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Learning Theory

5312 directly classified papers

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Papers

Reinforcement Learning in Robust Markov Decision Processes NIPS 2013
More data speeds up training time in learning halfspaces over sparse vectors NIPS 2013
The Pareto Regret Frontier NIPS 2013
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion NIPS 2013
Estimating the Unseen: Improved Estimators for Entropy and other Properties NIPS 2013
Eluder Dimension and the Sample Complexity of Optimistic Exploration NIPS 2013
Prior-free and prior-dependent regret bounds for Thompson Sampling NIPS 2013
Learning with Noisy Labels NIPS 2013
Adaptivity to Local Smoothness and Dimension in Kernel Regression NIPS 2013
Predictive PAC Learning and Process Decompositions NIPS 2013
Estimation, Optimization, and Parallelism when Data is Sparse NIPS 2013
Tracking Time-varying Graphical Structure NIPS 2013
A Gang of Bandits NIPS 2013
On model selection consistency of penalized M-estimators: a geometric theory NIPS 2013
Open Problem: Adversarial Multiarmed Bandits with Limited Advice COLT 2013
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization COLT 2013
Excess risk bounds for multitask learning with trace norm regularization COLT 2013
Honest Compressions and Their Application to Compression Schemes COLT 2013
General Oracle Inequalities for Gibbs Posterior with Application to Ranking COLT 2013
Exact Learning of Bounded Tree-width Bayesian Networks AISTATS 2013
Distribution-Free Distribution Regression AISTATS 2013
Localization and Adaptation in Online Learning AISTATS 2013
On the Asymptotic Optimality of Maximum Margin Bayesian Networks AISTATS 2013
Competing with an Infinite Set of Models in Reinforcement Learning AISTATS 2013
Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality JMLR 2013