conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Learning Theory

5,334 papers

Papers per year

17
66
123
466
307
'05
'10
'15
'20
'25

Papers

Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits AISTATS 2021
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression AISTATS 2021
Tractable contextual bandits beyond realizability AISTATS 2021
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation AISTATS 2021
Q-learning with Logarithmic Regret AISTATS 2021
Competing AI: How does competition feedback affect machine learning? AISTATS 2021
Stability and Risk Bounds of Iterative Hard Thresholding AISTATS 2021
Reinforcement Learning in Parametric MDPs with Exponential Families AISTATS 2021
No-regret Algorithms for Multi-task Bayesian Optimization AISTATS 2021
Learning with Gradient Descent and Weakly Convex Losses AISTATS 2021
The Teaching Dimension of Kernel Perceptron AISTATS 2021
Stochastic Gradient Descent Meets Distribution Regression AISTATS 2021
Misspecification in Prediction Problems and Robustness via Improper Learning AISTATS 2021
Smooth Bandit Optimization: Generalization to Holder Space AISTATS 2021
List Learning with Attribute Noise AISTATS 2021
Implicit Regularization via Neural Feature Alignment AISTATS 2021
Aggregating Incomplete and Noisy Rankings AISTATS 2021
On the Linear Convergence of Policy Gradient Methods for Finite MDPs AISTATS 2021
Continuum-Armed Bandits: A Function Space Perspective AISTATS 2021
Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum AISTATS 2021
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions AISTATS 2021
Power of Hints for Online Learning with Movement Costs AISTATS 2021
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2021
Experimental Design for Regret Minimization in Linear Bandits AISTATS 2021
Prediction with Finitely many Errors Almost Surely AISTATS 2021