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COLT 2024

A*

Annual Conference on Learning Theory

Official site ↗
170
accepted papers
156
authors
June 30 - July 3, 2024
dates
Canada
location

170 / 170 papers

  1. 1
    (ε, u)-Adaptive Regret Minimization in Heavy-Tailed Bandits
    Gianmarco Genalti
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  2. 2
    A faster and simpler algorithm for learning shallow networks
    Sitan Chen
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  3. 3
    A Non-Adaptive Algorithm for the Quantitative Group Testing Problem
    Mahdi Soleymani
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  4. 4
    A non-backtracking method for long matrix and tensor completion
    Ludovic Stephan
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  5. 5
    A Theory of Interpretable Approximations
    Marco Bressan
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  6. 6
    A Unified Characterization of Private Learnability via Graph Theory
    Noga Alon
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  7. 7
    Accelerated Parameter-Free Stochastic Optimization
    Itai Kreisler
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  8. 8
    Active Learning with Simple Questions
    Vasilis Kontonis
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  9. 9
    Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds
    Shinji Ito
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  10. 10
    Adversarial Online Learning with Temporal Feedback Graphs
    Khashayar Gatmiry
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  11. 11
    Adversarially-Robust Inference on Trees via Belief Propagation
    Samuel B. Hopkins
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  12. 12
    Agnostic Active Learning of Single Index Models with Linear Sample Complexity
    Aarshvi Gajjar
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  13. 13
    Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
    Yiheng Jiang
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  14. 14
    An information-theoretic lower bound in time-uniform estimation
    John C. Duchi
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  15. 15
    Apple Tasting: Combinatorial Dimensions and Minimax Rates
    Vinod Raman
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  16. 16
    Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
    Brendan Lucier
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  17. 17
    Better-than-KL PAC-Bayes Bounds
    Ilja Kuzborskij
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  18. 18
    Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation
    Shivam Gupta
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  19. 19
    Black-Box k-to-1-PCA Reductions: Theory and Applications
    Arun Jambulapati
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  20. 20
    Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization
    Jiancong Xiao
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  21. 21
    Choosing the p in Lp Loss: Adaptive Rates for Symmetric Mean Estimation
    Yu-Chun Kao
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  22. 22
    Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization
    Arun Jambulapati
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  23. 23
    Community detection in the hypergraph stochastic block model and reconstruction on hypertrees
    Yuzhou Gu
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  24. 24
    Computation-information gap in high-dimensional clustering
    Bertrand Even
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  25. 25
    Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression
    Rares-Darius Buhai
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  26. 26
    Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract)
    Alex Damian
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  27. 27
    Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo (Extended Abstract)
    Amedeo Roberto Esposito
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  28. 28
    Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
    Jianhao Ma
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  29. 29
    Convergence of Kinetic Langevin Monte Carlo on Lie groups
    Lingkai Kong
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  30. 30
    Correlated Binomial Process
    Moïse Blanchard
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  31. 31
    Counting Stars is Constant-Degree Optimal For Detecting Any Planted Subgraph: Extended Abstract
    Xifan Yu
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  32. 32
    Depth Separation in Norm-Bounded Infinite-Width Neural Networks
    Suzanna Parkinson
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  33. 33
    Detection of L∞ Geometry in Random Geometric Graphs: Suboptimality of Triangles and Cluster Expansion
    Kiril Bangachev
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  34. 34
    Dimension-free Structured Covariance Estimation
    Nikita Puchkin
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  35. 35
    Dual VC Dimension Obstructs Sample Compression by Embeddings
    Zachary Chase
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  36. 36
    Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation Extended Abstract
    Ziao Wang
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  37. 37
    Efficient Algorithms for Learning Monophonic Halfspaces in Graphs
    Marco Bressan
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  38. 38
    Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials
    Ilias Diakonikolas
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  39. 39
    Errors are Robustly Tamed in Cumulative Knowledge Processes
    Anna M. Brandenberger
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  40. 40
    Exact Mean Square Linear Stability Analysis for SGD
    Rotem Mulayoff
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  41. 41
    Fast parallel sampling under isoperimetry
    Nima Anari
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  42. 42
    Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm
    Vishwak Srinivasan
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  43. 43
    Fast two-time-scale stochastic gradient method with applications in reinforcement learning
    Sihan Zeng
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  44. 44
    Fast, blind, and accurate: Tuning-free sparse regression with global linear convergence
    Claudio Mayrink Verdun
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  45. 45
    Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo
    Xunpeng Huang
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  46. 46
    Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract)
    Yujia Jin
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  47. 47
    Finding Super-spreaders in Network Cascades
    Elchanan Mossel
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  48. 48
    Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions
    Yilong Qin
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  49. 49
    Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
    Jongyeong Lee
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  50. 50
    Fundamental Limits of Non-Linear Low-Rank Matrix Estimation
    Pierre Mergny
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