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Learning Theory

5,334 papers

Papers per year

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123
466
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Papers

A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal and Parameter-free COLT 2019
Artificial Constraints and Hints for Unbounded Online Learning COLT 2019
Combining Online Learning Guarantees COLT 2019
Learning from Weakly Dependent Data under Dobrushin’s Condition COLT 2019
Space lower bounds for linear prediction in the streaming model COLT 2019
Reconstructing Trees from Traces COLT 2019
Computational Limitations in Robust Classification and Win-Win Results COLT 2019
Communication and Memory Efficient Testing of Discrete Distributions COLT 2019
Testing Identity of Multidimensional Histograms COLT 2019
Lower Bounds for Locally Private Estimation via Communication Complexity COLT 2019
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate COLT 2019
The Complexity of Making the Gradient Small in Stochastic Convex Optimization COLT 2019
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time COLT 2019
How Hard is Robust Mean Estimation? COLT 2019
Sample complexity of partition identification using multi-armed bandits COLT 2019
On Communication Complexity of Classification Problems COLT 2019
Contextual bandits with continuous actions: Smoothing, zooming, and adapting COLT 2019
Distribution-Dependent Analysis of Gibbs-ERM Principle COLT 2019
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring COLT 2019
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits COLT 2019
Uniform concentration and symmetrization for weak interactions COLT 2019
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem COLT 2019
VC Classes are Adversarially Robustly Learnable, but Only Improperly COLT 2019
A Theory of Selective Prediction COLT 2019
Classification with unknown class-conditional label noise on non-compact feature spaces COLT 2019