Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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