statistical learning theory

On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning ICLR 2020 Risk Bounds for Reservoir Computing JMLR 2020 Sharp Asymptotics and Optimal Performance for Inference in Binary Models AISTATS 2020 Towards Problem-dependent Optimal Learning Rates NeurIPS 2020 Toward the Fundamental Limits of Imitation Learning NeurIPS 2020 Learning from Weakly Dependent Data under Dobrushin’s Condition COLT 2019 Learning to Screen NeurIPS 2019 Localized Structured Prediction NeurIPS 2019 On Kernel Derivative Approximation with Random Fourier Features AISTATS 2019 Robust Learning from Untrusted Sources ICML 2019 Sharp Analysis of Learning with Discrete Losses AISTATS 2019 Statistical-Computational Tradeoff in Single Index Models NeurIPS 2019 Inference in Sparse Graphs with Pairwise Measurements and Side Information AISTATS 2018 Consistency Analysis for Binary Classification Revisited ICML 2017 Minimax Estimation of Kernel Mean Embeddings JMLR 2017 Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation AISTATS 2017 When Does Label Propagation Fail? A View from a Network Generative Model IJCAI 2017 Algorithmic Stability and Uniform Generalization NeurIPS 2015 Fast Rates in Statistical and Online Learning JMLR 2015 On the Pseudo-Dimension of Nearly Optimal Auctions NeurIPS 2015 Regularized M-estimators with Nonconvexity: Statistical and Algorithmic Theory for Local Optima JMLR 2015 Deep Fisher Kernels - End to End Learning of the Fisher Kernel GMM Parameters CVPR 2014 From Stochastic Mixability to Fast Rates NeurIPS 2014 Kernel Mean Estimation via Spectral Filtering NeurIPS 2014 Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis ICML 2014