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COLT 2025

A*

Annual Conference on Learning Theory

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183
accepted papers
165
authors
June 30 - July 4, 2025
dates
France
location

183 / 183 papers

  1. 1
    "All-Something-Nothing" Phase Transitions in Planted k-Factor Recovery (Extended Abstract)
    Julia Gaudio
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  2. 2
    A Distributional-Lifting Theorem for PAC Learning
    Guy Blanc
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  3. 3
    A Fine-grained Characterization of PAC Learnability
    Marco Bressan
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  4. 4
    A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis
    Akash Kumar
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  5. 5
    A Polynomial-time Algorithm for Online Sparse Linear Regression with Improved Regret Bound under Weaker Conditions
    Junfan Li
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  6. 6
    A Proof of The Changepoint Detection Threshold Conjecture in Preferential Attachment Models
    Hang Du
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  7. 7
    A Theory of Learning with Autoregressive Chain of Thought
    Nirmit Joshi
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  8. 8
    Accelerating Proximal Gradient Descent via Silver Stepsizes
    Jinho Bok
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  9. 9
    Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals
    Anxin Guo
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  10. 10
    Algorithms for Sparse LPN and LSPN Against Low-noise (extended abstract)
    Xue Chen
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  11. 11
    Alternating Regret for Online Convex Optimization
    Soumita Hait
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  12. 12
    An uncertainty principle for Linear Recurrent Neural Networks
    Alexandre François
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  13. 13
    Anytime Acceleration of Gradient Descent
    Zihan Zhang
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  14. 14
    Approximating the total variation distance between spin systems
    Weiming Feng
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  15. 15
    Are all models wrong? Fundamental limits in distribution-free empirical model falsification
    Manuel M. Müller
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  16. 16
    Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy
    Kaito Fujii
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  17. 17
    Better Private Distribution Testing by Leveraging Unverified Auxiliary Data
    Maryam Aliakbarpour
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  18. 18
    Beyond propagation of chaos: A stochastic algorithm for mean field optimization
    Chandan Tankala
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  19. 19
    Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
    Omar Montasser
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  20. 20
    Black-Box Reductions for Decentralized Online Convex Optimization in Changing Environments
    Yuanyu Wan
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  21. 21
    Blackwell's Approachability with Approximation Algorithms
    Dan Garber
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  22. 22
    Can a calibration metric be both testable and actionable?
    Raphael Rossellini
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  23. 23
    Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs
    Alexander Ryabchenko
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  24. 24
    Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of Φ-Mutual Information (Extended Abstract)
    Jiaming Liang
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  25. 25
    Community detection with the Bethe-Hessian
    Ludovic Stephan
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  26. 26
    Complexity of Injectivity and Verification of ReLU Neural Networks (Extended Abstract)
    Vincent Froese
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  27. 27
    Compression Barriers in Autoregressive Transformers
    Themistoklis Haris
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  28. 28
    Computable learning of natural hypothesis classes
    Syed Akbari
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  29. 29
    Computational Equivalence of Spiked Covariance and Spiked Wigner Models via Gram-Schmidt Perturbation
    Guy Bresler
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  30. 30
    Computational Intractability of Strategizing against Online Learners
    Angelos Assos
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  31. 31
    Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification (extended abstract)
    Dhruv Rohatgi
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  32. 32
    Computing High-dimensional Confidence Sets for Arbitrary Distributions
    Chao Gao
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  33. 33
    Computing Optimal Regularizers for Online Linear Optimization
    Khashayar Gatmiry
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  34. 34
    Conference on Learning Theory 2025: Preface
    Nika Haghtalab
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  35. 35
    Corrupted Learning Dynamics in Games
    Taira Tsuchiya
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  36. 36
    Data Selection for ERMs
    Steve Hanneke
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  37. 37
    Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching
    Quan M. Nguyen
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  38. 38
    Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
    Fan Chen
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  39. 39
    Decision Making in Hybrid Environments: A Model Aggregation Approach
    Haolin Liu
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  40. 40
    Depth Separations in Neural Networks: Separating the Dimension from the Accuracy
    Itay Safran
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  41. 41
    Detecting Arbitrary Planted Subgraphs in Random Graphs
    Dor Elimelech
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  42. 42
    Deterministic Apple Tasting
    Zachary Chase
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  43. 43
    Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts
    Pan Peng
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  44. 44
    DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory
    Jerry Chee
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  45. 45
    Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
    Arnab Maiti
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  46. 46
    Efficiently learning and sampling multimodal distributions with data-based initialization
    Frederic Koehler
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  47. 47
    Estimating stationary mass, frequency by frequency
    Milind Nakul
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  48. 48
    Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{SO}(d)$
    Amit Daniely
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  49. 49
    Experimental Design for Semiparametric Bandits
    Seok-Jin Kim
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  50. 50
    Exploring Facets of Language Generation in the Limit
    Moses Charikar
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