Learning Theory

Width Provably Matters in Optimization for Deep Linear Neural Networks ICML 2019 Worst-Case Regret Bounds for Exploration via Randomized Value Functions NeurIPS 2019 ℓ_1-regression with Heavy-tailed Distributions NeurIPS 2018 A Better Resource Allocation Algorithm with Semi-Bandit Feedback ALT 2018 Actively Avoiding Nonsense in Generative Models COLT 2018 Active Tolerant Testing COLT 2018 Adaptation to Easy Data in Prediction with Limited Advice NeurIPS 2018 Adaptive Dropout with Rademacher Complexity Regularization ICLR 2018 Adaptive Learning with Unknown Information Flows NeurIPS 2018 Adaptivity to Smoothness in X-armed bandits COLT 2018 Adding One Neuron Can Eliminate All Bad Local Minima NeurIPS 2018 A Direct Sum Result for the Information Complexity of Learning COLT 2018 Adversarially Robust Generalization Requires More Data NeurIPS 2018 Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution NeurIPS 2018 An Alternative View: When Does SGD Escape Local Minima? ICML 2018 Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model ICML 2018 An Order-based Algorithm for Learning Structure of Bayesian Networks PGM 2018 A no-regret generalization of hierarchical softmax to extreme multi-label classification NeurIPS 2018 A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks ICLR 2018 Approximation Guarantees for Adaptive Sampling ICML 2018 A Property Testing Framework for the Theoretical Expressivity of Graph Kernels IJCAI 2018 A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem NeurIPS 2018 Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures NeurIPS 2018 A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice NeurIPS 2018 Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization JMLR 2018