design for treatment effect estimation. 6384-6401 - Elisabetta Cornacchia, Elchanan Mossel:
A Mathematical Model for Curriculum Learning for Parities. 6402-6423 - Ian Connick Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, Su-In Lee:
Learning to Maximize Mutual Information for Dynamic Feature Selection. 6424-6447 - Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang:
Rethinking Weak Supervision in Helping Contrastive Learning. 6448-6467 - Hugo Cui, Florent Krzakala, Lenka Zdeborová:
Bayes-optimal Learning of Deep Random Networks of Extensive-width. 6468-6521 - Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang:
A General Representation Learning Framework with Generalization Performance Guarantees. 6522-6544 - Yuning Cui, Wenqi Ren, Sining Yang, Xiaochun Cao, Alois Knoll:
IRNeXt: Rethinking Convolutional Network Design for Image Restoration. 6545-6564 - Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. 6565-6590 - Yiming Cui, Linjie Yang, Haichao Yu:
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation. 6591-6602 - Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. 6603-6622 - Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. 6623-6642 - Ashok Cutkosky, Harsh Mehta, Francesco Orabona:
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion. 6643-6670 - Marco Cuturi, Michal Klein, Pierre Ablin:
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. 6671-6682 - Edwige Cyffers, Aurélien Bellet, Debabrota Basu:
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. 6683-6711 - Yanbo Dai, Songze Li:
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning. 6712-6725 - Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert:
Refined Regret for Adversarial MDPs with Linear Function Approximation. 6726-6759 - Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal:
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. 6760-6785 - Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. 6786-6817 - Brett Daley, Martha White, Christopher Amato, Marlos C. Machado:
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. 6818-6835 - Hadi Daneshmand, Jason D. Lee, Chi Jin:
Efficient displacement convex optimization with particle gradient descent. 6836-6854 - Ronghao Dang, Lu Chen, Liuyi Wang, Zongtao He, Chengju Liu, Qijun Chen:
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation. 6855-6872 - Hien Dang, Tho Tran Huu, Stanley J. Osher, Hung Tran-The, Nhat Ho, Tan Minh Nguyen:
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. 6873-6947 - Christoph Dann, Yishay Mansour, Mehryar Mohri:
Reinforcement Learning Can Be More Efficient with Multiple Rewards. 6948-6967 - Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. 6968-7008 - Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Fengting Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia:
Image generation with shortest path diffusion. 7009-7024 - Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen:
Efficient List-Decodable Regression using Batches. 7025-7065 - Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. 7066-7101 - Rudrajit Das, Sujay Sanghavi:
Understanding Self-Distillation in the Presence of Label Noise. 7102-7140 - Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty, Swagatam Das:
Interval Bound Interpolation for Few-shot Learning with Few Tasks. 7141-7166 - Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. 7167-7204 - Sami Davies, Benjamin Moseley, Heather Newman:
Fast Combinatorial Algorithms for Min Max Correlation Clustering. 7205-7230 - Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang:
Predictive Flows for Faster Ford-Fulkerson. 7231-7248 - Thomas Davies, Zhengchao Wan, Rubén J. Sánchez-García:
The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data. 7249-7263 - Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne:
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. 7264-7302 - Hassan Dbouk, Naresh R. Shanbhag:
On the Robustness of Randomized Ensembles to Adversarial Perturbations. 7303-7328 - Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Joshua Ainslie, Sumit Sanghai, Fei Sha, William W. Cohen:
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute. 7329-7342 - Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David van Dijk:
Continuous Spatiotemporal Transformer. 7343-7365 - Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein:
The Value of Out-of-Distribution Data. 7366-7389 - Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker:
High Fidelity Image Counterfactuals with Probabilistic Causal Models. 7390-7425 - Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla:
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. 7426-7448 - Aaron Defazio, Konstantin Mishchenko:
Learning-Rate-Free Learning by D-Adaptation. 7449-7479 - Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby:
Scaling Vision Transformers to 22 Billion Parameters. 7480-7512 - Blaise Delattre, Quentin Barthélemy, Alexandre Araujo, Alexandre Allauzen:
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration. 7513-7532 - Emir Demirovic, Emmanuel Hebrard, Louis Jean:
Blossom: an Anytime Algorithm for Computing Optimal Decision Trees. 7533-7562 - Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. 7563-7595 - Danruo Deng, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng:
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning. 7596-7616 - Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Multi-channel Autobidding with Budget and ROI Constraints. 7617-7644 - Shikuang Deng, Hao Lin, Yuhang Li, Shi Gu:
Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks. 7645-7657 - Weijian Deng, Yumin Suh, Stephen Gould, Liang Zheng:
Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation. 7658-7674 - Ailin Deng, Miao Xiong, Bryan Hooi:
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement. 7675-7693 - Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Shanmukha Ramakrishna Vedantam:
Hyperbolic Image-text Representations. 7694-7731 - Aditya Desai, Keren Zhou, Anshumali Shrivastava:
Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing. 7732-7749 - Tim Dettmers, Luke Zettlemoyer:
The case for 4-bit precision: k-bit Inference Scaling Laws. 7750-7774 - Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova:
Fairness in Matching under Uncertainty. 7775-7794 - Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse:
Efficient Parametric Approximations of Neural Network Function Space Distance. 7795-7812 - Victor Dheur, Souhaib Ben Taieb:
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. 7813-7836 - Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. 7837-7864 - Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. 7865-7885 - Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. 7886-7921 - Ilias Diakonikolas, Daniel Kane, Lisheng Ren:
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. 7922-7938 - Nathaniel Lee Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia:
Improving Graph Generation by Restricting Graph Bandwidth. 7939-7959 - Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim:
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space. 7960-7991 - Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh:
Subset-Based Instance Optimality in Private Estimation. 7992-8014 - Nikolaos Dimitriadis, Pascal Frossard, François Fleuret:
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models. 8015-8052 - Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone:
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models. 8053-8066 - Lisang Ding, Kexin Jin, Bicheng Ying, Kun Yuan, Wotao Yin:
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm. 8067-8089 - Zheng Ding, Jieke Wang, Zhuowen Tu:
Open-Vocabulary Universal Image Segmentation with MaskCLIP. 8090-8102 - Ziluo Ding, Wanpeng Zhang, Junpeng Yue, Xiangjun Wang, Tiejun Huang, Zongqing Lu:
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning. 8103-8119 - AnhDung Dinh, Daochang Liu, Chang Xu:
PixelAsParam: A Gradient View on Diffusion Sampling with Guidance. 8120-8137 - Nikita Doikov, El Mahdi Chayti, Martin Jaggi:
Second-Order Optimization with Lazy Hessians. 8138-8161 - Nikita Doikov, Anton Rodomanov:
Polynomial Preconditioning for Gradient Methods. 8162-8187 - Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Georgios Arvanitidis, Bernhard Schölkopf:
On Data Manifolds Entailed by Structural Causal Models. 8188-8201 - Mingze Dong, Yuval Kluger:
Towards Understanding and Reducing Graph Structural Noise for GNNs. 8202-8226 - Peiyan Dong, Zhenglun Kong, Xin Meng, Peng Zhang, Hao Tang, Yanzhi Wang, Chih-Hsien Chou:
SpeedDETR: Speed-aware Transformers for End-to-end Object Detection. 8227-8243 - Chengyu Dong, Liyuan Liu, Hao Cheng, Jingbo Shang, Jianfeng Gao, Xiaodong Liu:
Understand and Modularize Generator Optimization in ELECTRA-style Pretraining. 8244-8259 - Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. 8260-8275 - Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X. Fang, Vahid Tarokh:
PASTA: Pessimistic Assortment Optimization. 8276-8295 - Yijun Dong, Yuege Xie, Rachel A. Ward:
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift. 8296-8316 - Jialin Dong, Lin Yang:
Does Sparsity Help in Learning Misspecified Linear Bandits? 8317-8333 - Heng Dong, Junyu Zhang, Tonghan Wang, Chongjie Zhang:
Symmetry-Aware Robot Design with Structured Subgroups. 8334-8355 - Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy:
DoCoFL: Downlink Compression for Cross-Device Federated Learning. 8356-8388 - Will Dorrell, Maria Yuffa, Peter E. Latham:
Meta-Learning the Inductive Bias of Simple Neural Circuits. 8389-8402 - Vishwaraj Doshi, Jie Hu, Do Young Eun:
Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains. 8403-8423 - Matthew Dowling, Yuan Zhao, Il Memming Park:
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains. 8424-8448 - Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe:
On the Convergence Rate of Gaussianization with Random Rotations. 8449-8468 - Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence:
PaLM-E: An Embodied Multimodal Language Model. 8469-8488 - Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. 8489-8510 - Yihan Du, Longbo Huang, Wen Sun:
Multi-task Representation Learning for Pure Exploration in Linear Bandits. 8511-8564 - Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin:
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows. 8565-8584 - Jin-Hong Du, Pratik Patil, Arun K. Kuchibhotla:
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation. 8585-8631 - Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Tianyuan Yuan, Yue Wang, Yang Yuan, Hang Zhao:
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning. 8632-8656 - Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas:
Guiding Pretraining in Reinforcement Learning with Large Language Models. 8657-8677 - Weitao Du, He Zhang, Tao Yang, Yuanqi Du:
A Flexible Diffusion Model. 8678-8696 - Chenguang Duan, Yuling Jiao, Lican Kang, Xiliang Lu, Jerry Zhijian Yang:
Fast Excess Risk Rates via Offset Rademacher Complexity. 8697-8716 - Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu:
Are Diffusion Models Vulnerable to Membership Inference Attacks? 8717-8730 - Zhibin Duan, Xinyang Liu, Yudi Su, Yishi Xu, Bo Chen, Mingyuan Zhou:
Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process. 8731-8746 - Zhijian Duan, Yunxuan Ma, Xiaotie Deng:
Are Equivariant Equilibrium Approximators Beneficial? 8747-8778 - Yann Dubois, Tatsunori Hashimoto, Percy Liang:
Evaluating Self-Supervised Learning via Risk Decomposition. 8779-8820 - Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam:
Fully Dynamic Submodular Maximization over Matroids. 8821-8835 - Paul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi Wang:
Optimal No-Regret Learning for One-Sided Lipschitz Functions. 8836-8850 - Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori:
Integrating Prior Knowledge in Contrastive Learning with Kernel. 8851-8878 - Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic:
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. 8879-8901 - Lyndon R. Duong, David Lipshutz, David J. Heeger, Dmitri B. Chklovskii, Eero P. Simoncelli:
Adaptive Whitening in Neural Populations with Gain-modulating Interneurons. 8902-8921 - Benjamin Dupuis, George Deligiannidis, Umut Simsekli:
Generalization Bounds using Data-Dependent Fractal Dimensions. 8922-8968 - Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman:
Multi-Objective Population Based Training. 8969-8989 - Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson:
Neural Diffusion Processes. 8990-9012 - Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick:
FAENet: Frame Averaging Equivariant GNN for Materials Modeling. 9013-9033 - Javier E. Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin:
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces. 9034-9059 - Eduard Eiben, Robert Ganian, Iyad A. Kanj, Sebastian Ordyniak, Stefan Szeider:
The Computational Complexity of Concise Hypersphere Classification. 9060-9070 - Floor Eijkelboom, Rob Hesselink, Erik J. Bekkers:
E(n) Equivariant Message Passing Simplicial Networks. 9071-9081 - Itay Eilat, Nir Rosenfeld:
Performative Recommendation: Diversifying Content via Strategic Incentives. 9082-9103 - Theresa Eimer, Marius Lindauer, Roberta Raileanu:
Hyperparameters in Reinforcement Learning and How To Tune Them. 9104-9149 - Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski:
Fairness in Streaming Submodular Maximization over a Matroid Constraint. 9150-9171 - Marwa El Halabi, George Orfanides, Tim Hoheisel:
Difference of submodular minimization via DC programming. 9172-9201 - Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron:
Graph Positional Encoding via Random Feature Propagation. 9202-9223 - Moshe Eliasof, Lars Ruthotto, Eran Treister:
Improving Graph Neural Networks with Learnable Propagation Operators. 9224-9245 - Dor Elimelech, Wasim Huleihel:
Phase Transitions in the Detection of Correlated Databases. 9246-9266 - Yury Elkin, Vitaliy Kurlin:
A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree. 9267-9311 - Mark Endo, Joy Hsu, Jiaman Li, Jiajun Wu:
Motion Question Answering via Modular Motion Programs. 9312-9328 - Joseph Enguehard:
Learning Perturbations to Explain Time Series Predictions. 9329-9342 - Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour:
Regret Minimization and Convergence to Equilibria in General-sum Markov Games. 9343-9373 - Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Yevgeny Seldin:
Delayed Bandits: When Do Intermediate Observations Help? 9374-9395 - Carlos Esteves, Jean-Jacques E. Slotine, Ameesh Makadia:
Scaling Spherical CNNs. 9396-9411 - Mathieu Even:
Stochastic Gradient Descent under Markovian Sampling Schemes. 9412-9439 - Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry:
Continual Learning in Linear Classification on Separable Data. 9440-9484 - Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov:
A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. 9485-9507 - Lukas Faber, Roger Wattenhofer:
Neural Status Registers. 9508-9522 - Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. 9523-9546 - Gregory Faletto, Jacob Bien:
Predicting Rare Events by Shrinking Towards Proportional Odds. 9547-9602 - Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson:
Free-Form Variational Inference for Gaussian Process State-Space Models. 9603-9622 - Ying Fan, Kangwook Lee:
Optimizing DDPM Sampling with Shortcut Fine-Tuning. 9623-9639 - Chenglin Fan, Ping Li, Xiaoyun Li:
LSDS++ : Dual Sampling for Accelerated k-means++. 9640-9649 - Zhenan Fan, Xinglu Wang, Oleksandr Yakovenko, Abdullah Ali Sivas, Owen Ren, Yong Zhang, Zirui Zhou:
Smart Initial Basis Selection for Linear Programs. 9650-9664 - Vladimir Fanaskov, Tianchi Yu, Alexander Rudikov, Ivan V. Oseledets:
General Covariance Data Augmentation for Neural PDE Solvers. 9665-9688 - Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen:
The Fast Johnson-Lindenstrauss Transform Is Even Faster. 9689-9715 - Guanhua Fang, Ping Li:
Regression with Label Permutation in Generalized Linear Model. 9716-9760 - Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
Robust Collaborative Learning with Linear Gradient Overhead. 9761-9813 - Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy:
Neural FIM for learning Fisher information metrics from point cloud data. 9814-9826 - Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. 9827-9869 - Jonathan Feldstein, Modestas Jurcius, Efthymia Tsamoura:
Parallel Neurosymbolic Integration with Concordia. 9870-9885 - Mattie Fellows, Matthew J. A. Smith, Shimon Whiteson:
Why Target Networks Stabilise Temporal Difference Methods. 9886-9909 - Dieqiao Feng, Yuanqi Du, Carla P. Gomes, Bart Selman:
Weighted Sampling without Replacement for Deep Top-k Classification. 9910-9920 - Zhe Feng, Christopher Liaw, Zixin Zhou:
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. 9921-9937 - Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma:
Fractional Denoising for 3D Molecular Pre-training. 9938-9961 - Ying Feng, David P. Woodruff:
Improved Algorithms for White-Box Adversarial Streams. 9962-9975 - Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. 9976-10007 - Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux:
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. 10008-10033 - Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian:
SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems. 10034-10052 - Patrick Fernandes, Behrooz Ghorbani, Xavier Garcia, Markus Freitag, Orhan Firat:
Scaling Laws for Multilingual Neural Machine Translation. 10053-10071 - Hendrik Fichtenberger, Monika Henzinger, Jalaj Upadhyay:
Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation. 10072-10092 - Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko:
Adapting to game trees in zero-sum imperfect information games. 10093-10135 - Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. 10136-10152 - Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna M. Wardlaw, Grant Mair, Emanuele Trucco, Amos J. Storkey:
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging. 10153-10169 - Alexandre Forel, Axel Parmentier, Thibaut Vidal:
Explainable Data-Driven Optimization: From Context to Decision and Back Again. 10170-10187 - Dylan J. Foster, Noah Golowich, Sham M. Kakade:
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. 10188-10221 - Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D. Cantwell, Anil Anthony Bharath:
Disentangled Generative Models for Robust Prediction of System Dynamics. 10222-10248 - Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Can Forward Gradient Match Backpropagation? 10249-10264 - Ayoub Foussoul, Vineet Goyal, Orestis Papadigenopoulos, Assaf Zeevi:
Last Switch Dependent Bandits with Monotone Payoff Functions. 10265-10284 - Emanuele Francazi, Marco Baity-Jesi, Aurélien Lucchi:
A Theoretical Analysis of the Learning Dynamics under Class Imbalance. 10285-10322 - Elias Frantar, Dan Alistarh:
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot. 10323-10337 - Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset:
Learning Temporally AbstractWorld Models without Online Experimentation. 10338-10356 - Gideon Joseph Freund, Elad Sarafian, Sarit Kraus:
A Coupled Flow Approach to Imitation Learning. 10357-10372 - Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen, Armin W. Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Ré:
Simple Hardware-Efficient Long Convolutions for Sequence Modeling. 10373-10391 - Yang Fu, Ishan Misra, Xiaolong Wang:
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses. 10392-10404 - Yao Fu, Run Peng, Honglak Lee:
Go Beyond Imagination: Maximizing Episodic Reachability with World Models. 10405-10420 - Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot:
Specializing Smaller Language Models towards Multi-Step Reasoning. 10421-10430 - Qiang Fu, Dongchu Xu, Ashia Camage Wilson:
Accelerated Stochastic Optimization Methods under Quasar-convexity. 10431-10460 - Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris:
Meta-learning Parameterized Skills. 10461-10481 - Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan Celine Lin:
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations. 10482-10493 - Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo:
Hierarchies of Reward Machines. 10494-10541 - Advait Harshal Gadhikar, Sohom Mukherjee, Rebekka Burkholz:
Why Random Pruning Is All We Need to Start Sparse. 10542-10570 - Quentin Gallouédec, Emmanuel Dellandréa:
Cell-Free Latent Go-Explore. 10571-10586 - Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira:
Graph Reinforcement Learning for Network Control via Bi-Level Optimization. 10587-10610 - Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang:
Why Is Public Pretraining Necessary for Private Model Training? 10611-10627 - Roy Ganz, Bahjat Kawar, Michael Elad:
Do Perceptually Aligned Gradients Imply Robustness? 10628-10648 - Wenzhi Gao, Dongdong Ge, Chunlin Sun, Yinyu Ye:
Solving Linear Programs with Fast Online Learning Algorithms. 10649-10675 - Yihang Gao, Yiqi Gu, Michael Ng:
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks. 10676-10707 - Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. 10708-10726 - Ruijiang Gao, Himabindu Lakkaraju:
On the Impact of Algorithmic Recourse on Social Segregation. 10727-10743 - Rui Gao, Weiwei Liu:
DDGR: Continual Learning with Deep Diffusion-based Generative Replay. 10744-10763 - Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig:
PAL: Program-aided Language Models. 10764-10799 - Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang:
Out-of-Domain Robustness via Targeted Augmentations. 10800-10834 - Leo Gao, John Schulman, Jacob Hilton:
Scaling Laws for Reward Model Overoptimization. 10835-10866 - Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Melvin Johnson, Orhan Firat:
The Unreasonable Effectiveness of Few-shot Learning for Machine Translation. 10867-10878 - Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. 10879-10928 - Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun:
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank. 10929-10974 - Quentin Garrido, Laurent Najman, Yann LeCun:
Self-supervised learning of Split Invariant Equivariant representations. 10975-10996 - Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh:
Federated Heavy Hitter Recovery under Linear Sketching. 10997-11012 - Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal:
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization. 11013-11049 - Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song:
A Reinforcement Learning Framework for Dynamic Mediation Analysis. 11050-11097 - Tomas Geffner, George Papamakarios, Andriy Mnih:
Compositional Score Modeling for Simulation-Based Inference. 11098-11116 - Jonas Geiping, Tom Goldstein:
Cramming: Training a Language Model on a single GPU in one day. 11117-11143 - Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. 11144-11172 - Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness:
Memory-Based Meta-Learning on Non-Stationary Distributions. 11173-11195 - Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si:
Towards Reliable Neural Specifications. 11196-11212 - Matthias Gerstgrasser, David C. Parkes:
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning. 11213-11236 - Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Approximately Optimal Core Shapes for Tensor Decompositions. 11237-11254 - Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. 11255-11282 - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. 11283-11299 - Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan:
Contextual Reliability: When Different Features Matter in Different Contexts. 11300-11320 - Dibya Ghosh, Chethan Anand Bhateja, Sergey Levine:
Reinforcement Learning from Passive Data via Latent Intentions. 11321-11339 - Atiyo Ghosh, Antonio Andrea Gentile, Mario Dagrada, Chul Lee, Seong-Hyok Sean Kim, Hyukgeun Cha, Yunjun Choi, Dongho Kim, Jeong-Il Kye, Vincent Emanuel Elfving:
Harmonic Neural Networks. 11340-11359 - Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich:
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat. 11360-11397 - Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos:
Looped Transformers as Programmable Computers. 11398-11442 - Luca Giuliani, Eleonora Misino, Michele Lombardi:
Generalized Disparate Impact for Configurable Fairness Solutions in ML. 11443-11458 - Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. 11459-11492 - Manuel Glöckler, Michael Deistler, Jakob H. Macke:
Adversarial robustness of amortized Bayesian inference. 11493-11524 - Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak:
Efficient RL via Disentangled Environment and Agent Representations. 11525-11545 - Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman:
Aligning Language Models with Preferences through f-divergence Minimization. 11546-11583 - Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stéphan Clémençon:
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues. 11584-11597 - Weiyuan Gong, Scott Aaronson:
Learning Distributions over Quantum Measurement Outcomes. 11598-11613 - Eduard Gorbunov, Adrien B. Taylor, Samuel Horváth, Gauthier Gidel:
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity. 11614-11641 - Shirin Goshtasbpour, Victor Cohen, Fernando Pérez-Cruz:
Adaptive Annealed Importance Sampling with Constant Rate Progress. 11642-11658 - Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden:
Formalizing Preferences Over Runtime Distributions. 11659-11682 - Vincent Peter Grande, Michael T. Schaub:
Topological Point Cloud Clustering. 11683-11697 - Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié:
On Sampling with Approximate Transport Maps. 11698-11733 - J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick:
Hidden Symmetries of ReLU Networks. 11734-11760 - Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik:
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. 11761-11807 - Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M. Susskind, Christian Theobalt, Lingjie Liu, Ravi Ramamoorthi:
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion. 11808-11826 - Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu:
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. 11827-11846 - Aritra Guha, Nhat Ho, XuanLong Nguyen:
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances. 11847-11870 - Etash Kumar Guha, Eugène Ndiaye, Xiaoming Huo:
Conformalization of Sparse Generalized Linear Models. 11871-11887 - Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat:
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. 11888-11904 - Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, Yi Chang:
Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. 11905-11933 - Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang:
FeDXL: Provable Federated Learning for Deep X-Risk Optimization. 11934-11966 - Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang:
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. 11967-11997 - Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi:
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano. 11998-12011 - Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao:
Linkless Link Prediction via Relational Distillation. 12012-12033 - Yongxin Guo, Xiaoying Tang, Tao Lin:
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. 12034-12054 - Minghao Guo, Veronika Thost, Samuel W. Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik:
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction. 12055-12076 - Yuhe Guo, Zhewei Wei:
Graph Neural Networks with Learnable and Optimal Polynomial Bases. 12077-12097 - Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian J. McAuley:
LongCoder: A Long-Range Pre-trained Language Model for Code Completion. 12098-12107 - Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long:
Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms. 12108-12121 - Lan-Zhe Guo, Zhi Zhou, Yufeng Li, Zhi-Hua Zhou:
Identifying Useful Learnwares for Heterogeneous Label Spaces. 12122-12131 - Shivam Gupta, Jasper C. H. Lee, Eric Price:
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors. 12132-12164 - Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta J. Bedathur:
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets. 12165-12181 - Chirag Gupta, Aaditya Ramdas:
Online Platt Scaling with Calibeating. 12182-12204 - NareshKumar Gurulingan, Bahram Zonooz, Elahe Arani:
Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal. 12205-12223 - Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat:
Conditionally Strongly Log-Concave Generative Models. 12224-12251 - Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni:
DRew: Dynamically Rewired Message Passing with Delay. 12252-12267 - Marie Guyomard, Susana Barbosa, Lionel Fillatre:
Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network. 12268-12291 - Soroush H. Zargarbashi, Simone Antonelli, Aleksandar Bojchevski:
Conformal Prediction Sets for Graph Neural Networks. 12292-12318 - Seungwoong Ha, Hawoong Jeong:
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning. 12319-12338 - Daniel Haider, Martin Ehler, Péter Balázs:
Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction. 12339-12350 - Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta:
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. 12351-12367 - Boran Han:
Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition. 12368-12388 - Hyeongrok Han, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon:
On the Impact of Knowledge Distillation for Model Interpretability. 12389-12410 - Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi, Victor Lee, Jiliang Tang:
Alternately Optimized Graph Neural Networks. 12411-12429 - Yena Han, Tomaso A. Poggio, Brian Cheung:
System Identification of Neural Systems: If We Got It Right, Would We Know? 12430-12444 - Jonas Berg Hansen, Filippo Maria Bianchi:
Total Variation Graph Neural Networks. 12445-12468 - Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney:
Learning Physical Models that Can Respect Conservation Laws. 12469-12510 - Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang:
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. 12511-12526 - Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen:
Leveraging Demonstrations to Improve Online Learning: Quality Matters. 12527-12545 - Xiaoran Hao, Patrick Shafto:
Coupled Variational Autoencoder. 12546-12555 - Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu:
GNOT: A General Neural Operator Transformer for Operator Learning. 12556-12569 - Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. 12570-12586 - Marc Härkönen, Markus Lange-Hegermann, Bogdan Raita:
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. 12587-12615 - Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park:
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. 12616-12632 - Ali Hatamizadeh, Hongxu Yin, Greg Heinrich, Jan Kautz, Pavlo Molchanov:
Global Context Vision Transformers. 12633-12646 - Martin B. Haugh, Raghav Singal:
Counterfactual Analysis in Dynamic Latent State Models. 12647-12677 - Satoshi Hayakawa, Harald Oberhauser, Terry J. Lyons:
Sampling-based Nyström Approximation and Kernel Quadrature. 12678-12699 - Soufiane Hayou, Greg Yang:
Width and Depth Limits Commute in Residual Networks. 12700-12723 - Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, Xavier Bresson:
A Generalization of ViT/MLP-Mixer to Graphs. 12724-12745 - Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, Marinka Zitnik:
Domain Adaptation for Time Series Under Feature and Label Shifts. 12746-12774 - Dongxiao He, Jitao Zhao, Rui Guo, Zhiyong Feng, Di Jin, Yuxiao Huang, Zhen Wang, Weixiong Zhang:
Contrastive Learning Meets Homophily: Two Birds with One Stone. 12775-12789 - Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. 12790-12822 - S. Ashwin Hebbar, Viraj Vivek Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh, Pramod Viswanath:
CRISP: Curriculum based Sequential neural decoders for Polar code family. 12823-12845 - Jonathan Hehir, Daniel Ting, Graham Cormode:
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting. 12846-12865 - Florian Heinrichs, Mavin Heim, Corinna Weber:
Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification. 12866-12881 - Joey Hejna, Jensen Gao, Dorsa Sadigh:
Distance Weighted Supervised Learning for Offline Interaction Data. 12882-12906 - Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji:
Group Equivariant Fourier Neural Operators for Partial Differential Equations. 12907-12930 - Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low:
Training-Free Neural Active Learning with Initialization-Robustness Guarantees. 12931-12971 - Mikael Henaff, Minqi Jiang, Roberta Raileanu:
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs. 12972-12999 - Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J. Kim, Jin-Hwa Kim:
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding. 13000-13016 - Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz:
Generalized Teacher Forcing for Learning Chaotic Dynamics. 13017-13049 - Caglar Hizli, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietiläinen, Pekka Marttinen:
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences. 13050-13084 - Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. 13085-13117 - Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert:
AdaBoost is not an Optimal Weak to Strong Learner. 13118-13140 - Rasmus Kjær Høier, D. Staudt, Christopher Zach:
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons. 13141-13156 - Joey Hong, Branislav Kveton, Manzil Zaheer, Sumeet Katariya, Mohammad Ghavamzadeh:
Multi-Task Off-Policy Learning from Bandit Feedback. 13157-13173 - Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar:
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. 13174-13198 - Junyuan Hong, Yi Zeng, Shuyang Yu, Lingjuan Lyu, Ruoxi Jia, Jiayu Zhou:
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers. 13199-13212 - Emiel Hoogeboom, Jonathan Heek, Tim Salimans:
simple diffusion: End-to-end diffusion for high resolution images. 13213-13232 - Guy Horowitz, Nir Rosenfeld:
Causal Strategic Classification: A Tale of Two Shifts. 13233-13253 - Ramtin Hosseini, Li Zhang, Bhanu Garg, Pengtao Xie:
Fair and Accurate Decision Making through Group-Aware Learning. 13254-13269 - Sèdjro Salomon Hotegni, Sepideh Mahabadi, Ali Vakilian:
Approximation Algorithms for Fair Range Clustering. 13270-13284 - Elizabeth Mary Hou, Gregory David Castañón:
Decoding Layer Saliency in Language Transformers. 13285-13308 - Bairu Hou, Joe O'Connor, Jacob Andreas, Shiyu Chang, Yang Zhang:
PromptBoosting: Black-Box Text Classification with Ten Forward Passes. 13309-13324 - Boya Hou, Sina Sanjari, Nathan Dahlin, Subhonmesh Bose, Umesh Vaidya:
Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach. 13325-13352 - Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong:
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits. 13353-13409 - Ignacio Hounie, Luiz F. O. Chamon, Alejandro Ribeiro:
Automatic Data Augmentation via Invariance-Constrained Learning. 13410-13433 - Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum:
Thompson Sampling with Diffusion Generative Prior. 13434-13468 - Zhengmian Hu, Heng Huang:
Tighter Analysis for ProxSkip. 13469-13496 - Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang:
Omnipredictors for Constrained Optimization. 13497-13527 - Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie E. Everett, Alexandros Graikos, Yoshua Bengio:
GFlowNet-EM for Learning Compositional Latent Variable Models. 13528-13549 - Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang:
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. 13550-13583 - Hengyuan Hu, Dorsa Sadigh:
Language Instructed Reinforcement Learning for Human-AI Coordination. 13584-13598 - Yuan-Ting Hu, Alexander G. Schwing, Raymond A. Yeh:
Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction. 13599-13609 - Zixuan Hu, Li Shen, Zhenyi Wang, Baoyuan Wu, Chun Yuan, Dacheng Tao:
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning. 13610-13627 - Yingdong Hu, Renhao Wang, Li Erran Li, Yang Gao:
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal. 13628-13651 - Zhengmian Hu, Xidong Wu, Heng Huang:
Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization. 13652-13678 - Yuzheng Hu, Fan Wu, Hongyang Zhang, Han Zhao:
Understanding the Impact of Adversarial Robustness on Accuracy Disparity. 13679-13709 - Audrey Huang, Jinglin Chen, Nan Jiang:
Reinforcement Learning in Low-rank MDPs with Density Features. 13710-13752 - Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, Jingren Zhou:
Composer: Creative and Controllable Image Synthesis with Composable Conditions. 13753-13773 - Zizheng Huang, Haoxing Chen, Ziqi Wen, Chao Zhang, Huaxiong Li, Bo Wang, Chunlin Chen:
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas. 13774-13790 - Tianyi Huang, Shenghui Cheng, Stan Z. Li, Zhengjun Zhang:
High-dimensional Clustering onto Hamiltonian Cycle. 13791-13813 - Jiatai Huang, Yan Dai, Longbo Huang:
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning. 13814-13844 - Junyu Huang, Qilong Feng, Ziyun Huang, Jinhui Xu, Jianxin Wang:
Fast Algorithms for Distributed k-Clustering with Outliers. 13845-13868 - Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. 13869-13890 - Lingxiao Huang, Ruiyuan Huang, Zengfeng Huang, Xuan Wu:
On Coresets for Clustering in Small Dimensional Euclidean spaces. 13891-13915 - Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, Zhou Zhao:
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models. 13916-13932 - Lingxiao Huang, Shaofeng H.-C. Jiang, Jianing Lou:
The Power of Uniform Sampling for k-Median. 13933-13956 - Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li, Chuang Gan, Hao Su:
Reparameterized Policy Learning for Multimodal Trajectory Optimization. 13957-13975 - Yufan Huang, C. Seshadhri, David F. Gleich:
Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming. 13976-13992 - Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu:
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition. 13993-14006 - Jialei Huang, Zhao-Heng Yin, Yingdong Hu, Yang Gao:
Policy Contrastive Imitation Learning. 14007-14022 - Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu:
Are Large Kernels Better Teachers than Transformers for ConvNets? 14023-14038 - Minhui Huang, Dewei Zhang, Kaiyi Ji:
Achieving Linear Speedup in Non-IID Federated Bilevel Learning. 14039-14059 - Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hejazinia, Jing Yang:
Federated Linear Contextual Bandits with User-level Differential Privacy. 14060-14095 - Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. 14096-14113 - Like Hui, Mikhail Belkin, Stephen Wright:
Cut your Losses with Squentropy. 14114-14131 - Iris A. M. Huijben, Arthur Andreas Nijdam, Sebastiaan Overeem, Merel M. van Gilst, Ruud van Sloun:
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series. 14132-14152 - Pierre Humbert, Batiste Le Bars, Aurélien Bellet, Sylvain Arlot:
One-Shot Federated Conformal Prediction. 14153-14177 - Aamal Abbas Hussain, Francesco Belardinelli, Dario Paccagnan:
The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics. 14178-14202 - Taehyun Hwang, Kyuwook Chai, Min-hwan Oh:
Combinatorial Neural Bandits. 14203-14236 - Geonho Hwang, Jaewoong Choi, Hyunsoo Cho, Myungjoo Kang:
MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action. 14237-14248 - HyeongJoo Hwang, Seokin Seo, Youngsoo Jang, Sungyoon Kim, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim:
Information-Theoretic State Space Model for Multi-View Reinforcement Learning. 14249-14282 - Shahana Ibrahim, Xiao Fu, Rebecca A. Hutchinson, Eugene Seo:
Under-Counted Tensor Completion with Neural Incorporation of Attributes. 14283-14315 - Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. 14316-14332 - Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf:
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. 14333-14352 - Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. 14353-14375 - Brian Irwin, Eldad Haber, Raviv Gal, Avi Ziv:
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods. 14376-14389 - Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Rajiv Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford:
Principled Offline RL in the Presence of Rich Exogenous Information. 14390-14421 - Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary, David Picard:
Unveiling the Latent Space Geometry of Push-Forward Generative Models. 14422-14444 - Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. 14445-14464 - Maor Ivgi, Oliver Hinder, Yair Carmon:
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. 14465-14499 - Gaurav Iyer, Boris Hanin, David Rolnick:
Maximal Initial Learning Rates in Deep ReLU Networks. 14500-14530 - Zachary Izzo, Ruishan Liu, James Zou:
Data-Driven Subgroup Identification for Linear Regression. 14531-14552 - Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar:
Efficient Training of Language Models using Few-Shot Learning. 14553-14568 - Allan Jabri, David J. Fleet, Ting Chen:
Scalable Adaptive Computation for Iterative Generation. 14569-14589 - Andrew Jacobsen, Ashok Cutkosky:
Unconstrained Online Learning with Unbounded Losses. 14590-14630 - Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio:
Multi-Objective GFlowNets. 14631-14653 - Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith:
The Price of Differential Privacy under Continual Observation. 14654-14678 - Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang:
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication. 14679-14690 - Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang:
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models. 14691-14701 - Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, Minjoon Seo:
Exploring the Benefits of Training Expert Language Models over Instruction Tuning. 14702-14729 - Jinhyeok Jang, Woo-han Yun, Won Hwa Kim, Youngwoo Yoon, Jaehong Kim, Jaeyeon Lee, ByungOk Han:
Learning to Boost Training by Periodic Nowcasting Near Future Weights. 14730-14757 - Faris Janjos, Lars Rosenbaum, Maxim Dolgov, J. Marius Zoellner:
Unscented Autoencoder. 14758-14779 - Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko:
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. 14780-14816 - Kishaan Jeeveswaran, Prashant Shivaram Bhat, Bahram Zonooz, Elahe Arani:
BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning. 14817-14835 - Hyeonsu Jeong, Hye Won Chung:
Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing. 14836-14868 - Feng Ji, See Hian Lee, Hanyang Meng, Kai Zhao, Jielong Yang, Wee Peng Tay:
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks. 14869-14885 - Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Yuke Xiang, Yufeng Li:
Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions. 14886-14901 - Su Jia, Nishant Oli, Ian Anderson, Paul Duff, Andrew A. Li, R. Ravi:
Short-lived High-volume Bandits. 14902-14929 - Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier:
Smooth Non-stationary Bandits. 14930-14944 - Haiyan Jiang, Srinivas Anumasa, Giulia De Masi, Huan Xiong, Bin Gu:
A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates. 14945-14974 - Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan:
VIMA: Robot Manipulation with Multimodal Prompts. 14975-15022 - Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson:
Estimating Causal Effects using a Multi-task Deep Ensemble. 15023-15040 - Bowen Jiang, Bo Jiang, Jian Li, Tao Lin, Xinbing Wang, Chenghu Zhou:
Online Restless Bandits with Unobserved States. 15041-15066 - Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Detecting Out-of-distribution Data through In-distribution Class Prior. 15067-15088 - Yulun Jiang, Chen Liu, Zhichao Huang, Mathieu Salzmann, Sabine Süsstrunk:
Towards Stable and Efficient Adversarial Training against l1 Bounded Adversarial Attacks. 15089-15104 - Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang:
Learning Unnormalized Statistical Models via Compositional Optimization. 15105-15124 - Ziwei Jiang, Lai Wei, Murat Kocaoglu:
Approximate Causal Effect Identification under Weak Confounding. 15125-15143 - Guangyuan Jiang, Manjie Xu, Shiji Xin, Wei Liang, Yujia Peng, Chi Zhang, Yixin Zhu:
MEWL: Few-shot multimodal word learning with referential uncertainty. 15144-15169 - Chenbo Jiang, Jie Yang, Shwai He, Yu-Kun Lai, Lin Gao:
NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes. 15170-15185 - Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. 15186-15199 - Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee:
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. 15200-15238 - Tianyuan Jin, Xianglin Yang, Xiaokui Xiao, Pan Xu:
Thompson Sampling with Less Exploration is Fast and Optimal. 15239-15261 - Daniel D. Johnson, Daniel Tarlow, Christian Walder:
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. 15262-15306 - Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt:
Automatically Auditing Large Language Models via Discrete Optimization. 15307-15329 - Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio:
On the Expressive Power of Geometric Graph Neural Networks. 15330-15355 - Siddharth Joshi, Baharan Mirzasoleiman:
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least. 15356-15370 - Kishor Jothimurugan, Steve Hsu, Osbert Bastani, Rajeev Alur:
Robust Subtask Learning for Compositional Generalization. 15371-15387 - Amir Joudaki, Hadi Daneshmand, Francis R. Bach:
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization. 15388-15400 - Nikola Jovanovic, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
FARE: Provably Fair Representation Learning with Practical Certificates. 15401-15420 - Seungjin Jung, Seungmo Seo, Yonghyun Jeong, Jongwon Choi:
Scaling of Class-wise Training Losses for Post-hoc Calibration. 15421-15434 - Yeonsung Jung, Hajin Shim, June Yong Yang, Eunho Yang:
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation. 15435-15450 - Yonghan Jung, Jin Tian, Elias Bareinboim:
Estimating Joint Treatment Effects by Combining Multiple Experiments. 15451-15527 - Mateusz Maria Jurewicz, Graham W. Taylor, Leon Derczynski:
The Catalog Problem: Clustering and Ordering Variable-Sized Sets. 15528-15545 - Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh:
Equivariance with Learned Canonicalization Functions. 15546-15566 - Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami:
Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies. 15567-15585 - Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas:
Statistical Indistinguishability of Learning Algorithms. 15586-15622 - Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Identifying Interpretable Subspaces in Image Representations. 15623-15638 - David Kaltenpoth, Jilles Vreeken:
Nonlinear Causal Discovery with Latent Confounders. 15639-15654 - Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin:
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. 15655-15668 - Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Kentaro Ohno, Yasutoshi Ida:
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. 15669-15695 - Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel:
Large Language Models Struggle to Learn Long-Tail Knowledge. 15696-15707 - Nikhil Kandpal, Brian Lester, Mohammed Muqeeth, Anisha Mascarenhas, Monty Evans, Vishal Baskaran, Tenghao Huang, Haokun Liu, Colin Raffel:
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. 15708-15719 - Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Alicia Trevino Kala, David Hyde, Joseph Teran:
A Deep Conjugate Direction Method for Iteratively Solving Linear Systems. 15720-15736 - Juwon Kang, Nayeong Kim, Donghyeon Kwon, Jungseul Ok, Suha Kwak:
Leveraging Proxy of Training Data for Test-Time Adaptation. 15737-15752 - Yachen Kang, Diyuan Shi, Jinxin Liu, Li He, Donglin Wang:
Beyond Reward: Offline Preference-guided Policy Optimization. 15753-15768 - Siteng Kang, Zhan Shi, Xinhua Zhang:
Poisoning Generative Replay in Continual Learning to Promote Forgetting. 15769-15785 - Qiyu Kang, Kai Zhao, Yang Song, Sijie Wang, Wee Peng Tay:
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks. 15786-15808 - Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa:
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. 15809-15827 - Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra:
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. 15828-15860 - Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. 15861-15883 - Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, Hsien-Hsin S. Lee:
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. 15884-15899 - Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann:
General Sequential Episodic Memory Model. 15900-15910 - Takayuki Katsuki, Takayuki Osogami:
Regression with Sensor Data Containing Incomplete Observations. 15911-15927 - Ilya Kaufman, Omri Azencot:
Data Representations' Study of Latent Image Manifolds. 15928-15945 - Prannay Kaul, Weidi Xie, Andrew Zisserman:
Multi-Modal Classifiers for Open-Vocabulary Object Detection. 15946-15969 - Chinmaya Kausik, Kevin Tan, Ambuj Tewari:
Learning Mixtures of Markov Chains and MDPs. 15970-16017 - Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber:
Curious Replay for Model-based Adaptation. 16018-16048 - Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang:
How Does Information Bottleneck Help Deep Learning? 16049-16096 - Yuta Kawakami, Manabu Kuroki, Jin Tian:
Instrumental Variable Estimation of Average Partial Causal Effects. 16097-16130 - Zeki Kazan, Kaiyan Shi, Adam Groce, Andrew P. Bray:
The Test of Tests: A Framework for Differentially Private Hypothesis Testing. 16131-16151 - Chuyang Ke, Jean Honorio:
Exact Inference in High-order Structured Prediction. 16152-16167 - T. Anderson Keller, Max Welling:
Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks. 16168-16189 - Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf:
Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. 16190-16215 - Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry:
Rethinking Backdoor Attacks. 16216-16236 - Adam Khakhar, Stephen Mell, Osbert Bastani:
PAC Prediction Sets for Large Language Models of Code. 16237-16249 - Mohammad Khalafi, Digvijay Boob:
Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems. 16250-16270 - Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan:
Loss Balancing for Fair Supervised Learning. 16271-16290 - Prashant Khanduri, Ioannis C. Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu, Jiawei Zhang, Mingyi Hong:
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach. 16291-16325