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Learning Theory

5312 directly classified papers

Papers per year

Papers

Multi-scale exploration of convex functions and bandit convex optimization COLT 2016
PAC Reinforcement Learning with Rich Observations NIPS 2016
A Consistent Regularization Approach for Structured Prediction NIPS 2016
Boosting with Abstention NIPS 2016
PAC-Bayesian Theory Meets Bayesian Inference NIPS 2016
Online Learning with Noisy Side Observations AISTATS 2016
Optimal Architectures in a Solvable Model of Deep Networks NIPS 2016
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers NIPS 2016
Learning Kernels with Random Features NIPS 2016
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture COLT 2016
Parameter-Free Convex Learning through Coin Betting AUTOML 2016
Robustness of classifiers: from adversarial to random noise NIPS 2016
Quantum Perceptron Models NIPS 2016
Only H is left: Near-tight Episodic PAC RL NIPS 2016
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning NIPS 2016
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning NIPS 2016
Refined Error Bounds for Several Learning Algorithms JMLR 2016
Sample Complexity of Automated Mechanism Design NIPS 2016
Improving PAC Exploration Using the Median Of Means NIPS 2016
Online Sparse Linear Regression COLT 2016
Interactive Algorithms: from Pool to Stream COLT 2016
Average-case hardness of RIP certification NIPS 2016
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning NIPS 2016
Convex Calibration Dimension for Multiclass Loss Matrices JMLR 2016
Active Nearest-Neighbor Learning in Metric Spaces NIPS 2016