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Learning Theory

5,312 papers

Papers per year

Papers

A new regret analysis for Adam-type algorithms ICML 2020
Black-box Certification and Learning under Adversarial Perturbations ICML 2020
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks ICML 2020
Model-Based Reinforcement Learning with Value-Targeted Regression ICML 2020
Frequency Bias in Neural Networks for Input of Non-Uniform Density ICML 2020
Interference and Generalization in Temporal Difference Learning ICML 2020
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures ICML 2020
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance ICML 2020
Provable guarantees for decision tree induction: the agnostic setting ICML 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks ICML 2020
Tightening Exploration in Upper Confidence Reinforcement Learning ICML 2020
Online Pricing with Offline Data: Phase Transition and Inverse Square Law ICML 2020
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models ICML 2020
Provably Efficient Exploration in Policy Optimization ICML 2020
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently ICML 2020
Meta-learning with Stochastic Linear Bandits ICML 2020
Better depth-width trade-offs for neural networks through the lens of dynamical systems ICML 2020
Combinatorial Pure Exploration for Dueling Bandit ICML 2020
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models ICML 2020
k-means++: few more steps yield constant approximation ICML 2020
Teaching with Limited Information on the Learner’s Behaviour ICML 2020
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime ICML 2020
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors ICML 2020
Gamification of Pure Exploration for Linear Bandits ICML 2020
Structure Adaptive Algorithms for Stochastic Bandits ICML 2020