minimax optimality

Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification NeurIPS 2022 Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits NeurIPS 2022 Neural Estimation of Statistical Divergences JMLR 2022 Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis AISTATS 2022 Uniform deconvolution for Poisson Point Processes JMLR 2022 A Technical Note on Non-Stationary Parametric Bandits: Existing Mistakes and Preliminary Solutions ALT 2021 Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space NeurIPS 2021 How rotational invariance of common kernels prevents generalization in high dimensions ICML 2021 MOTS: Minimax Optimal Thompson Sampling ICML 2021 On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression AISTATS 2021 On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests JMLR 2021 Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations ICML 2021 Optimality of variational inference for stochasticblock model with missing links NeurIPS 2021 Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality JMLR 2020 Agnostic Estimation for Phase Retrieval JMLR 2020 Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model NeurIPS 2020 Estimation Rates for Sparse Linear Cyclic Causal Models UAI 2020 Minimax Nonparametric Parallelism Test JMLR 2020 Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal COLT 2020 On Minimax Optimality of GANs for Robust Mean Estimation AISTATS 2020 Optimal Bipartite Network Clustering JMLR 2020 Randomized tests for high-dimensional regression: A more efficient and powerful solution NeurIPS 2020 Robust Gradient-Based Markov Subsampling AAAI 2020 Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons AISTATS 2020 Covariate-Powered Empirical Bayes Estimation NeurIPS 2019