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ORCID39th ICML 2022: Baltimore, MD, USA
- Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, Sivan Sabato:
International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research 162, PMLR 2022 - Alhabib Abbas, Yiannis Andreopoulos:
PAC-Bayesian Bounds on Rate-Efficient Classifiers. 1-9 - Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen:
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning. 10-32 - Emmanuel Abbe, Elisabetta Cornacchia, Jan Hazla, Christopher Marquis:
An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn. 33-52 - Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang:
Active Sampling for Min-Max Fairness. 53-65 - Abubakar Abid, Mert Yüksekgönül, James Zou:
Meaningfully debugging model mistakes using conceptual counterfactual explanations. 66-88 - Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan:
Batched Dueling Bandits. 89-110 - Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu:
Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. 111-135 - Atish Agarwala, Samuel S. Schoenholz:
Deep equilibrium networks are sensitive to initialization statistics. 136-160 - Henrique Aguiar, Mauro D. Santos, Peter J. Watkinson, Tingting Zhu:
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series. 161-179 - Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low:
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. 180-196 - Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
:
Individual Preference Stability for Clustering. 197-246 - Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra:
Understanding the unstable convergence of gradient descent. 247-257 - Sina Akbari, Jalal Etesami, Negar Kiyavash:
Minimum Cost Intervention Design for Causal Effect Identification. 258-289 - Ahmed M. Alaa, Boris van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar:
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. 290-306 - Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher:
A Natural Actor-Critic Framework for Zero-Sum Markov Games. 307-366 - Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt:
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations. 367-393 - Lucas Nunes Alegre, Ana L. C. Bazzan, Bruno C. da Silva:
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer. 394-413 - Antonios Alexos, Alex J. Boyd, Stephan Mandt:
Structured Stochastic Gradient MCMC. 414-434 - Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf:
XAI for Transformers: Better Explanations through Conservative Propagation. 435-451 - Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins:
RUMs from Head-to-Head Contests. 452-467 - Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig:
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval. 468-485 - Verónica Álvarez, Santiago Mazuelas, José Antonio Lozano:
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees. 486-499 - Sebastian E. Ament, Carla P. Gomes:
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation. 500-516 - Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta:
Public Data-Assisted Mirror Descent for Private Model Training. 517-535 - Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm:
On Last-Iterate Convergence Beyond Zero-Sum Games. 536-581 - Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi:
Online Algorithms with Multiple Predictions. 582-598 - Alexandr Andoni, Daniel Beaglehole:
Learning to Hash Robustly, Guaranteed. 599-618 - Bruno Andreis, Seanie Lee, Tuan A. Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang:
Set Based Stochastic Subsampling. 619-638 - Maksym Andriushchenko, Nicolas Flammarion:
Towards Understanding Sharpness-Aware Minimization. 639-668 - Haris Angelidakis, Adam Kurpisz, Leon Sering, Rico Zenklusen:
Fair and Fast k-Center Clustering for Data Summarization. 669-702 - Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum:
Interactive Correlation Clustering with Existential Cluster Constraints. 703-716 - Anastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano:
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. 717-730 - Kimon Antonakopoulos, Panayotis Mertikopoulos, Georgios Piliouras, Xiao Wang:
AdaGrad Avoids Saddle Points. 731-771 - Kimon Antonakopoulos, Dong Quan Vu, Volkan Cevher, Kfir Y. Levy, Panayotis Mertikopoulos:
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees. 772-795 - Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. 796-821 - Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang:
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning. 822-843 - David Arbour, Drew Dimmery, Tung Mai, Anup B. Rao:
Online Balanced Experimental Design. 844-864 - Randy Ardywibowo, Zepeng Huo, Zhangyang Wang, Bobak J. Mortazavi, Shuai Huang, Xiaoning Qian:
VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty. 865-877 - Kaito Ariu, Kenshi Abe, Alexandre Proutière:
Thresholded Lasso Bandit. 878-928 - Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic, Soummya Kar:
Gradient Based Clustering. 929-947 - Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi:
Understanding Gradient Descent on the Edge of Stability in Deep Learning. 948-1024 - Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi:
Private optimization in the interpolation regime: faster rates and hardness results. 1025-1045 - Hilal Asi, Vitaly Feldman, Kunal Talwar:
Optimal Algorithms for Mean Estimation under Local Differential Privacy. 1046-1056 - Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai:
Asymptotically-Optimal Gaussian Bandits with Side Observations. 1057-1077 - Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias:
Congested Bandits: Optimal Routing via Short-term Resets. 1078-1100 - Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath:
Do More Negative Samples Necessarily Hurt In Contrastive Learning? 1101-1116 - Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
H-Consistency Bounds for Surrogate Loss Minimizers. 1117-1174 - Kyriakos Axiotis, Maxim Sviridenko:
Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime. 1175-1197 - Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen Marcus McAleer, Vlad Firoiu, Lei M. Zhang, Doina Precup, Shibl Mourad:
Proving Theorems using Incremental Learning and Hindsight Experience Replay. 1198-1210 - Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Near-optimal rate of consistency for linear models with missing values. 1211-1243 - Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
How Tempering Fixes Data Augmentation in Bayesian Neural Networks. 1244-1260 - Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas:
ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD. 1261-1276 - HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon:
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model. 1277-1297 - Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli:
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language. 1298-1312 - Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman:
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation. 1313-1326 - Lu Bai, Lixin Cui, Edwin R. Hancock:
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs. 1327-1336 - Yu Bai, Chi Jin, Song Mei, Tiancheng Yu:
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. 1337-1382 - Junwen Bai, Shufeng Kong, Carla P. Gomes:
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification. 1383-1398 - He Bai, Renjie Zheng, Junkun Chen, Mingbo Ma, Xintong Li, Liang Huang:
A3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing. 1399-1411 - Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou:
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics. 1412-1449 - Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. 1450-1465 - Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat:
Data Scaling Laws in NMT: The Effect of Noise and Architecture. 1466-1482 - Yujia Bao, Shiyu Chang, Regina Barzilay:
Learning Stable Classifiers by Transferring Unstable Features. 1483-1507 - Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu:
Fast Composite Optimization and Statistical Recovery in Federated Learning. 1508-1536 - Zhipeng Bao, Martial Hebert, Yu-Xiong Wang:
Generative Modeling for Multi-task Visual Learning. 1537-1554 - Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang:
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models. 1555-1584 - Han Bao, Yoshihiro Nagano, Kento Nozawa:
On the Surrogate Gap between Contrastive and Supervised Losses. 1585-1606 - Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev:
Representation Topology Divergence: A Method for Comparing Neural Network Representations. 1607-1626 - Adarsh Barik, Jean Honorio:
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation. 1627-1646 - Burak Bartan, Mert Pilanci:
Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time. 1647-1663 - Lucas Baudin, Rida Laraki:
Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games. 1664-1690 - Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani:
Information Discrepancy in Strategic Learning. 1691-1715 - Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M. Sadler, Pratap Tokekar, Alec Koppel:
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces. 1716-1731 - Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani:
Imitation Learning by Estimating Expertise of Demonstrators. 1732-1748 - Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximilian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman:
Matching Normalizing Flows and Probability Paths on Manifolds. 1749-1763 - Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. 1764-1786 - Raphael Bensadoun, Shir Gur, Nitsan Blau, Lior Wolf:
Neural Inverse Kinematic. 1787-1797 - Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson:
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. 1798-1816 - Frederik Benzing:
Gradient Descent on Neurons and its Link to Approximate Second-order Optimization. 1817-1853 - Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò:
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints. 1854-1873 - Peter J. Bevan, Amir Atapour-Abarghouei:
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification. 1874-1892 - Ayush Bharti, Louis Filstroff, Samuel Kaski:
Approximate Bayesian Computation with Domain Expert in the Loop. 1893-1905 - Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky:
Minimax M-estimation under Adversarial Contamination. 1906-1924 - Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky:
Nearly Optimal Catoni's M-estimator for Infinite Variance. 1925-1944 - Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. 1945-1962 - Felix Biggs, Benjamin Guedj:
Non-Vacuous Generalisation Bounds for Shallow Neural Networks. 1963-1981 - Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Structure-preserving GANs. 1982-2020 - Niloy Biswas, Lester Mackey, Xiao-Li Meng:
Scalable Spike-and-Slab. 2021-2040 - Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein:
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. 2041-2074 - Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan:
A query-optimal algorithm for finding counterfactuals. 2075-2090 - Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
Popular decision tree algorithms are provably noise tolerant. 2091-2106 - Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli:
Optimizing Sequential Experimental Design with Deep Reinforcement Learning. 2107-2128 - Bojun Huang:
Lagrangian Method for Q-Function Learning (with Applications to Machine Translation). 2129-2159 - Heejong Bong, Alessandro Rinaldo:
Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model. 2160-2177 - Akhilan Boopathy, Ila Fiete:
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective. 2178-2205 - Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre:
Improving Language Models by Retrieving from Trillions of Tokens. 2206-2240 - Johannes Brandstetter, Max Welling, Daniel E. Worrall:
Lie Point Symmetry Data Augmentation for Neural PDE Solvers. 2241-2256 - Guillaume Braun, Hemant Tyagi, Christophe Biernacki:
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees. 2257-2291 - Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz:
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. 2292-2320 - Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc:
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. 2321-2335 - Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz:
Efficient Learning of CNNs using Patch Based Features. 2336-2356 - Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum, Dominik Janzing:
Causal structure-based root cause analysis of outliers. 2357-2369 - Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo Maria Ponti, Ivan Vulic:
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages. 2370-2392 - Thomas Kleine Büning, Anne-Marie George, Christos Dimitrakakis:
Interactive Inverse Reinforcement Learning for Cooperative Games. 2393-2413 - Rebekka Burkholz:
Convolutional and Residual Networks Provably Contain Lottery Tickets. 2414-2433 - Haoyuan Cai, Tengyu Ma, Simon S. Du:
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path. 2434-2456 - Chen Cai, Yusu Wang:
Convergence of Invariant Graph Networks. 2457-2484 - Qi Cai, Zhuoran Yang, Zhaoran Wang:
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency. 2485-2522 - Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. 2523-2541 - Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause:
Adaptive Gaussian Process Change Point Detection. 2542-2571 - Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi:
Measuring dissimilarity with diffeomorphism invariance. 2572-2596 - Yiting Cao, Chao Lan:
A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling. 2597-2608 - Alexandre Capone, Armin Lederer, Sandra Hirche:
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications. 2609-2624 - Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci:
Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning. 2625-2637 - Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti:
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving. 2638-2657 - Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. 2658-2685 - Micah D. Carroll, Anca D. Dragan, Stuart Russell, Dylan Hadfield-Menell:
Estimating and Penalizing Induced Preference Shifts in Recommender Systems. 2686-2708 - Edresson Casanova, Julian Weber, Christopher Dane Shulby, Arnaldo Cândido Júnior, Eren Gölge, Moacir A. Ponti:
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone. 2709-2720 - Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli:
The Infinite Contextual Graph Markov Model. 2721-2737 - Timothy J. Castiglia, Anirban Das, Shiqiang Wang, Stacy Patterson:
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data. 2738-2766 - Matteo Castiglioni, Andrea Celli, Christian Kroer:
Online Learning with Knapsacks: the Best of Both Worlds. 2767-2783 - Edoardo Cetin, Philip J. Ball, Stephen J. Roberts, Oya Çeliktutan:
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels. 2784-2810 - Karan N. Chadha, Gary Cheng, John C. Duchi:
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization. 2811-2827 - Jongseong Chae, Seungyul Han, Whiyoung Jung, Myungsik Cho, Sungho Choi, Youngchul Sung:
Robust Imitation Learning against Variations in Environment Dynamics. 2828-2852 - Junyi Chai, Xiaoqian Wang:
Fairness with Adaptive Weights. 2853-2866 - Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, Hamed Firooz:
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction. 2867-2889 - Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Yunqing Zhao, Ngai-Man Cheung:
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing? 2890-2916 - Jen-Hao Rick Chang, Ashish Shrivastava, Hema Koppula, Xiaoshuai Zhang, Oncel Tuzel:
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models. 2917-2937 - Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun:
Learning Bellman Complete Representations for Offline Policy Evaluation. 2938-2971 - Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi:
Sample Efficient Learning of Predictors that Complement Humans. 2972-3005 - Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi:
Nyström Kernel Mean Embeddings. 3006-3024 - Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang:
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets. 3025-3039 - Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, Shiliang Pu:
Learning Domain Adaptive Object Detection with Probabilistic Teacher. 3040-3055 - Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh:
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. 3056-3089 - Mayee F. Chen, Daniel Y. Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Ré:
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning. 3090-3122 - Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama:
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk. 3123-3148 - Yuanzhou Chen, Jiafan He, Quanquan Gu:
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. 3149-3183 - Justin Y. Chen, Piotr Indyk, Tal Wagner:
Streaming Algorithms for Support-Aware Histograms. 3184-3203 - Liyu Chen, Rahul Jain, Haipeng Luo:
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP. 3204-3245 - Liyu Chen, Rahul Jain, Haipeng Luo:
Learning Infinite-horizon Average-reward Markov Decision Process with Constraints. 3246-3270 - Yifang Chen, Kevin G. Jamieson, Simon S. Du:
Active Multi-Task Representation Learning. 3271-3298 - Ruoxin Chen, Zenan Li, Jie Li, Junchi Yan, Chentao Wu:
On Collective Robustness of Bagging Against Data Poisoning. 3299-3319 - Cheng Chen, Yi Li, Yiming Sun:
Online Active Regression. 3320-3335 - Junjie Chen, Minming Li, Haifeng Xu:
Selling Data To a Machine Learner: Pricing via Costly Signaling. 3336-3359 - Jintai Chen, Kuanlun Liao, Kun Wei, Haochao Ying, Danny Z. Chen, Jian Wu:
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases. 3360-3370 - Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang:
Weisfeiler-Lehman Meets Gromov-Wasserstein. 3371-3416 - Kun Chen, Dachao Lin, Zhihua Zhang:
On Non-local Convergence Analysis of Deep Linear Networks. 3417-3443 - Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Li, Jianyu Chen:
Flow-based Recurrent Belief State Learning for POMDPs. 3444-3468 - Dexiong Chen, Leslie O'Bray, Karsten M. Borgwardt:
Structure-Aware Transformer for Graph Representation Learning. 3469-3489 - Wei-Ning Chen, Ayfer Özgür, Peter Kairouz:
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. 3490-3506 - Yanxi Chen, H. Vincent Poor:
Learning Mixtures of Linear Dynamical Systems. 3507-3557 - Xiaohong Chen, Zhengling Qi:
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation. 3558-3582 - Justin Y. Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang:
Faster Fundamental Graph Algorithms via Learned Predictions. 3583-3602 - Xin Chen, Yujie Tang, Na Li:
Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters. 3603-3620 - Wenchao Chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan, Mingyuan Zhou:
Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection. 3621-3633 - Hong Chen, Xin Wang, Chaoyu Guan, Yue Liu, Wenwu Zhu:
Auxiliary Learning with Joint Task and Data Scheduling. 3634-3647 - Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Optimization-Induced Graph Implicit Nonlinear Diffusion. 3648-3661 - Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long, Yueting Zhuang:
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile. 3662-3678 - Siyu Chen, Donglin Yang, Jiayang Li, Senmiao Wang, Zhuoran Yang, Zhaoran Wang:
Adaptive Model Design for Markov Decision Process. 3679-3700 - Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, Yonghong Tian:
State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks. 3701-3715 - Lingjiao Chen, Matei Zaharia, James Zou:
Efficient Online ML API Selection for Multi-Label Classification Tasks. 3716-3746 - Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang:
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training. 3747-3759 - Tianlong Chen, Huan Zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang:
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness. 3760-3772 - Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang:
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. 3773-3793 - Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou:
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. 3794-3834 - Jiangnan Cheng, Ao Tang, Sandeep Chinchali:
Task-aware Privacy Preservation for Multi-dimensional Data. 3835-3851 - Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. 3852-3878 - Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, Honghao Lin, David P. Woodruff:
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. 3879-3900 - Victor Chernozhukov, Whitney Newey, Victor Quintas-Martinez, Vasilis Syrgkanis:
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests. 3901-3914 - Chung-Cheng Chiu, James Qin, Yu Zhang, Jiahui Yu, Yonghui Wu:
Self-supervised learning with random-projection quantizer for speech recognition. 3915-3924 - Wei-Ting Chiu, Pei Wang, Patrick Shafto:
Discrete Probabilistic Inverse Optimal Transport. 3925-3946 - Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde:
Selective Network Linearization for Efficient Private Inference. 3947-3961 - Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, Thomas Weingarten:
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. 3962-3983 - Sayak Ray Chowdhury, Xingyu Zhou:
Shuffle Private Linear Contextual Bandits. 3984-4009 - Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang, Shanghang Zhang, Hong Mei:
DNA: Domain Generalization with Diversified Neural Averaging. 4010-4034 - Wenda Chu, Linyi Li, Bo Li:
TPC: Transformation-Specific Smoothing for Point Cloud Models. 4035-4056 - Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J. Johnson, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Marc'Aurelio Ranzato, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan:
Unified Scaling Laws for Routed Language Models. 4057-4086 - Oliver Cobb, Arnaud Van Looveren:
Context-Aware Drift Detection. 4087-4111 - Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer:
On the Robustness of CountSketch to Adaptive Inputs. 4112-4140 - Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines:
Diffusion bridges vector quantized variational autoencoders. 4141-4156 - Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis:
Online and Consistent Correlation Clustering. 4157-4179 - Vincent Cohen-Addad, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel k-Means Clustering for Perturbation Resilient Instances. 4180-4201 - Benjamin Coleman, Benito Geordie, Li Chou, Ryan A. Leo Elworth, Todd J. Treangen, Anshumali Shrivastava:
One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams. 4202-4218 - Mark Collier, Rodolphe Jenatton, Effrosyni Kokiopoulou, Jesse Berent:
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. 4219-4237 - Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai:
MAML and ANIL Provably Learn Representations. 4238-4310 - Spencer Compton, Kristjan H. Greenewald, Dmitriy A. Katz, Murat Kocaoglu:
Entropic Causal Inference: Graph Identifiability. 4311-4343 - Jean-Rémy Conti, Nathan Noiry, Stéphan Clémençon, Vincent Despiegel, Stéphane Gentric:
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model. 4344-4369 - Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Counterfactual Transportability: A Formal Approach. 4370-4390 - Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. 4391-4420 - Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil:
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. 4421-4435 - Francesco Croce, Matthias Hein:
Adversarial Robustness against Multiple and Single lp-Threat Models via Quick Fine-Tuning of Robust Classifiers. 4436-4454 - Xavier Suau Cuadros, Luca Zappella, Nicholas Apostoloff:
Self-conditioning Pre-Trained Language Models. 4455-4473 - Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette:
Only tails matter: Average-Case Universality and Robustness in the Convex Regime. 4474-4491 - Edmond Cunningham, Adam D. Cobb, Susmit Jha:
Principal Component Flows. 4492-4519 - Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, François Charton:
Deep symbolic regression for recurrence prediction. 4520-4536 - Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin:
Continuous Control with Action Quantization from Demonstrations. 4537-4557 - Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y. Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu:
Dialog Inpainting: Turning Documents into Dialogs. 4558-4586 - Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, Dacheng Tao:
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. 4587-4604 - Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai:
Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization. 4605-4617 - Angelo Damiani, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli:
Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning. 4618-4629 - Soham Dan, Osbert Bastani, Dan Roth:
Understanding Robust Generalization in Learning Regular Languages. 4630-4643 - Tal Daniel, Aviv Tamar:
Unsupervised Image Representation Learning with Deep Latent Particles. 4644-4665 - Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. 4666-4689 - Tri Dao, Beidi Chen, Nimit Sharad Sohoni, Arjun D. Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Ré:
Monarch: Expressive Structured Matrices for Efficient and Accurate Training. 4690-4721 - Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis:
Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems. 4722-4753 - Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel:
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing. 4754-4776 - Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew McCallum:
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs. 4777-4793 - Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz:
Framework for Evaluating Faithfulness of Local Explanations. 4794-4815 - Ishita Dasgupta, Erin Grant, Tom Griffiths:
Distinguishing rule and exemplar-based generalization in learning systems. 4816-4830 - Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy:
Robust Multi-Objective Bayesian Optimization Under Input Noise. 4831-4866 - Ben J. Day, Ramón Viñas Torné, Nikola Simidjievski, Pietro Lió:
Attentional Meta-learners for Few-shot Polythetic Classification. 4867-4889 - Hassan Dbouk, Naresh R. Shanbhag:
Adversarial Vulnerability of Randomized Ensembles. 4890-4917 - Giuseppe Bruno De Luca, Eva Silverstein:
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization. 4918-4936 - Giorgia Dellaferrera, Gabriel Kreiman:
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. 4937-4955 - Fei Deng, Ingook Jang, Sungjin Ahn:
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations. 4956-4975 - Zhijie Deng, Jiaxin Shi, Jun Zhu:
NeuralEF: Deconstructing Kernels by Deep Neural Networks. 4976-4992 - Xiang Deng, Zhongfei Zhang:
Deep Causal Metric Learning. 4993-5006 - Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas:
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming. 5007-5038 - Shirli Di-Castro Shashua, Shie Mannor, Dotan Di Castro:
Analysis of Stochastic Processes through Replay Buffers. 5039-5060 - Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. 5061-5117 - Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. 5118-5141 - Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou:
Variational Feature Pyramid Networks. 5142-5152 - Tianjiao Ding, Derek Lim, René Vidal, Benjamin D. Haeffele:
Understanding Doubly Stochastic Clustering. 5153-5165 - Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Mihailo R. Jovanovic:
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence. 5166-5220 - Andrea Dittadi, Samuele S. Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello:
Generalization and Robustness Implications in Object-Centric Learning. 5221-5285 - Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong:
Fair Generalized Linear Models with a Convex Penalty. 5286-5308 - Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe:
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. 5309-5323 - Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Bernhard Schölkopf:
On the Adversarial Robustness of Causal Algorithmic Recourse. 5324-5342 - Runpei Dong, Zhanhong Tan, Mengdi Wu, Linfeng Zhang, Kaisheng Ma:
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks. 5343-5359 - Zehao Dong, Muhan Zhang, Fuhai Li, Yixin Chen:
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs. 5360-5377 - Tian Dong, Bo Zhao, Lingjuan Lyu:
Privacy for Free: How does Dataset Condensation Help Privacy? 5378-5396 - Konstantin Donhauser, Nicolò Ruggeri, Stefan Stojanovic, Fanny Yang:
Fast rates for noisy interpolation require rethinking the effect of inductive bias. 5397-5428 - Ron Dorfman, Kfir Yehuda Levy:
Adapting to Mixing Time in Stochastic Optimization with Markovian Data. 5429-5446 - Alexandre Drouin, Étienne Marcotte, Nicolas Chapados:
TACTiS: Transformer-Attentional Copulas for Time Series. 5447-5493 - Yihan Du, Wei Chen:
Branching Reinforcement Learning. 5494-5530 - Yuqing Du, Daniel Ho, Alex Alemi, Eric Jang, Mohi Khansari:
Bayesian Imitation Learning for End-to-End Mobile Manipulation. 5531-5546 - Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P. Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen S. Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui:
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. 5547-5569 - Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch:
Learning Iterative Reasoning through Energy Minimization. 5570-5582 - Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu:
SE(3) Equivariant Graph Neural Networks with Complete Local Frames. 5583-5608 - Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng:
A Context-Integrated Transformer-Based Neural Network for Auction Design. 5609-5626 - Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison:
Augment with Care: Contrastive Learning for Combinatorial Problems. 5627-5642 - Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu:
Parametric Visual Program Induction with Function Modularization. 5643-5658 - Zhibin Duan, Yishi Xu, Jianqiao Sun, Bo Chen, Wenchao Chen, Chaojie Wang, Mingyuan Zhou:
Bayesian Deep Embedding Topic Meta-Learner. 5659-5670 - Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam:
Deletion Robust Submodular Maximization over Matroids. 5671-5693 - Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, Dan Rosenbaum:
From data to functa: Your data point is a function and you can treat it like one. 5694-5725 - Valentin Durante, George Katsirelos, Thomas Schiex:
Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models. 5726-5741 - Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni:
Robust Counterfactual Explanations for Tree-Based Ensembles. 5742-5756 - Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot:
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. 5757-5776 - Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, Rose Yu:
LIMO: Latent Inceptionism for Targeted Molecule Generation. 5777-5792 - Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang:
Inductive Biases and Variable Creation in Self-Attention Mechanisms. 5793-5831 - Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi:
Provable Reinforcement Learning with a Short-Term Memory. 5832-5850 - Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang:
Sparsity in Partially Controllable Linear Systems. 5851-5860 - Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi, Ketan Rajawat, Mehdi Bennis, Vaneet Aggarwal:
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning. 5861-5877 - Moshe Eliasof, Eldad Haber, Eran Treister:
pathGCN: Learning General Graph Spatial Operators from Paths. 5878-5891 - Mai Elkady, Hyung Zin Lim, David I. Inouye:
Discrete Tree Flows via Tree-Structured Permutations. 5892-5923 - Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell:
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria. 5924-5943 - Alina Ene, Huy L. Nguyen:
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints. 5944-5967 - Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio:
Towards Scaling Difference Target Propagation by Learning Backprop Targets. 5968-5987 - Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta:
Understanding Dataset Difficulty with V-Usable Information. 5988-6008 - Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer:
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. 6009-6033 - Kion Fallah, Christopher J. Rozell:
Variational Sparse Coding with Learned Thresholding. 6034-6058 - Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky, Peter J. Ramadge:
Training Discrete Deep Generative Models via Gapped Straight-Through Estimator. 6059-6073 - Jiameng Fan, Wenchao Li:
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck. 6074-6102 - Jiajun Fan, Changnan Xiao:
Generalized Data Distribution Iteration. 6103-6184 - Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen:
Variational Wasserstein gradient flow. 6185-6215 - Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt:
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP). 6216-6234 - Shikai Fang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Bayesian Continuous-Time Tucker Decomposition. 6235-6245 - Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. 6246-6283 - Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud:
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks. 6284-6323 - Alexander R. Farhang, Jeremy D. Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue:
Investigating Generalization by Controlling Normalized Margin. 6324-6336 - Gabriele Farina, Chung-Wei Lee, Haipeng Luo, Christian Kroer:
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games. 6337-6357 - Oisin Faust, Hamza Fawzi:
Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis. 6358-6372 - Hao Fei, Shengqiong Wu, Yafeng Ren, Meishan Zhang:
Matching Structure for Dual Learning. 6373-6391 - Yingjie Fei, Ruitu Xu:
Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning. 6392-6417 - Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar:
Private frequency estimation via projective geometry. 6418-6433 - Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin:
An Intriguing Property of Geophysics Inversion. 6434-6446 - Ruili Feng, Jie Xiao, Kecheng Zheng, Deli Zhao, Jingren Zhou, Qibin Sun, Zheng-Jun Zha:
Principled Knowledge Extrapolation with GANs. 6447-6464 - Yuval Filmus, Idan Mehalel, Shay Moran:
A Resilient Distributed Boosting Algorithm. 6465-6473 - Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero:
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. 6474-6498 - Nitai Fingerhut, Matteo Sesia, Yaniv Romano:
Coordinated Double Machine Learning. 6499-6513 - Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Conformal Prediction Sets with Limited False Positives. 6514-6532 - Arthur Flajolet, Claire Bizon Monroc, Karim Beguir, Thomas Pierrot:
Fast Population-Based Reinforcement Learning on a Single Machine. 6533-6547 - Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato:
Fast Relative Entropy Coding with A* coding. 6548-6577 - Adam Foster, Árpi Vezér, Craig A. Glastonbury, Páidí Creed, Samer Abujudeh, Aaron Sim:
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. 6578-6621 - Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki:
Label Ranking through Nonparametric Regression. 6622-6659 - Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari:
A Neural Tangent Kernel Perspective of GANs. 6660-6704 - Abraham Frandsen, Rong Ge, Holden Lee:
Extracting Latent State Representations with Linear Dynamics from Rich Observations. 6705-6725 - Elias Frantar, Dan Alistarh:
SPDY: Accurate Pruning with Speedup Guarantees. 6726-6743 - Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
Revisiting the Effects of Stochasticity for Hamiltonian Samplers. 6744-6778 - Jordan Frécon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo:
Bregman Neural Networks. 6779-6792 - Simon Frieder, Thomas Lukasiewicz:
(Non-)Convergence Results for Predictive Coding Networks. 6793-6810 - Yao Fu, John P. Cunningham, Mirella Lapata:
Scaling Structured Inference with Randomization. 6811-6828 - Haobo Fu, Ye Tian, Hongxiang Yu, Weiming Liu, Shuang Wu, Jiechao Xiong, Ying Wen, Kai Li, Junliang Xing, Qiang Fu, Wei Yang:
Greedy when Sure and Conservative when Uncertain about the Opponents. 6829-6848 - Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin:
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks. 6849-6862 - Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu:
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning. 6863-6877 - Guoji Fu, Peilin Zhao, Yatao Bian:
p-Laplacian Based Graph Neural Networks. 6878-6917 - Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu:
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error. 6918-6943 - Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro:
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data. 6944-6959 - Robert Ganian, Thekla Hamm, Viktoriia Korchemna, Karolina Okrasa, Kirill Simonov:
The Complexity of k-Means Clustering when Little is Known. 6960-6987 - Tian Gao, Debarun Bhattacharjya, Elliot Nelson, Miao Liu, Yue Yu:
IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data. 6988-7001 - Boyan Gao, Henry Gouk, Yongxin Yang, Timothy M. Hospedales:
Loss Function Learning for Domain Generalization by Implicit Gradient. 7002-7016 - Hongchang Gao, Junyi Li, Heng Huang:
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum. 7017-7035 - Yansong Gao, Rahul Ramesh, Pratik Chaudhari:
Deep Reference Priors: What is the best way to pretrain a model? 7036-7051 - Jianfei Gao, Bruno Ribeiro:
On the Equivalence Between Temporal and Static Equivariant Graph Representations. 7052-7076 - Katelyn Gao, Ozan Sener:
Generalizing Gaussian Smoothing for Random Search. 7077-7101 - Yue Gao, Ilia Shumailov, Kassem Fawaz:
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems. 7102-7121 - Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett:
Lazy Estimation of Variable Importance for Large Neural Networks. 7122-7143 - Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. 7144-7163 - Lucy L. Gao, Jane J. Ye, Haian Yin, Shangzhi Zeng, Jin Zhang:
Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems. 7164-7182 - Xiang Gao, Yuqi Zhang, Yingjie Tian:
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization. 7183-7207 - Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon:
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification. 7208-7222 - Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler H. Summers, John Lygeros:
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation. 7223-7240 - Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu:
The power of first-order smooth optimization for black-box non-smooth problems. 7241-7265 - Itai Gat, Nitay Calderon, Roi Reichart, Tamir Hazan:
A Functional Information Perspective on Model Interpretation. 7266-7278 - Camille-Sovanneary Gauthier, Romaric Gaudel, Élisa Fromont:
UniRank: Unimodal Bandit Algorithms for Online Ranking. 7279-7309 - Tomas Geffner, Justin Domke:
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. 7310-7323 - Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, Christopher Potts:
Inducing Causal Structure for Interpretable Neural Networks. 7324-7338 - Claudio Gentile, Zhilei Wang, Tong Zhang:
Achieving Minimax Rates in Pool-Based Batch Active Learning. 7339-7367 - Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März:
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. 7368-7381 - Evangelia Gergatsouli, Christos Tzamos:
Online Learning for Min Sum Set Cover and Pandora's Box. 7382-7403 - Jan E. Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson:
Equivariance versus Augmentation for Spherical Images. 7404-7421 - Udaya Ghai, Udari Madhushani, Naomi Ehrich Leonard, Elad Hazan:
A Regret Minimization Approach to Multi-Agent Control. 7422-7434 - Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, Igor Mordatch:
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning. 7435-7469 - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. 7470-7483 - Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein:
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. 7484-7512 - Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine:
Offline RL Policies Should Be Trained to be Adaptive. 7513-7530 - Avishek Ghosh, Abishek Sankararaman:
Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits. 7531-7549 - Giorgio Giannone, Ole Winther:
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation. 7550-7569 - Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz:
A Joint Exponential Mechanism For Differentially Private Top-k. 7570-7582 - Claire Glanois, Zhaohui Jiang, Xuening Feng, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu, Jianye Hao:
Neuro-Symbolic Hierarchical Rule Induction. 7583-7615 - Karan Goel, Albert Gu, Chris Donahue, Christopher Ré:
It's Raw! Audio Generation with State-Space Models. 7616-7633 - Yu Gong, Greg Mori, Frederick Tung:
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression. 7634-7649 - Chengyue Gong, Lemeng Wu, Qiang Liu:
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. 7650-7664 - Xiuwen Gong, Dong Yuan, Wei Bao:
Partial Label Learning via Label Influence Function. 7665-7678 - Eduard Gorbunov, Alexander Borzunov, Michael Diskin, Max Ryabinin:
Secure Distributed Training at Scale. 7679-7739 - Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. 7740-7765 - Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro:
The State of Sparse Training in Deep Reinforcement Learning. 7766-7792 - Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing:
Causal Inference Through the Structural Causal Marginal Problem. 7793-7824 - Jakub Grudzien Kuba, Christian A. Schröder de Witt, Jakob N. Foerster:
Mirror Learning: A Unifying Framework of Policy Optimisation. 7825-7844 - Christoph Grunau, Václav Rozhon:
Adapting k-means Algorithms for Outliers. 7845-7886 - Yichen Gu, David T. Blaauw, Joshua D. Welch:
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics. 7887-7901 - Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, Bo An:
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning. 7902-7918 - Shanyan Guan, Huayu Deng, Yunbo Wang, Xiaokang Yang:
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields. 7919-7929 - Jiechao Guan, Zhiwu Lu:
Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning. 7930-7948 - Lin Guan, Sarath Sreedharan, Subbarao Kambhampati:
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity. 7949-7967 - Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, Wenwu Zhu:
Large-Scale Graph Neural Architecture Search. 7968-7981 - Ishaan Gulrajani, Tatsunori Hashimoto:
Identifiability Conditions for Domain Adaptation. 7982-7997 - Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans:
A Parametric Class of Approximate Gradient Updates for Policy Optimization. 7998-8015 - Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang:
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes. 8016-8038 - Wenshuo Guo, Michael I. Jordan, Ellen Vitercik:
No-Regret Learning in Partially-Informed Auctions. 8039-8055 - Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten:
Bounding Training Data Reconstruction in Private (Deep) Learning. 8056-8071 - Chong Guo, Michael J. Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James J. DiCarlo:
Adversarially trained neural representations are already as robust as biological neural representations. 8072-8081 - Lan-Zhe Guo, Yufeng Li:
Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding. 8082-8094 - Alan J. X. Guo, Cong Liang, Qing-Hu Hou:
Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage. 8095-8108 - Yiduo Guo, Bing Liu, Dongyan Zhao:
Online Continual Learning through Mutual Information Maximization. 8109-8126 - Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou:
Fast Provably Robust Decision Trees and Boosting. 8127-8144 - Lingbing Guo, Qiang Zhang, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen:
Understanding and Improving Knowledge Graph Embedding for Entity Alignment. 8145-8156 - Mustafa Burak Gurbuz, Constantine Dovrolis:
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks. 8157-8174 - Guy Hacohen, Avihu Dekel, Daphna Weinshall:
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets. 8175-8195 - Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian D. Reid, Lars Petersson, Hongdong Li:
You Only Cut Once: Boosting Data Augmentation with a Single Cut. 8196-8212 - Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi:
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. 8213-8229 - Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu:
G-Mixup: Graph Data Augmentation for Graph Classification. 8230-8248 - Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao, Jiheng Zhang:
Private Streaming SCO in ℓp geometry with Applications in High Dimensional Online Decision Making. 8249-8279 - Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng:
Off-Policy Reinforcement Learning with Delayed Rewards. 8280-8303 - Eric Han, Jonathan Scarlett:
Adversarial Attacks on Gaussian Process Bandits. 8304-8329 - Insu Han, Amir Zandieh, Haim Avron:
Random Gegenbauer Features for Scalable Kernel Methods. 8330-8358 - Ayoub El Hanchi, David A. Stephens, Chris J. Maddison:
Stochastic Reweighted Gradient Descent. 8359-8374 - Jun-Yi Hang, Min-Ling Zhang:
Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification. 8375-8386 - Nicklas Hansen, Hao Su, Xiaolong Wang:
Temporal Difference Learning for Model Predictive Control. 8387-8406 - Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine:
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. 8407-8426 - Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar:
TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm. 8427-8445 - Botao Hao, Tor Lattimore, Chao Qin:
Contextual Information-Directed Sampling. 8446-8464 - Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu:
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. 8465-8483 - Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher, Thierry Artières:
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization. 8484-8501 - Keegan Harris, Dung Daniel T. Ngo, Logan Stapleton, Hoda Heidari, Steven Wu:
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses. 8502-8522 - Yuka Hashimoto, Zhao Wang, Tomoko Matsui:
C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra. 8523-8534 - Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel:
General-purpose, long-context autoregressive modeling with Perceiver AR. 8535-8558 - Jingxuan He, Luca Beurer-Kellner, Martin T. Vechev:
On Distribution Shift in Learning-based Bug Detectors. 8559-8580 - Yixuan He, Quan Gan, David Wipf, Gesine D. Reinert, Junchi Yan, Mihai Cucuringu:
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks. 8581-8612 - Bobby He, Mete Ozay:
Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning. 8613-8634 - Zheng He, Zeke Xie, Quanzhi Zhu, Zengchang Qin:
Sparse Double Descent: Where Network Pruning Aggravates Overfitting. 8635-8659 - Jiahao He, Jiheng Zhang, Rachel Q. Zhang:
A Reduction from Linear Contextual Bandit Lower Bounds to Estimation Lower Bounds. 8660-8677 - Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Prakash Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi:
HyperPrompt: Prompt-based Task-Conditioning of Transformers. 8678-8690 - Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken:
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors. 8691-8707 - Jakob Heiss, Jakob Weissteiner, Hanna S. Wutte, Sven Seuken, Josef Teichmann:
NOMU: Neural Optimization-based Model Uncertainty. 8708-8758 - Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song:
Scaling Out-of-Distribution Detection for Real-World Settings. 8759-8773 - Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. 8774-8795 - Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze:
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology. 8796-8810 - Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. 8811-8832 - Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh:
Deep Hierarchy in Bandits. 8833-8851 - Robert Hönig, Yiren Zhao, Robert D. Mullins:
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. 8852-8866 - Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling:
Equivariant Diffusion for Molecule Generation in 3D. 8867-8887 - Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng:
Conditional GANs with Auxiliary Discriminative Classifier. 8888-8902 - Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He, Qingming Huang:
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems. 8903-8925 - Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein:
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling. 8926-8945 - Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives:
Learning inverse folding from millions of predicted structures. 8946-8970 - Pihe Hu, Yu Chen, Longbo Huang:
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation. 8971-9019 - Yaojie Hu, Jin Tian:
Neuron Dependency Graphs: A Causal Abstraction of Neural Networks. 9020-9040 - Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang:
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL. 9041-9071 - Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang:
On the Role of Discount Factor in Offline Reinforcement Learning. 9072-9098 - Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc V. Le:
Transformer Quality in Linear Time. 9099-9117 - Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch:
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents. 9118-9147 - Zhichun Huang, Rudrasis Chakraborty, Vikas Singh:
Forward Operator Estimation in Generative Models with Kernel Transfer Operators. 9148-9172 - Jiatai Huang, Yan Dai, Longbo Huang:
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits. 9173-9200 - Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei:
Frustratingly Easy Transferability Estimation. 9201-9225 - Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang:
Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably). 9226-9259 - Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. 9260-9279 - Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang:
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design. 9280-9294 - Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Xianghong Hu, Jeffry Wicaksana, Eric P. Xing, Kwang-Ting Cheng:
SDQ: Stochastic Differentiable Quantization with Mixed Precision. 9295-9309 - Yan Huang, Ying Sun, Zehan Zhu, Changzhi Yan, Jinming Xu:
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology. 9310-9345 - Chen Huang, Walter Talbott, Navdeep Jaitly, Joshua M. Susskind:
Efficient Representation Learning via Adaptive Context Pooling. 9346-9355 - Fei Huang, Tianhua Tao, Hao Zhou, Lei Li, Minlie Huang:
On the Learning of Non-Autoregressive Transformers. 9356-9376 - Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He:
Going Deeper into Permutation-Sensitive Graph Neural Networks. 9377-9409 - Fei Huang, Hao Zhou, Yang Liu, Hang Li, Minlie Huang:
Directed Acyclic Transformer for Non-Autoregressive Machine Translation. 9410-9428 - Geert-Jan Huizing, Laura Cantini, Gabriel Peyré:
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors. 9429-9443 - Pierre Humbert, Batiste Le Bars, Ludovic Minvielle:
Robust Kernel Density Estimation with Median-of-Means principle. 9444-9465 - Peter Conway Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy P. Lillicrap:
A data-driven approach for learning to control computers. 9466-9482 - Samuel Hurault, Arthur Leclaire, Nicolas Papadakis:
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization. 9483-9505 - Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Inverse Contextual Bandits: Learning How Behavior Evolves over Time. 9506-9524 - Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry:
Datamodels: Understanding Predictions with Data and Data with Predictions. 9525-9587 - Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit:
Parsimonious Learning-Augmented Caching. 9588-9601 - Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi:
Bayesian Optimization for Distributionally Robust Chance-constrained Problem. 9602-9621 - David Ireland, Giovanni Montana:
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation. 9622-9638 - Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention. 9639-9659 - Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
A Modern Self-Referential Weight Matrix That Learns to Modify Itself. 9660-9677 - Shinji Ito:
Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness. 9678-9694 - Athul Paul Jacob, David J. Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown:
Modeling Strong and Human-Like Gameplay with KL-Regularized Search. 9695-9728 - Brandon G. Jacques, Zoran Tiganj, Aakash Sarkar, Marc W. Howard, Per B. Sederberg:
A deep convolutional neural network that is invariant to time rescaling. 9729-9738 - Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Input Dependent Sparse Gaussian Processes. 9739-9759 - Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner:
Regret Minimization with Performative Feedback. 9760-9785 - Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio:
Biological Sequence Design with GFlowNets. 9786-9801 - Saachi Jain, Dimitris Tsipras, Aleksander Madry:
Combining Diverse Feature Priors. 9802-9832 - Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang:
Training Your Sparse Neural Network Better with Any Mask. 9833-9844 - Sooyong Jang, Sangdon Park, Insup Lee, Osbert Bastani:
Sequential Covariate Shift Detection Using Classifier Two-Sample Tests. 9845-9880 - Martin Jankowiak, Du Phan:
Surrogate Likelihoods for Variational Annealed Importance Sampling. 9881-9901 - Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine:
Planning with Diffusion for Flexible Behavior Synthesis. 9902-9915 - Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. 9916-9937 - Adrián Javaloy, Maryam Meghdadi, Isabel Valera:
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization. 9938-9964 - Samy Jelassi, Yuanzhi Li:
Towards understanding how momentum improves generalization in deep learning. 9965-10040 - Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung:
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer. 10041-10052 - Jihwan Jeong, Parth Jaggi, Andrew Butler, Scott Sanner:
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming. 10053-10067 - Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp:
Agnostic Learnability of Halfspaces via Logistic Loss. 10068-10103 - Zhiwei Jia, Xuanlin Li, Zhan Ling, Shuang Liu, Yiran Wu, Hao Su:
Improving Policy Optimization with Generalist-Specialist Learning. 10104-10119 - Ye Jia, Michelle Tadmor Ramanovich, Tal Remez, Roi Pomerantz:
Translatotron 2: High-quality direct speech-to-speech translation with voice preservation. 10120-10134 - Huiwen Jia, Cong Shi, Siqian Shen:
Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards. 10135-10160 - Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, Ying Wei:
The Role of Deconfounding in Meta-learning. 10161-10176 - Weisen Jiang, James T. Kwok, Yu Zhang:
Subspace Learning for Effective Meta-Learning. 10177-10194 - Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang:
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. 10195-10216 - Wengong Jin, Regina Barzilay, Tommi S. Jaakkola:
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. 10217-10227 - Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari:
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. 10228-10250 - Chi Jin, Qinghua Liu, Tiancheng Yu:
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. 10251-10279 - Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang:
Domain Adaptation for Time Series Forecasting via Attention Sharing. 10280-10297 - Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou:
Accelerated Federated Learning with Decoupled Adaptive Optimization. 10298-10322 - Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu:
Supervised Off-Policy Ranking. 10323-10339 - Jiayin Jin, Zeru Zhang, Yang Zhou, Lingfei Wu:
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing. 10340-10361 - Jaehyeong Jo, Seul Lee, Sung Ju Hwang:
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations. 10362-10383 - Marc Jourdan, Rémy Degenne:
Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits. 10384-10430 - Haotian Ju, Dongyue Li, Hongyang R. Zhang:
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees. 10431-10461 - Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig:
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation. 10462-10475 - Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim:
On Measuring Causal Contributions via do-interventions. 10476-10501 - Yohan Jung, Kyungwoo Song, Jinkyoo Park:
Efficient Approximate Inference for Stationary Kernel on Frequency Domain. 10502-10538 - Praneeth Kacham, David P. Woodruff:
Sketching Algorithms and Lower Bounds for Ridge Regression. 10539-10556 - Jacob D. Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Y. Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert:
Flashlight: Enabling Innovation in Tools for Machine Learning. 10557-10574 - Jan Kaiser, Oliver Stein, Annika Eichler:
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training. 10575-10585 - Konstantinos Kalais, Sotirios Chatzis:
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning. 10586-10597 - Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou:
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. 10598-10632 - Gautam Kamath, Xingtu Liu, Huanyu Zhang:
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. 10633-10660 - Hidetaka Kamigaito, Katsuhiko Hayashi:
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning. 10661-10675 - Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian, Amit Sharma:
Matching Learned Causal Effects of Neural Networks with Domain Priors. 10676-10696 - Nikhil Kandpal, Eric Wallace, Colin Raffel:
Deduplicating Training Data Mitigates Privacy Risks in Language Models. 10697-10707 - Katie Kang, Paula Gradu, Jason J. Choi, Michael Janner, Claire J. Tomlin, Sergey Levine:
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control. 10708-10733 - Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, Chang D. Yoo:
Forget-free Continual Learning with Winning Subnetworks. 10734-10750 - Haim Kaplan, Shachar Schnapp, Uri Stemmer:
Differentially Private Approximate Quantiles. 10751-10761 - Abdullah Karaaslanli, Selin Aviyente:
Simultaneous Graph Signal Clustering and Graph Learning. 10762-10772 - Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz:
Composing Partial Differential Equations with Physics-Aware Neural Networks. 10773-10801 - Parnian Kassraie, Jonas Rothfuss, Andreas Krause:
Meta-Learning Hypothesis Spaces for Sequential Decision-making. 10802-10824 - Priyatham Kattakinda, Soheil Feizi:
FOCUS: Familiar Objects in Common and Uncommon Settings. 10825-10847 - Julian Katz-Samuels, Julia B. Nakhleh, Robert D. Nowak, Yixuan Li:
Training OOD Detectors in their Natural Habitats. 10848-10865 - Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang:
Robustness Implies Generalization via Data-Dependent Generalization Bounds. 10866-10894 - Yigitcan Kaya, Muhammad Bilal Zafar, Sergül Aydöre, Nathalie Rauschmayr, Krishnaram Kenthapadi:
Generating Distributional Adversarial Examples to Evade Statistical Detectors. 10895-10911 - Marcel Keller, Ke Sun:
Secure Quantized Training for Deep Learning. 10912-10938 - Vijay Keswani, Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi:
A Convergent and Dimension-Independent Min-Max Optimization Algorithm. 10939-10973 - Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli:
Neural Network Poisson Models for Behavioural and Neural Spike Train Data. 10974-10996 - Sajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri:
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling. 10997-11057 - Jinkyu Kim, Geeho Kim, Bohyung Han:
Multi-Level Branched Regularization for Federated Learning. 11058-11073 - Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim:
Learning fair representation with a parametric integral probability metric. 11074-11101 - Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song:
Dataset Condensation via Efficient Synthetic-Data Parameterization. 11102-11118 - Heeseung Kim, Sungwon Kim, Sungroh Yoon:
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance. 11119-11133 - Jangho Kim, Juntae Lee, Simyung Chang, Nojun Kwak:
Variational On-the-Fly Personalization. 11134-11147 - Minyoung Kim, Da Li, Shell Xu Hu, Timothy M. Hospedales:
Fisher SAM: Information Geometry and Sharpness Aware Minimisation. 11148-11161 - Sangwon Kim, Jae-Yeal Nam, ByoungChul Ko:
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder. 11162-11172 - Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Sanity Simulations for Saliency Methods. 11173-11200 - Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon:
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation. 11201-11228 - Jung-Hun Kim, Milan Vojnovic, Se-Young Yun:
Rotting Infinitely Many-Armed Bandits. 11229-11254 - Jungbin Kim, Insoon Yang:
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis. 11255-11282 - Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari:
Generalizing to New Physical Systems via Context-Informed Dynamics Model. 11283-11301 - Dani Kiyasseh, Tingting Zhu, David A. Clifton:
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals. 11302-11340 - Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Curriculum Reinforcement Learning via Constrained Optimal Transport. 11341-11358 - David M. Knigge, David W. Romero, Erik J. Bekkers:
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups. 11359-11386 - Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng:
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. 11387-11412 - Refael Kohen, Or Sheffet:
Transfer Learning In Differential Privacy's Hybrid-Model. 11413-11429 - Lukas Köhs, Bastian Alt, Heinz Koeppl:
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems. 11430-11454 - Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang:
Partial disentanglement for domain adaptation. 11455-11472 - Fang Kong, Yichi Zhou, Shuai Li:
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback. 11473-11482 - Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer:
Adaptive Data Analysis with Correlated Observations. 11483-11498 - Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman:
Controlling Conditional Language Models without Catastrophic Forgetting. 11499-11528 - Vladimir R. Kostic, Saverio Salzo, Massimiliano Pontil:
Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity. 11529-11558 - Yiwen Kou, Qinyuan Zheng, Yisen Wang:
Certified Adversarial Robustness Under the Bounded Support Set. 11559-11597 - Sonja Kraiczy, Edith Elkind:
Exact Learning of Preference Structure: Single-peaked Preferences and Beyond. 11598-11612 - Daniel Kramer, Philine Lou Bommer, Daniel Durstewitz, Carlo Tombolini, Georgia Koppe:
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series. 11613-11633 - Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig:
Probabilistic ODE Solutions in Millions of Dimensions. 11634-11649 - Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic:
Active Nearest Neighbor Regression Through Delaunay Refinement. 11650-11664 - Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf:
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. 11665-11682 - Volodymyr Kuleshov, Shachi Deshpande:
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation. 11683-11693 - Bhuvesh Kumar, Jacob D. Abernethy, Venkatesh Saligrama:
ActiveHedge: Hedge meets Active Learning. 11694-11709 - Jogendra Nath Kundu, Akshay R. Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, Venkatesh Babu Radhakrishnan:
Balancing Discriminability and Transferability for Source-Free Domain Adaptation. 11710-11728 - Vladislav Kurenkov, Sergey Kolesnikov:
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters. 11729-11752 - Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi:
Equivariant Priors for compressed sensing with unknown orientation. 11753-11771 - Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor:
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. 11772-11789 - Thibault Lahire, Matthieu Geist, Emmanuel Rachelson:
Large Batch Experience Replay. 11790-11813 - Fan Lai, Yinwei Dai, Sanjay Sri Vallabh Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, Mosharaf Chowdhury:
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. 11814-11827 - Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S. Cheung, Chen-Nee Chuah:
Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data. 11828-11843 - Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc:
Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. 11844-11867 - Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill:
Tell me why! Explanations support learning relational and causal structure. 11868-11890 - Sylvain Lamprier, Thomas Scialom, Antoine Chaffin, Vincent Claveau, Ewa Kijak, Jacopo Staiano, Benjamin Piwowarski:
Generative Cooperative Networks for Natural Language Generation. 11891-11905 - Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li:
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. 11906-11917 - Tal Lancewicki, Aviv Rosenberg, Yishay Mansour:
Cooperative Online Learning in Stochastic and Adversarial MDPs. 11918-11968 - Zoe Landgraf, Alexander Sorkine-Hornung, Ricardo Silveira Cabral:
PINs: Progressive Implicit Networks for Multi-Scale Neural Representations. 11969-11984 - Hunter Lang, Monica N. Agrawal, Yoon Kim, David A. Sontag:
Co-training Improves Prompt-based Learning for Large Language Models. 11985-12003 - Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger:
Goal Misgeneralization in Deep Reinforcement Learning. 12004-12019 - Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
Marginal Tail-Adaptive Normalizing Flows. 12020-12048 - Tim Tsz-Kit Lau, Han Liu:
Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes. 12049-12077 - Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist:
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. 12078-12095 - Hannah Lawrence, Bobak Toussi Kiani, Kristian G. Georgiev, Andrew K. Dienes:
Implicit Bias of Linear Equivariant Networks. 12096-12125 - John Lazarsfeld, Aaron Johnson, Emmanuel Adéníran:
Differentially Private Maximal Information Coefficients. 12126-12163 - Khang Le, Dung Q. Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho:
Entropic Gromov-Wasserstein between Gaussian Distributions. 12164-12203 - Hung Le, Svetha Venkatesh:
Neurocoder: General-Purpose Computation Using Stored Neural Programs. 12204-12221 - James-Michael Leahy, Bekzhan Kerimkulov, David Siska, Lukasz Szpruch:
Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime. 12222-12252 - Hugo Lebeau, Romain Couillet, Florent Chatelain:
A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources. 12253-12281 - Jongmin Lee, Joo Young Choi, Ernest K. Ryu, Albert No:
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks. 12282-12351 - Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon:
Dataset Condensation with Contrastive Signals. 12352-12364 - Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon:
Confidence Score for Source-Free Unsupervised Domain Adaptation. 12365-12377 - Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank Chongwoo Park:
A Statistical Manifold Framework for Point Cloud Data. 12378-12402 - Eunsang Lee, Joon-Woo Lee, Junghyun Lee, Young-Sik Kim, Yongjune Kim, Jong-Seon No, Woosuk Choi:
Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions. 12403-12422 - Yoonhyung Lee, Sungdong Lee, Joong-Ho Won:
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert. 12423-12454 - Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew M. Saxe:
Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation. 12455-12477 - Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song:
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization. 12478-12497 - Sokbae Lee, Serena Ng:
Least Squares Estimation using Sketched Data with Heteroskedastic Errors. 12498-12520 - Changwoo J. Lee, Huiyan Sang:
Why the Rich Get Richer? On the Balancedness of Random Partition Models. 12521-12541 - Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. 12542-12569 - Liu Leqi, Audrey Huang, Zachary C. Lipton, Kamyar Azizzadenesheli:
Supervised Learning with General Risk Functionals. 12570-12592 - Sagi Levanon, Nir Rosenfeld:
Generalized Strategic Classification and the Case of Aligned Incentives. 12593-12618 - Wenjie Li, Adarsh Barik, Jean Honorio:
A Simple Unified Framework for High Dimensional Bandit Problems. 12619-12655 - Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks Using Scale Invariant Architectures. 12656-12684 - Yanxi Li, Xinghao Chen, Minjing Dong, Yehui Tang, Yunhe Wang, Chang Xu:
Spatial-Channel Token Distillation for Vision MLPs. 12685-12695 - Hepeng Li, Nicholas Clavette, Haibo He:
An Analytical Update Rule for General Policy Optimization. 12696-12716 - Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie:
On Convergence of Gradient Descent Ascent: A Tight Local Analysis. 12717-12740 - Yanwen Li, Siyang Gao:
On the Finite-Time Performance of the Knowledge Gradient Algorithm. 12741-12764 - Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu:
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning. 12765-12781 - Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin:
G2CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters. 12782-12796 - Shibo Li, Robert M. Kirby, Shandian Zhe:
Decomposing Temporal High-Order Interactions via Latent ODEs. 12797-12812 - Henry Li, Yuval Kluger:
Neural Inverse Transform Sampler. 12813-12825 - Changbin Li, Suraj Kothawade, Feng Chen, Rishabh K. Iyer:
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information. 12826-12842 - Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao:
Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning. 12843-12856 - Xiaoyun Li, Ping Li:
C-MinHash: Improving Minwise Hashing with Circulant Permutation. 12857-12887 - Junnan Li, Dongxu Li, Caiming Xiong, Steven C. H. Hoi:
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. 12888-12900 - Huan Li, Zhouchen Lin:
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ε-7/4) Complexity. 12901-12916 - Peizhao Li, Hongfu Liu:
Achieving Fairness at No Utility Cost via Data Reweighing with Influence. 12917-12930 - Shaojie Li, Yong Liu:
High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails. 12931-12963 - Jiangmeng Li, Wenwen Qiang, Changwen Zheng, Bing Su, Hui Xiong:
MetAug: Contrastive Learning via Meta Feature Augmentation. 12964-12978 - Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang:
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. 12979-12997 - Mingjie Li, Yisen Wang, Zhouchen Lin:
CerDEQ: Certifiable Deep Equilibrium Model. 12998-13013 - Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. 13014-13051 - Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua:
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. 13052-13065 - Yueheng Li, Guangming Xie, Zongqing Lu:
Difference Advantage Estimation for Multi-Agent Policy Gradients. 13066-13085 - Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith:
Private Adaptive Optimization with Side information. 13086-13105 - Chao Li, Junhua Zeng, Zerui Tao, Qibin Zhao:
Permutation Search of Tensor Network Structures via Local Sampling. 13106-13124 - Ruilin Li, Hongyuan Zha, Molei Tao:
Hessian-Free High-Resolution Nesterov Acceleration For Sampling. 13125-13162 - Linyi Li, Jiawei Zhang, Tao Xie, Bo Li:
Double Sampling Randomized Smoothing. 13163-13208 - Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang:
HousE: Knowledge Graph Embedding with Householder Parameterization. 13209-13224 - Bei Li, Tong Zheng, Yi Jing, Chengbo Jiao, Tong Xiao, Jingbo Zhu:
Learning Multiscale Transformer Models for Sequence Generation. 13225-13241 - Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian:
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily. 13242-13256 - Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson:
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. 13257-13270 - Yuanbang Liang, Jing Wu, Yu-Kun Lai, Yipeng Qin:
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling. 13271-13284 - Litian Liang, Yaosheng Xu, Stephen McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox:
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks. 13285-13301 - Hwijoon Lim, Yechan Kim, Sukmin Yun, Jinwoo Shin, Dongsu Han:
TSPipe: Learn from Teacher Faster with Pipelines. 13302-13312 - Yu Chin, Fabian Lim, Laura Wynter, Shiau Hong Lim:
Order Constraints in Optimal Transport. 13313-13333 - Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xueyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, Luc Van Gool:
Flow-Guided Sparse Transformer for Video Deblurring. 13334-13343 - Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang:
Federated Learning with Positive and Unlabeled Data. 13344-13355 - Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman:
Decentralized Online Convex Optimization in Networked Systems. 13356-13393 - Jing Lin, Xiaowan Hu, Yuanhao Cai, Haoqian Wang, Youliang Yan, Xueyi Zou, Yulun Zhang, Luc Van Gool:
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration. 13394-13404 - Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif:
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. 13405-13430 - Honghao Lin, Tian Luo, David P. Woodruff:
Learning Augmented Binary Search Trees. 13431-13440 - Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. 13441-13467 - Jinkun Lin, Anqi Zhang, Mathias Lécuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen:
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments. 13468-13504 - David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Interactively Learning Preference Constraints in Linear Bandits. 13505-13527 - Pierre Liotet, Davide Maran, Lorenzo Bisi, Marcello Restelli:
Delayed Reinforcement Learning by Imitation. 13528-13556 - Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Stratis Gavves:
CITRIS: Causal Identifiability from Temporal Intervened Sequences. 13557-13603 - Adam Liska, Tomás Kociský, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien de Masson d'Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, Angeliki Lazaridou:
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models. 13604-13622 - Zijian Liu, Qinxun Bai, Jose H. Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou:
Distributionally Robust Q-Learning. 13623-13643 - Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao:
Constrained Variational Policy Optimization for Safe Reinforcement Learning. 13644-13668 - Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao:
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. 13669-13703 - Xin Liu, Jiayang Cheng, Yangqiu Song, Xin Jiang:
Boosting Graph Structure Learning with Dummy Nodes. 13704-13716 - Weiming Liu, Huacong Jiang, Bin Li, Houqiang Li:
Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent. 13717-13745 - Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda:
Deep Probability Estimation. 13746-13781 - Rui Liu, Young Jin Kim, Alexandre Muzio, Hany Hassan:
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers. 13782-13792 - Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess:
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. 13793-13806 - Yibing Liu, Haoliang Li, Yangyang Guo, Chenqi Kong, Jing Li, Shiqi Wang:
Rethinking Attention-Model Explainability through Faithfulness Violation Test. 13807-13824 - Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, Yixuan Zhang:
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training. 13825-13856 - Chang Liu, Chenfei Lou, Runzhong Wang, Alan Yuhan Xi, Li Shen, Junchi Yan:
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning. 13857-13869 - Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. 13870-13911 - Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji:
Generating 3D Molecules for Target Protein Binding. 13912-13924 - Rui Liu, Barzan Mozafari:
Communication-efficient Distributed Learning for Large Batch Optimization. 13925-13946 - Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy L. Nguyen:
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction. 13947-13994 - Xingyu Liu, Deepak Pathak, Kris Kitani:
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer. 13995-14007 - Yuanyuan Liu, Fanhua Shang, Weixin An, Hongying Liu, Zhouchen Lin:
Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots. 14008-14035 - Qinghua Liu, Yuanhao Wang, Chi Jin:
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. 14036-14053 - Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu:
Local Augmentation for Graph Neural Networks. 14054-14072 - Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alexander G. Schwing:
Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language. 14073-14093 - Zhihan Liu, Yufeng Zhang, Zuyue Fu, Zhuoran Yang, Zhaoran Wang:
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation. 14094-14138 - Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael W. Mahoney, Alvin Cheung:
GACT: Activation Compressed Training for Generic Network Architectures. 14139-14152 - Sheng Liu, Zhihui Zhu, Qing Qu, Chong You:
Robust Training under Label Noise by Over-parameterization. 14153-14172 - Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang:
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization. 14173-14196 - Robert Tyler Loftin, Frans A. Oliehoek:
On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games. 14197-14209 - Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. 14210-14222 - Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson:
Bayesian Model Selection, the Marginal Likelihood, and Generalization. 14223-14247 - Yizhang Lou, Chris E. Mingard, Soufiane Hayou:
Feature Learning and Signal Propagation in Deep Neural Networks. 14248-14282 - Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala:
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension. 14283-14314 - Songtao Lu:
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization. 14315-14357 - Xiaoyu Lu, Alexis Boukouvalas, James Hensman:
Additive Gaussian Processes Revisited. 14358-14383 - Yupu Lu, Shijie Lin, Guanqi Chen, Jia Pan:
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias. 14384-14397 - Christopher Lu, Timon Willi, Christian A. Schröder de Witt, Jakob N. Foerster:
Model-Free Opponent Shaping. 14398-14411 - Xingyu Lu, Qintong Wu, Wenliang Zhong:
Multi-slots Online Matching with High Entropy. 14412-14428 - Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching. 14429-14460 - Ekdeep Singh Lubana, Chi Ian Tang, Fahim Kawsar, Robert P. Dick, Akhil Mathur:
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering. 14461-14484 - Daniel Lundström, Tianjian Huang, Meisam Razaviyayn:
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions. 14485-14508 - Zhao Tang Luo, Huiyan Sang, Bani K. Mallick:
BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression. 14509-14526 - Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, Tieniu Tan:
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring. 14527-14541 - Xu Luo, Jing Xu, Zenglin Xu:
Channel Importance Matters in Few-Shot Image Classification. 14542-14559 - Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Deep Reinforcement Learning. 14560-14581 - Qi Lyu, Xiao Fu:
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis. 14582-14600 - Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang:
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. 14601-14638 - Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani:
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching. 14639-14663 - Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang:
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding. 14664-14698 - Jan MacDonald, Mathieu Besançon, Sebastian Pokutta:
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. 14699-14716 - Peter Macgregor, He Sun:
A Tighter Analysis of Spectral Clustering, and Beyond. 14717-14742 - Parsa Mahmoudieh, Deepak Pathak, Trevor Darrell:
Zero-Shot Reward Specification via Grounded Natural Language. 14743-14752 - Subhabrata Majumdar, Snigdhansu Chatterjee:
Feature selection using e-values. 14753-14773 - Arjun Majumdar, Gunnar A. Sigurdsson, Robinson Piramuthu, Jesse Thomason, Dhruv Batra, Gaurav S. Sukhatme:
SSL Enables Learning from Sparse Rewards in Image-Goal Navigation. 14774-14785 - Bodhisattwa Prasad Majumder, Oana Camburu, Thomas Lukasiewicz, Julian J. McAuley:
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations. 14786-14801 - Carol Mak, Fabian Zaiser, Luke Ong:
Nonparametric Involutive Markov Chain Monte Carlo. 14802-14859 - Disha Makhija, Xing Han, Nhat Ho, Joydeep Ghosh:
Architecture Agnostic Federated Learning for Neural Networks. 14860-14870 - Cédric Malherbe, Kevin Scaman:
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach. 14871-14886 - Osman Asif Malik:
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees. 14887-14917 - Ben Maman, Amit H. Bermano:
Unaligned Supervision for Automatic Music Transcription in The Wild. 14918-14934 - Jayanta Mandi, Víctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns:
Decision-Focused Learning: Through the Lens of Learning to Rank. 14935-14947 - Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. 14948-14978 - Tudor A. Manole, Nhat Ho:
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models. 14979-15006 - Weichao Mao, Lin Yang, Kaiqing Zhang, Tamer Basar:
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning. 15007-15049 - Antonia Marcu, Adam Prügel-Bennett:
On the Effects of Artificial Data Modification. 15050-15069 - Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni:
Personalized Federated Learning through Local Memorization. 15070-15092 - Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati:
Nested Bandits. 15093-15121 - Iñigo Martinez, Elisabeth Viles, Igor G. Olaizola:
Closed-Form Diffeomorphic Transformations for Time Series Alignment. 15122-15158 - Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer:
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators. 15159-15179 - Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. 15180-15195 - Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. 15196-15219 - Augustine N. Mavor-Parker, Kimberly A. Young, Caswell Barry, Lewis D. Griffin:
How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation. 15220-15240 - Mantas Mazeika, Bo Li, David A. Forsyth:
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection. 15241-15254