| 2025 | "All-Something-Nothing" Phase Transitions in Planted k-Factor Recovery (Extended Abstract). Julia Gaudio, Colin Sandon, Jiaming Xu, Dana Yang |
| 2025 | A Distributional-Lifting Theorem for PAC Learning. Guy Blanc, Jane Lange, Carmen Strassle, Li-Yang Tan |
| 2025 | A Fine-grained Characterization of PAC Learnability. Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen |
| 2025 | A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis. Akash Kumar, Rahul Parhi, Mikhail Belkin |
| 2025 | A Polynomial-time Algorithm for Online Sparse Linear Regression with Improved Regret Bound under Weaker Conditions. Junfan Li, Shizhong Liao, Zenglin Xu, Liqiang Nie |
| 2025 | A Proof of The Changepoint Detection Threshold Conjecture in Preferential Attachment Models. Hang Du, Shuyang Gong, Jiaming Xu |
| 2025 | A Theory of Learning with Autoregressive Chain of Thought. Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro |
| 2025 | Accelerating Proximal Gradient Descent via Silver Stepsizes. Jinho Bok, Jason M. Altschuler |
| 2025 | Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals. Anxin Guo, Aravindan Vijayaraghavan |
| 2025 | Algorithms for Sparse LPN and LSPN Against Low-noise (extended abstract). Xue Chen, Wenxuan Shu, Zhaienhe Zhou |
| 2025 | Alternating Regret for Online Convex Optimization. Soumita Hait, Ping Li, Haipeng Luo, Mengxiao Zhang |
| 2025 | An uncertainty principle for Linear Recurrent Neural Networks. Alexandre François, Antonio Orvieto, Francis R. Bach |
| 2025 | Anytime Acceleration of Gradient Descent. Zihan Zhang, Jason D. Lee, Simon S. Du, Yuxin Chen |
| 2025 | Approximating the total variation distance between spin systems. Weiming Feng, Hongyang Liu, Minji Yang |
| 2025 | Are all models wrong? Fundamental limits in distribution-free empirical model falsification. Manuel M. Müller, Yuetian Luo, Rina Foygel Barber |
| 2025 | Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy. Kaito Fujii |
| 2025 | Better Private Distribution Testing by Leveraging Unverified Auxiliary Data. Maryam Aliakbarpour, Arnav Burudgunte, Clément L. Canonne, Ronitt Rubinfeld |
| 2025 | Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks. Omar Montasser, Abhishek Shetty, Nikita Zhivotovskiy |
| 2025 | Beyond propagation of chaos: A stochastic algorithm for mean field optimization. Chandan Tankala, Dheeraj Nagaraj, Anant Raj |
| 2025 | Black-Box Reductions for Decentralized Online Convex Optimization in Changing Environments. Yuanyu Wan |
| 2025 | Blackwell's Approachability with Approximation Algorithms. Dan Garber, Mhna Massalha |
| 2025 | Can a calibration metric be both testable and actionable? Raphael Rossellini, Jake A. Soloff, Rina Foygel Barber, Zhimei Ren, Rebecca Willett |
| 2025 | Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs. Alexander Ryabchenko, Idan Attias, Daniel M. Roy |
| 2025 | Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of Φ-Mutual Information (Extended Abstract). Jiaming Liang, Siddharth Mitra, Andre Wibisono |
| 2025 | Community detection with the Bethe-Hessian. Ludovic Stephan, Yizhe Zhu |
| 2025 | Complexity of Injectivity and Verification of ReLU Neural Networks (Extended Abstract). Vincent Froese, Moritz Grillo, Martin Skutella |
| 2025 | Compression Barriers in Autoregressive Transformers. Themistoklis Haris, Krzysztof Onak |
| 2025 | Computable learning of natural hypothesis classes. Syed Akbari, Matthew Harrison-Trainor |
| 2025 | Computational Equivalence of Spiked Covariance and Spiked Wigner Models via Gram-Schmidt Perturbation. Guy Bresler, Alina Harbuzova |
| 2025 | Computational Intractability of Strategizing against Online Learners. Angelos Assos, Yuval Dagan, Nived Rajaraman |
| 2025 | Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification (extended abstract). Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, Dylan J. Foster |
| 2025 | Computing High-dimensional Confidence Sets for Arbitrary Distributions. Chao Gao, Liren Shan, Vaidehi Srinivas, Aravindan Vijayaraghavan |
| 2025 | Computing Optimal Regularizers for Online Linear Optimization. Khashayar Gatmiry, Jon Schneider, Stefanie Jegelka |
| 2025 | Conference on Learning Theory 2025: Preface. Nika Haghtalab, Ankur Moitra |
| 2025 | Corrupted Learning Dynamics in Games. Taira Tsuchiya, Shinji Ito, Haipeng Luo |
| 2025 | Data Selection for ERMs. Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff |
| 2025 | Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. Quan M. Nguyen, Shinji Ito, Junpei Komiyama, Nishant A. Mehta |
| 2025 | Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy. Fan Chen, Alexander Rakhlin |
| 2025 | Decision Making in Hybrid Environments: A Model Aggregation Approach. Haolin Liu, Chen-Yu Wei, Julian Zimmert |
| 2025 | Depth Separations in Neural Networks: Separating the Dimension from the Accuracy. Itay Safran, Daniel Reichman, Paul Valiant |
| 2025 | Detecting Arbitrary Planted Subgraphs in Random Graphs. Dor Elimelech, Wasim Huleihel |
| 2025 | Deterministic Apple Tasting. Zachary Chase, Idan Mehalel |
| 2025 | Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts. Pan Peng, Hangyu Xu |
| 2025 | DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory. Jerry Chee, Arturs Backurs, Rainie Heck, Li Zhang, Janardhan Kulkarni, Thomas Rothvoss, Sivakanth Gopi |
| 2025 | Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries. Arnab Maiti, Zhiyuan Fan, Kevin Jamieson, Lillian J. Ratliff, Gabriele Farina |
| 2025 | Efficiently learning and sampling multimodal distributions with data-based initialization. Frederic Koehler, Holden Lee, Thuy-Duong Vuong |
| 2025 | Estimating stationary mass, frequency by frequency. Milind Nakul, Vidya Muthukumar, Ashwin Pananjady |
| 2025 | Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{SO}(d)$. Amit Daniely |
| 2025 | Experimental Design for Semiparametric Bandits. Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh |
| 2025 | Exploring Facets of Language Generation in the Limit. Moses Charikar, Chirag Pabbaraju |
| 2025 | Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play. John Lazarsfeld, Georgios Piliouras, Ryann Sim, Andre Wibisono |
| 2025 | Fast and Multiphase Rates for Nearest Neighbor Classifiers. Pengkun Yang, Jingzhao Zhang |
| 2025 | Faster Acceleration for Steepest Descent. Cedar Site Bai, Brian Bullins |
| 2025 | Faster Algorithms for Agnostically Learning Disjunctions and their Implications. Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren |
| 2025 | Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method. Sachin Garg, Michal Derezinski |
| 2025 | From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs. Cynthia Dwork, Chris Hays, Nicole Immorlica, Juan C. Perdomo, Pranay Tankala |
| 2025 | Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications. Yizhou Xu, Antoine Maillard, Lenka Zdeborová, Florent Krzakala |
| 2025 | Generalization error bound for denoising score matching under relaxed manifold assumption. Konstantin Yakovlev, Nikita Puchkin |
| 2025 | Generation through the lens of learning theory. Vinod Raman, Jiaxun Li, Ambuj Tewari |
| 2025 | Gradient Methods with Online Scaling. Wenzhi Gao, Ya-Chi Chu, Yinyu Ye, Madeleine Udell |
| 2025 | Heavy-tailed Estimation is Easier than Adversarial Contamination. Yeshwanth Cherapanamjeri, Daniel Lee |
| 2025 | How to safely discard features based on aggregate SHAP values. Robi Bhattacharjee, Karolin Frohnapfel, Ulrike von Luxburg |
| 2025 | Identifiability and Estimation in High-Dimensional Nonparametric Latent Structure Models. Yichen Lyu, Pengkun Yang |
| 2025 | Improved Algorithms for Effective Resistance Computation on Graphs. Yichun Yang, Ronghua Li, Meihao Liao, Guoren Wang |
| 2025 | Improved Margin Generalization Bounds for Voting Classifiers. Mikael Møller Høgsgaard, Kasper Green Larsen |
| 2025 | Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. Jongha Jon Ryu, Jeongyeol Kwon, Benjamin Koppe, Kwang-Sung Jun |
| 2025 | Improved algorithms for learning quantum Hamiltonians, via flat polynomials. Shyam Narayanan |
| 2025 | Improved sample upper and lower bounds for trace estimation of quantum state powers. Kean Chen, Qisheng Wang |
| 2025 | Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime. Francesco Camilli, Daria Tieplova, Eleonora Bergamin, Jean Barbier |
| 2025 | Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback. Shinji Ito, Haipeng Luo, Taira Tsuchiya, Yue Wu |
| 2025 | Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration. Dylan J. Foster, Zakaria Mhammedi, Dhruv Rohatgi |
| 2025 | Learning Algorithms in the Limit. Hristo Papazov, Nicolas Flammarion |
| 2025 | Learning Augmented Graph k-Clustering. Chenglin Fan, Kijun Shin |
| 2025 | Learning Compositional Functions with Transformers from Easy-to-Hard Data. Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D. Lee, Denny Wu |
| 2025 | Learning Constant-Depth Circuits in Malicious Noise Models. Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2025 | Learning DNF through Generalized Fourier Representations. Mohsen Heidari, Roni Khardon |
| 2025 | Learning Intersections of Two Margin Halfspaces under Factorizable Distributions. Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos |
| 2025 | Learning Mixtures of Gaussians Using Diffusion Models. Khashayar Gatmiry, Jonathan A. Kelner, Holden Lee |
| 2025 | Learning Partitions with Optimal Query and Round Complexities. Hadley Black, Arya Mazumdar, Barna Saha |
| 2025 | Learning general Gaussian mixtures with efficient score matching. Sitan Chen, Vasilis Kontonis, Kulin Shah |
| 2025 | Learning shallow quantum circuits with many-qubit gates. Francisco Vasconcelos, Hsin-Yuan Huang |
| 2025 | Learning sparse generalized linear models with binary outcomes via iterative hard thresholding. Namiko Matsumoto, Arya Mazumdar |
| 2025 | Linear Bandits on Ellipsoids: Minimax Optimal Algorithms. Raymond Zhang, Hédi Hadiji, Richard Combes |
| 2025 | Linear Convergence of Diffusion Models Under the Manifold Hypothesis. Peter Potaptchik, Iskander Azangulov, George Deligiannidis |
| 2025 | Local Regularizers Are Not Transductive Learners. Sky Jafar, Julian Asilis, Shaddin Dughmi |
| 2025 | Logarithmic Width Suffices for Robust Memorization. Amitsour Egosi, Gilad Yehudai, Ohad Shamir |
| 2025 | Logarithmic regret of exploration in average reward Markov decision processes. Victor Boone, Bruno Gaujal |
| 2025 | Low coordinate degree algorithms II: Categorical signals and generalized stochastic block models. Dmitriy Kunisky |
| 2025 | Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions. Elisabetta Cornacchia, Dan Mikulincer, Elchanan Mossel |
| 2025 | Low-dimensional adaptation of diffusion models: Convergence in total variation (extended abstract). Jiadong Liang, Zhihan Huang, Yuxin Chen |
| 2025 | Low-rank fine-tuning lies between lazy training and feature learning. Arif Kerem Dayi, Sitan Chen |
| 2025 | Lower Bounds for Greedy Teaching Set Constructions. Spencer Compton, Chirag Pabbaraju, Nikita Zhivotovskiy |
| 2025 | Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes. Victor S. Portella, Nicholas J. A. Harvey |
| 2025 | Market Making without Regret. Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Luigi Foscari, Vinayak Pathak |
| 2025 | Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon. Margalit Glasgow, Denny Wu, Joan Bruna |
| 2025 | Metric Clustering and Graph Optimization Problems using Weak Comparison Oracles. Rahul Raychaudhury, Wen-Zhi Li, Syamantak Das, Sainyam Galhotra, Stavros Sintos |
| 2025 | Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams. Ainesh Bakshi, Vincent Cohen-Addad, Rajesh Jayaram, Sam Hopkins, Silvio Lattanzi |
| 2025 | Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality (Extended Abstract). Andre Wibisono |
| 2025 | Model predictive control is almost optimal for restless bandits. Nicolas Gast, Dheeraj Narasimha |
| 2025 | Multi-Pass Memory Lower Bounds for Learning Problems. Qian Li, Shuo Wang, Jiapeng Zhang |
| 2025 | Necessary and Sufficient Oracles: Toward a Computational Taxonomy for Reinforcement Learning. Dhruv Rohatgi, Dylan J. Foster |
| 2025 | New Lower Bounds for Non-Convex Stochastic Optimization through Divergence Decomposition. El Mehdi Saad, Wei-Cheng Lee, Francesco Orabona |
| 2025 | Noisy Group Testing in the Linear Regime: Exact Thresholds and Efficient. Lukas Hintze, Lena Krieg, Olga Scheftelowitsch, Haodong Zhu |
| 2025 | Non-Euclidean High-Order Smooth Convex Optimization Extended Abstract. Juan Pablo Contreras, Cristóbal Guzmán, David Martínez-Rubio |
| 2025 | Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely (Extended Abstract). Yan Dai, Moïse Blanchard, Patrick Jaillet |
| 2025 | Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity Extended Abstract. Dominik Stöger, Yizhe Zhu |
| 2025 | Of Dice and Games: A Theory of Generalized Boosting. Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen |
| 2025 | On the Convergence of Min-Max Langevin Dynamics and Algorithm. Yang Cai, Siddharth Mitra, Xiuyuan Wang, Andre Wibisono |
| 2025 | On the Hardness of Bandit Learning. Nataly Brukhim, Aldo Pacchiano, Miroslav Dudík, Robert E. Schapire |
| 2025 | On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy. Zeyu Jia, Alexander Rakhlin, Yury Polyanskiy |
| 2025 | On the query complexity of sampling from non-log-concave distributions (extended abstract). Yuchen He, Chihao Zhang |
| 2025 | Online Convex Optimization with a Separation Oracle. Zakaria Mhammedi |
| 2025 | Online Covariance Estimation in Nonsmooth Stochastic Approximation. Liwei Jiang, Abhishek Roy, Krishna Balasubramanian, Damek Davis, Dmitriy Drusvyatskiy, Sen Na |
| 2025 | Open Problem: Data Selection for Regression Tasks. Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff |
| 2025 | Open Problem: Fixed-Parameter Tractability of Zonotope Problems. Vincent Froese, Moritz Grillo, Christoph Hertrich, Martin Skutella |
| 2025 | Open Problem: Optimal Instance-Dependent Sample Complexity for finding Nash Equilibrium in Two Player Zero-Sum Matrix games. Arnab Maiti |
| 2025 | Open Problem: Regret Minimization in Heavy-Tailed Bandits with Unknown Distributional Parameters. Gianmarco Genalti, Alberto Maria Metelli |
| 2025 | Open Problem: Structure-Agnostic Minimax Risk for Partial Linear Model. Yihong Gu |
| 2025 | Optimal Differentially Private Sampling of Unbounded Gaussians. Valentio Iverson, Gautam Kamath, Argyris Mouzakis |
| 2025 | Optimal Graph Reconstruction by Counting Connected Components in Induced Subgraphs. Hadley Black, Arya Mazumdar, Barna Saha, Yinzhan Xu |
| 2025 | Optimal Online Bookmaking for Any Number of Outcomes. Hadar Tal, Oron Sabag |
| 2025 | Optimal Robust Estimation under Local and Global Corruptions: Stronger Adversary and Smaller Error. Thanasis Pittas, Ankit Pensia |
| 2025 | Optimal Scheduling of Dynamic Transport. Panos Tsimpos, Zhi Ren, Jakob Zech, Youssef Marzouk |
| 2025 | Optimistic Q-learning for average reward and episodic reinforcement learning extended abstract. Priyank Agrawal, Shipra Agrawal |
| 2025 | Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning. Antoine Moulin, Gergely Neu, Luca Viano |
| 2025 | Optimization, Isoperimetric Inequalities, and Sampling via Lyapunov Potentials. August Y. Chen, Karthik Sridharan |
| 2025 | Orthogonal Causal Calibration (Extended Abstract). Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu |
| 2025 | PREM: Privately Answering Statistical Queries with Relative Error. Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Sushant Sachdeva |
| 2025 | Partial and Exact Recovery of a Random Hypergraph from its Graph Projection. Guy Bresler, Chenghao Guo, Yury Polyanskiy, Andrew Yao |
| 2025 | Polynomial low degree hardness for Broadcasting on Trees (Extended Abstract). Han Huang, Elchanan Mossel |
| 2025 | Predicting quantum channels over general product distributions. Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li |
| 2025 | Private List Learnability vs. Online List Learnability. Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari |
| 2025 | Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity. Bo Li, Wei Wang, Peng Ye |
| 2025 | Proofs as Explanations: Short Certificates for Reliable Predictions. Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless |
| 2025 | Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization. Ruichen Jiang, Devyani Maladkar, Aryan Mokhtari |
| 2025 | Quantifying Overfitting along the Regularization Path for Two-Part-Code MDL in Supervised Classification. Xiaohan Zhu, Nathan Srebro |
| 2025 | Quantum State and Unitary Learning Implies Circuit Lower Bounds. Nai-Hui Chia, Daniel Liang, Fang Song |
| 2025 | Rate-Preserving Reductions for Blackwell Approachability. Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan |
| 2025 | Recovering Labels from Crowdsourced Data: an Optimal and Polynomial-Time Method. Emmanuel Pilliat |
| 2025 | Regret Bounds for Robust Online Decision Making. Alexander Appel, Vanessa Kosoy |
| 2025 | Regularized Dikin Walks for Sampling Truncated Logconcave Measures, Mixed Isoperimetry and Beyond Worst-Case Analysis. Minhui Jiang, Yuansi Chen |
| 2025 | Robust Algorithms for Recovering Planted r-Colorable Graphs. Anand Louis, Rameesh Paul, Prasad Raghavendra |
| 2025 | Robust random graph matching in Gaussian models via vector approximate message passing. Zhangsong Li |
| 2025 | Robustly Learning Monotone Generalized Linear Models via Data Augmentation. Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas |
| 2025 | Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses. Jiuyao Lu, Aaron Roth, Mirah Shi |
| 2025 | Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions. Zakaria Mhammedi |
| 2025 | Sharper Bounds for Chebyshev Moment Matching, with Applications. Cameron Musco, Christopher Musco, Lucas Rosenblatt, Apoorv Vikram Singh |
| 2025 | Simplifying Adversarially Robust PAC Learning With Tolerance. Hassan Ashtiani, Vinayak Pathak, Ruth Urner |
| 2025 | Solving Convex-Concave Problems with 풪(ε Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang |
| 2025 | Some easy optimization problems have the overlap-gap property. Shuangping Li, Tselil Schramm |
| 2025 | Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for Average-Reward RL. Matthew Zurek, Yudong Chen |
| 2025 | Sparsity-Based Interpolation of External, Internal and Swap Regret. Zhou Lu, Y. Jennifer Sun, Zhiyu Zhang |
| 2025 | Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery. Filip Kovacevic, Yihan Zhang, Marco Mondelli |
| 2025 | Spherical Dimension. Bogdan Chornomaz, Shay Moran, Tom Waknine |
| 2025 | Spike-and-Slab Posterior Sampling in High Dimensions. Symantak Kumar, Purnamrita Sarkar, Kevin Tian, Yusong Zhu |
| 2025 | Stability and List-Replicability for Agnostic Learners. Ari Blondal, Shan Gao, Hamed Hatami, Pooya Hatami |
| 2025 | Stochastic block models with many communities and the Kesten-Stigum bound - extended abstract. Byron Chin, Elchanan Mossel, Youngtak Sohn, Alexander S. Wein |
| 2025 | Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation (Extended Abstract). Jikai Jin, Vasilis Syrgkanis |
| 2025 | Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization. Yu-Han Wu, Pierre Marion, Gérard Biau, Claire Boyer |
| 2025 | Testing (Conditional) Mutual Information - Extended Abstract. Jan Seyfried, Sayantan Sen, Marco Tomamichel |
| 2025 | Testing Juntas and Junta Subclasses with Relative Error. Xi Chen, William Pires, Toniann Pitassi, Rocco A. Servedio |
| 2025 | Testing Thresholds and Spectral Properties of High-Dimensional Random Toroidal Graphs via Edgeworth-Style Expansions. Samuel Baguley, Andreas Göbel, Marcus Pappik, Leon Schiller |
| 2025 | The Adaptive Complexity of Finding a Stationary Point. Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama |
| 2025 | The Fundamental Limits of Recovering Planted Subgraphs (extended abstract). Daniel Lee, Francisco Pernice, Amit Rajaraman, Ilias Zadik |
| 2025 | The Oracle Complexity of Simplex-based Matrix Games: Linear Separability and Nash Equilibria. Guy Kornowski, Ohad Shamir |
| 2025 | The Planted Spanning Tree Problems: Exact Overlap Characterization via Local Weak Convergence Extended Abstract. Mehrdad Moharrami, Cristopher Moore, Jiaming Xu |
| 2025 | The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract). Akshay Krishnamurthy, Gene Li, Ayush Sekhari |
| 2025 | The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraints. Hadi Kazemi, Ankit Pensia, Varun S. Jog |
| 2025 | The Space Complexity of Learning-Unlearning Algorithms (extended abstract). Yeshwanth Cherapanamjeri, Sumegba Garg, Nived Rajaraman, Ayush Sekhari, Abhishek Shetty |
| 2025 | The Thirty Eighth Annual Conference on Learning Theory, 30-4 July 2025, Lyon, France. Nika Haghtalab, Ankur Moitra |
| 2025 | The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networks. Sholom Schechtman, Nicolas Schreuder |
| 2025 | The title of the paper. Max Simchowitz, Daniel Pfrommer, Ali Jadbabaie |
| 2025 | Thompson Sampling for Bandit Convex Optimisation. Alireza Bakhtiari, Tor Lattimore, Csaba Szepesvári |
| 2025 | Tight Bounds for Noisy Computation of High-Influence Functions, Connectivity, and Threshold. Yuzhou Gu, Xin Li, Yinzhan Xu |
| 2025 | Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (Extended Abstract). Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas |
| 2025 | Towards Fair Representation: Clustering and Consensus. Diptarka Chakraborty, Kushagra Chatterjee, Debarati Das, Tien Long Nguyen, Romina Nobahari |
| 2025 | Towards Fundamental Limits for Active Multi-distribution Learning. Chicheng Zhang, Yihan Zhou |
| 2025 | Trade-offs in Data Memorization via Strong Data Processing Inequalities. Vitaly Feldman, Guy Kornowski, Xin Lyu |
| 2025 | Truthfulness of Decision-Theoretic Calibration Measures. Mingda Qiao, Eric Zhao |
| 2025 | Universal Rates for Multiclass Learning with Bandit Feedback. Steve Hanneke, Amirreza Shaeiri, Qian Zhang |
| 2025 | Universal Rates of ERM for Agnostic Learning. Steve Hanneke, Mingyue Xu |
| 2025 | Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation. Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern |
| 2025 | What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness (Extended Abstract). Yang Cai, Alkis Kalavasis, Katerina Mamali, Anay Mehrotra, Manolis Zampetakis |