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ORCID40th ICML 2023: Honolulu, HI, USA
- Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett:
International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research 202, PMLR 2023 - Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. 1-18 - Ahmed Abbas, Paul Swoboda:
ClusterFuG: Clustering Fully connected Graphs by Multicut. 19-30 - Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk:
Generalization on the Unseen, Logic Reasoning and Degree Curriculum. 31-60 - Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit:
Toward Large Kernel Models. 61-78 - Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé:
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. 79-90 - Naoufal Acharki, Ramiro Lugo, Antoine Bertoncello, Josselin Garnier:
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects. 91-132 - Steven Adams, Andrea Patane, Morteza Lahijanian, Luca Laurenti:
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming. 133-151 - Atish Agarwala, Yann N. Dauphin:
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. 152-168 - Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. 169-195 - Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee:
Global optimality of Elman-type RNNs in the mean-field regime. 196-227 - Pranjal Aggarwal, Ameet Deshpande, Karthik R. Narasimhan:
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification. 228-247 - Mehran Aghabozorgi, Shichong Peng, Ke Li:
Adaptive IMLE for Few-shot Pretraining-free Generative Modelling. 248-264 - Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer:
Scaling Laws for Generative Mixed-Modal Language Models. 265-279 - Anass Aghbalou, Guillaume Staerman:
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. 280-303 - Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa:
Constrained Causal Bayesian Optimization. 304-321 - Elisabeth Agoritsas, Giovanni Catania, Aurélien Decelle, Beatriz Seoane:
Explaining the effects of non-convergent MCMC in the training of Energy-Based Models. 322-336 - Gati V. Aher, Rosa I. Arriaga, Adam Tauman Kalai:
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies. 337-371 - Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio:
Interventional Causal Representation Learning. 372-407 - Elisabeth Ailer, Jason S. Hartford, Niki Kilbertus:
Sequential Underspecified Instrument Selection for Cause-Effect Estimation. 408-420 - Matthew Aitchison, Penny Sweetser, Marcus Hutter:
Atari-5: Distilling the Arcade Learning Environment down to Five Games. 421-438 - Naveed Akhtar, Mohammad A. A. K. Jalwana:
Towards credible visual model interpretation with path attribution. 439-457 - Ahmet Alacaoglu, Hanbaek Lyu:
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data. 458-489 - Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Recasting Self-Attention with Holographic Reduced Representations. 490-507 - Wael Alghamdi, Juan Felipe Gómez, Shahab Asoodeh, Flávio P. Calmon, Oliver Kosut, Lalitha Sankar:
The Saddle-Point Method in Differential Privacy. 508-528 - Christian H. X. Ali Mehmeti-Göpel, Jan Disselhoff:
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think. 529-546 - James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. 547-568 - Youssef Allouah
, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
On the Privacy-Robustness-Utility Trilemma in Distributed Learning. 569-626 - Baris Alparslan, Sinan Yildirim, S. Ilker Birbil:
Differentially Private Distributed Bayesian Linear Regression with MCMC. 627-641 - Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch:
Robust and Scalable Bayesian Online Changepoint Detection. 642-663 - Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl:
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels. 664-690 - Sanae Amani, Tor Lattimore, András György, Lin Yang:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. 691-717 - Alan Nawzad Amin, Eli N. Weinstein, Debora Susan Marks:
A Kernelized Stein Discrepancy for Biological Sequences. 718-767 - Philip Amortila, Nan Jiang, Csaba Szepesvári:
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. 768-790 - Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko:
Meta Optimal Transport. 791-813 - Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm:
Near-Optimal Φ-Regret Learning in Extensive-Form Games. 814-839 - Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion:
A Modern Look at the Relationship between Sharpness and Generalization. 840-902 - Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with Large Step Sizes Learns Sparse Features. 903-925 - Abdul Fatir Ansari, Alvin Heng, Andre Lim, Harold Soh:
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series. 926-951 - Antonios Antoniadis, Joan Boyar, Marek Eliás, Lene Monrad Favrholdt, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon:
Paging with Succinct Predictions. 952-968 - Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, Bertrand Simon:
Mixing Predictions for Online Metric Algorithms. 969-983 - Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Exponential Smoothing for Off-Policy Learning. 984-1017 - Jamil Arbas, Hassan Ashtiani, Christopher Liaw:
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. 1018-1040 - Sohei Arisaka, Qianxiao Li:
Principled Acceleration of Iterative Numerical Methods Using Machine Learning. 1041-1059 - Raman Arora, Raef Bassily, Tomás González, Cristóbal Guzmán, Michael Menart, Enayat Ullah:
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization. 1060-1092 - Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky:
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning. 1093-1106 - Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. 1107-1120 - Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. 1121-1146 - Amit Attia, Tomer Koren:
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance. 1147-1171 - Idan Attias, Steve Hanneke:
Adversarially Robust PAC Learnability of Real-Valued Functions. 1172-1199 - Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas:
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning. 1200-1217 - Yuval Atzmon, Eli A. Meirom, Shie Mannor, Gal Chechik:
Learning to Initiate and Reason in Event-Driven Cascading Processes. 1218-1243 - Julien Aubert, Luc Lehéricy, Patricia Reynaud-Bouret:
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm. 1244-1275 - Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, Jian Zhou:
Dirichlet Diffusion Score Model for Biological Sequence Generation. 1276-1301 - Kyriakos Axiotis, Maxim Sviridenko:
Gradient Descent Converges Linearly for Logistic Regression on Separable Data. 1302-1319 - Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Naive imputation implicitly regularizes high-dimensional linear models. 1320-1340 - Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer:
Half-Hop: A graph upsampling approach for slowing down message passing. 1341-1360 - Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica:
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning. 1361-1395 - Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang:
Personalized Subgraph Federated Learning. 1396-1415 - Alexei Baevski, Arun Babu, Wei-Ning Hsu, Michael Auli:
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language. 1416-1429 - Charlotte Baey, Maud Delattre, Estelle Kuhn, Jean-Benoist Leger, Sarah Lemler:
Efficient preconditioned stochastic gradient descent for estimation in latent variable models. 1430-1453 - Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li:
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. 1454-1471 - Yushi Bai, Xin Lv, Juanzi Li, Lei Hou:
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization. 1472-1491 - Yikun Bai, Ivan Vladimir Medri, Rocio Diaz Martin, Rana Muhammad Shahroz Khan, Soheil Kolouri:
Linear optimal partial transport embedding. 1492-1520 - Justin M. Baker, Qingsong Wang, Cory D. Hauck, Bao Wang:
Implicit Graph Neural Networks: A Monotone Operator Viewpoint. 1521-1548 - Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau:
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. 1549-1563 - Oleg Balabanov, Matthias Beaupère, Laura Grigori, Victor Lederer:
Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures. 1564-1576 - Philip J. Ball, Laura M. Smith, Ilya Kostrikov, Sergey Levine:
Efficient Online Reinforcement Learning with Offline Data. 1577-1594 - Marin Ballu, Quentin Berthet:
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes. 1595-1613 - András Balogh, Márk Jelasity:
On the Functional Similarity of Robust and Non-Robust Neural Representations. 1614-1635 - Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Robust Budget Pacing with a Single Sample. 1636-1659 - Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh:
Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time. 1660-1691 - Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu:
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. 1692-1717 - Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He:
Optimizing the Collaboration Structure in Cross-Silo Federated Learning. 1718-1736 - Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel:
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation. 1737-1752 - Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. 1753-1800 - Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frédéric Precioso, Mateja Jamnik, Giuseppe Marra:
Interpretable Neural-Symbolic Concept Reasoning. 1801-1825 - Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina:
Moccasin: Efficient Tensor Rematerialization for Neural Networks. 1826-1837 - Raef Bassily, Ziteng Sun:
User-level Private Stochastic Convex Optimization with Optimal Rates. 1838-1851 - Soumya Basu, Ankit Singh Rawat, Manzil Zaheer:
A Statistical Perspective on Retrieval-Based Models. 1852-1886 - Jakob Bauer, Kate Baumli, Feryal M. P. Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M. Zhang:
Human-Timescale Adaptation in an Open-Ended Task Space. 1887-1935 - Jerome Baum, Heishiro Kanagawa, Arthur Gretton:
A Kernel Stein Test of Goodness of Fit for Sequential Models. 1936-1953 - Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. 1954-1977 - Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. 1978-2002 - Daniel Beechey, Thomas M. S. Smith, Özgür Simsek:
Explaining Reinforcement Learning with Shapley Values. 2003-2014 - Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov:
TIDE: Time Derivative Diffusion for Deep Learning on Graphs. 2015-2030 - Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder:
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. 2031-2049 - Christopher M. Bender, Yifeng Shi, Marc Niethammer, Junier Oliva:
Continuously Parameterized Mixture Models. 2050-2062 - Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny:
Controllable Neural Symbolic Regression. 2063-2077 - Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. 2078-2091 - M. Amine Bennouna, Ryan Lucas, Bart P. G. Van Parys:
Certified Robust Neural Networks: Generalization and Corruption Resistance. 2092-2112 - Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick:
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. 2113-2163 - Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Francesco Trovò, Nicola Gatti:
Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion. 2164-2183 - Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò, Nicola Gatti:
Constrained Phi-Equilibria. 2184-2205 - Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. 2206-2233 - Arturs Berzins:
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision. 2234-2244 - Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis:
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks. 2245-2271 - Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic:
Neural Algorithmic Reasoning with Causal Regularisation. 2272-2288 - Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol:
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. 2289-2312 - Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Bandit Online Linear Optimization with Hints and Queries. 2313-2336 - Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai:
Improved Online Conformal Prediction via Strongly Adaptive Online Learning. 2337-2363 - Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri:
Data-Copying in Generative Models: A Formal Framework. 2364-2396 - Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, Oskar van der Wal:
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling. 2397-2430 - Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan:
StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes. 2431-2451 - Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. 2452-2470 - Julian Bitterwolf, Maximilian Müller, Matthias Hein:
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. 2471-2506 - Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Thomas Kipf:
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames. 2507-2527 - Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang:
Understanding Oversquashing in GNNs through the Lens of Effective Resistance. 2528-2547 - Charlie Blake, Douglas Orr, Carlo Luschi:
Unit Scaling: Out-of-the-Box Low-Precision Training. 2548-2576 - Matthieu Blanke, Marc Lelarge:
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems. 2577-2591 - Markus Bläser:
Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits. 2592-2602 - Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux:
Learning the Dynamics of Sparsely Observed Interacting Systems. 2603-2640 - Niclas Boehmer, L. Elisa Celis, Lingxiao Huang, Anay Mehrotra, Nisheeth K. Vishnoi:
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions. 2641-2688 - Niclas Boehmer, Piotr Faliszewski, Sonja Kraiczy:
Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist. 2689-2711 - David Boetius, Stefan Leue, Tobias Sutter:
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks. 2712-2737 - Simone Bombari, Shayan Kiyani, Marco Mondelli:
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels. 2738-2776 - Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. 2777-2805 - Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar:
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. 2806-2823 - Victor Boone, Bruno Gaujal:
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning. 2824-2856 - Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete:
Model-agnostic Measure of Generalization Difficulty. 2857-2884 - Shahine Bouabid, Jake Fawkes, Dino Sejdinovic:
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. 2885-2913 - Malik Boudiaf, Tom Denton, Bart van Merrienboer, Vincent Dumoulin, Eleni Triantafillou:
In Search for a Generalizable Method for Source Free Domain Adaptation. 2914-2931 - Adam Bouland, Yosheb M. Getachew, Yujia Jin, Aaron Sidford, Kevin Tian:
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. 2932-2952 - Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, Thomas Serre:
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? 2953-3002 - Michael Bowling, John D. Martin, David Abel, Will Dabney:
Settling the Reward Hypothesis. 3003-3020 - Manuel Brack, Patrick Schramowski, Björn Deiseroth, Kristian Kersting:
ILLUME: Rationalizing Vision-Language Models through Human Interactions. 3021-3037