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Learning Theory

5312 directly classified papers

Papers per year

Papers

Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities NIPS 2016
Optimistic Bandit Convex Optimization NIPS 2016
Memory, Communication, and Statistical Queries COLT 2016
Global Optimality of Local Search for Low Rank Matrix Recovery NIPS 2016
Mistake Bounds for Binary Matrix Completion NIPS 2016
Open Problem: Second order regret bounds based on scaling time COLT 2016
Loss Bounds and Time Complexity for Speed Priors AISTATS 2016
Instance-dependent Regret Bounds for Dueling Bandits COLT 2016
Complexity Theoretic Limitations on Learning DNF’s COLT 2016
Learning in Games: Robustness of Fast Convergence NIPS 2016
PAC-Bayesian Bounds based on the Rényi Divergence AISTATS 2016
Controlling Bias in Adaptive Data Analysis Using Information Theory AISTATS 2016
Adaptive Learning with Robust Generalization Guarantees COLT 2016
A Closer Look at Adaptive Regret JMLR 2016
Delay and Cooperation in Nonstochastic Bandits COLT 2016
Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well ICML 2016
An optimal algorithm for the Thresholding Bandit Problem ICML 2016
Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables COLT 2016
Adaptive Concentration Inequalities for Sequential Decision Problems NIPS 2016
Learning Simple Auctions COLT 2016
Learning Combinatorial Functions from Pairwise Comparisons COLT 2016
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time COLT 2016
Actively Learning Hemimetrics with Applications to Eliciting User Preferences ICML 2016
The Effect of Combination Functions on the Complexity of Relational Bayesian Networks PGM 2016
Representation and Learning of Temporal Relations COLING 2016