minimax optimality

Necessary and Sufficient Geometries for Gradient Methods NeurIPS 2019 On Asymptotic and Finite-Time Optimality of Bayesian Predictors JMLR 2019 Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019 Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization ICLR 2019 Unified Sample-Optimal Property Estimation in Near-Linear Time NeurIPS 2019 Early Stopping for Nonparametric Testing NeurIPS 2018 Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets ALT 2018 Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems AISTATS 2018 Refining the Confidence Level for Optimistic Bandit Strategies JMLR 2018 Optimal Sample Complexity of M-wise Data for Top-K Ranking NeurIPS 2017 The power of absolute discounting: all-dimensional distribution estimation NeurIPS 2017 Universality of Bayesian mixture predictors ALT 2017 Efficient Nonparametric Smoothness Estimation NeurIPS 2016 Gaussian process nonparametric tensor estimator and its minimax optimality ICML 2016 More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning NeurIPS 2016 Optimal Learning Rates for Localized SVMs JMLR 2016 Competitive Distribution Estimation: Why is Good-Turing Good NeurIPS 2015 Convergence rate of Bayesian tensor estimator and its minimax optimality ICML 2015 Optimal Linear Estimation under Unknown Nonlinear Transform NeurIPS 2015 Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time NeurIPS 2014 Estimating the Unseen: Improved Estimators for Entropy and other Properties NeurIPS 2013 Minimax Optimal Algorithms for Unconstrained Linear Optimization NeurIPS 2013 Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery ICML 2013 CAKE: Convex Adaptive Kernel Density Estimation AISTATS 2011 Online Optimization in X-Armed Bandits NeurIPS 2008