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COLT 2026

A*

Annual Conference on Learning Theory

Official site ↗
197
accepted papers
186
authors
June 29 - July 3, 2026
dates
California
location

197 / 197 papers

  1. 1
    A Characterization of List Language Identification in the Limit
    Moses Charikar
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  2. 2
    A Complexity Measure for Active Learning in Multi-group Mean Estimation
    Abdellah Aznag
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  3. 3
    A Depth Hierarchy for Computing the Maximum in ReLU Networks via Extremal Graph Theory
    Itay Safran
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  4. 4
    A Distribution Testing Approach to Clustering Distributions
    Gunjan Kumar
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  5. 5
    A Perfectly Truthful Calibration Measure
    Jason D. Hartline
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  6. 6
    A Quasi-Polynomial Time Mean Estimator Under Mean-Shift Contamination with Unknown Covariance
    Ilias Diakonikolas
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  7. 7
    A simple, optimal and efficient algorithm for online exp-concave optimization
    Yi-Han Wang
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  8. 8
    A Single Stepsize Suffices for Unprojected Linear TD(0): Simultaneous Robust and Fast Rates via Polyak-Ruppert Averaging
    Wei-Cheng Lee
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  9. 9
    A Tight Lower Bound for Non-stochastic Multi-armed Bandits with Expert Advice
    Zachary Chase
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  10. 10
    A Unified Lower Bound on the Noisy Query Complexity of Boolean Functions
    Yuzhou Gu
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  11. 11
    Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time
    Xiuyuan Wang
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  12. 12
    Active Learning on Adversarially Corrupted Graphs
    Marco Bressan
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  13. 13
    Actively Learning Halfspaces without Synthetic Data
    Hadley Black
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  14. 14
    Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader
    Jongyeong Lee
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  15. 15
    Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization
    Ruichen Jiang
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  16. 16
    Adaptive Weighted Averaging
    Aditya Bhaskara
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  17. 17
    Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
    Shinji Ito
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  18. 18
    Algorithmic Thinking Theory
    MohammadHossein Bateni
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  19. 19
    Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion
    Xunpeng Huang
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  20. 20
    Almost sure null bankruptcy of testing-by-betting strategies
    Hongjian Wang
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  21. 21
    Ambiguous Online Learning
    Vanessa Kosoy
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  22. 22
    An Empirical Bayes Perspective on Heteroskedastic Mean Estimation
    Yanjun Han
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  23. 23
    An Exponential Lower Bound for Spectral Density Estimation on Unweighted Graphs
    Pan Peng
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  24. 24
    Avoiding exp(k*) Scaling for Thompson Sampling in Combinatorial Semi-Bandits: From Multiple Seeds to a Single Seed
    Tianyuan Jin
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  25. 25
    Blackwell Approachability and Gradient Equilibrium are Equivalent
    Brian W. Lee
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  26. 26
    Boosting with List-Decodable Codes
    Addison Prairie
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  27. 27
    Calibeating Made Simple
    Yurong Chen
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  28. 28
    Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases (Extended Abstract)
    Alkis Kalavasis
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  29. 29
    Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness
    Moïse Blanchard
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  30. 30
    Clipping the Price of Adaptivity at the Tail
    Itai Kreisler
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  31. 31
    Cloning is as Hard as Learning for Stabilizer States
    Nikhil Bansal
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  32. 32
    Compact Geometric Representations of Hierarchies
    Prashant Gokhale
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  33. 33
    Computing Lewis weights to high precision using local relative smoothness
    Sander Gribling
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  34. 34
    Continuous time policy evaluation is easier with noisy dynamics
    Samuel Robertson
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  35. 35
    Convergence of Continual Learning in Homogeneous Deep Networks
    Matan Schliserman
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  36. 36
    CONVERGENCE RATES FOR DISTRIBUTION MATCHING WITH SLICED OPTIMAL TRANSPORT
    Gauthier Thurin
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  37. 37
    Data Augmentation: A Fourier Analysis Perspective
    Behrooz Tahmasebi
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  38. 38
    DDPM Score Matching and Distribution Learning (Extended Abstract)
    Sinho Chewi
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  39. 39
    Deep Q-Learning on Hölder Spaces
    Qian Qi
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  40. 40
    Defensive Generation
    Gabriele Farina
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  41. 41
    Density estimation for Hellinger via minimum-distance estimators: mixtures of Gaussians, log-concave, and more
    Spencer Compton
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  42. 42
    Differentially Private Language Generation and Identification in the Limit (Extended Abstract)
    Anay Mehrotra
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  43. 43
    Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds
    Ziao Wang
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  44. 44
    Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures
    Prashanti Anderson
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  45. 45
    Distribution-Free Sequential Prediction with Abstentions
    Jialin Yu
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  46. 46
    Efficient Learning and Symmetry Discovery under Exact Invariances
    Ashkan Soleymani
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  47. 47
    Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
    Daniil Dmitriev
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  48. 48
    Efficient Swap Multicalibration of Elicitable Properties
    Lunjia Hu
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  49. 49
    Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data
    Collin Cranston
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  50. 50
    Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
    Shyamal Patel
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