Learning Theory

Adaptive Concentration Inequalities for Sequential Decision Problems NeurIPS 2016 Adaptive Learning with Robust Generalization Guarantees COLT 2016 A General Framework for Consistency of Principal Component Analysis JMLR 2016 Algorithms and matching lower bounds for approximately-convex optimization NeurIPS 2016 An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning JMLR 2016 An optimal algorithm for the Thresholding Bandit Problem ICML 2016 A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries JMLR 2016 Anytime Exploration for Multi-armed Bandits using Confidence Information ICML 2016 Anytime optimal algorithms in stochastic multi-armed bandits ICML 2016 A ranking approach to global optimization ICML 2016 Average-case hardness of RIP certification NeurIPS 2016 Barron and Cover’s Theory in Supervised Learning and its Application to Lasso ICML 2016 Blind Attacks on Machine Learners NeurIPS 2016 Boosting with Abstention NeurIPS 2016 Causal Bandits: Learning Good Interventions via Causal Inference NeurIPS 2016 Combinatorial semi-bandit with known covariance NeurIPS 2016 Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning NeurIPS 2016 Complexity Theoretic Limitations on Learning DNF’s COLT 2016 Computational and Statistical Tradeoffs in Learning to Rank NeurIPS 2016 Consistency of Cheeger and Ratio Graph Cuts JMLR 2016 Controlling Bias in Adaptive Data Analysis Using Information Theory AISTATS 2016 Convex Calibration Dimension for Multiclass Loss Matrices JMLR 2016 Data-driven Rank Breaking for Efficient Rank Aggregation ICML 2016 Deep Learning without Poor Local Minima NeurIPS 2016 Delay and Cooperation in Nonstochastic Bandits COLT 2016