Co-occurring keywords · papers mentioning both
Papers
Open Problem: Do Good Algorithms Necessarily Query Bad Points?
COLT 2019
ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
ICLR 2019
Sharp Theoretical Analysis for Nonparametric Testing under Random Projection
COLT 2019
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain
AISTATS 2019
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance
COLT 2018
Minimax Rates and Efficient Algorithms for Noisy Sorting
ALT 2018
Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning
AISTATS 2018
Nonparametric Density Estimation under Adversarial Losses
NeurIPS 2018
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
NeurIPS 2018
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
NeurIPS 2018
Bayesian Dyadic Trees and Histograms for Regression
NeurIPS 2017
Global optimization of Lipschitz functions
ICML 2017
Minimax Density Estimation for Growing Dimension
AISTATS 2017
Minimax Gaussian Classification & Clustering
AISTATS 2017
Universal consistency and minimax rates for online Mondrian Forests
NeurIPS 2017
Efficient Nonparametric Smoothness Estimation
NeurIPS 2016
Minimax Rates in Permutation Estimation for Feature Matching
JMLR 2016
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
NeurIPS 2016
Minimax rates for memory-bounded sparse linear regression
COLT 2015
A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates
COLT 2014
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition
NeurIPS 2014
On Communication Cost of Distributed Statistical Estimation and Dimensionality
NeurIPS 2014
Optimal rates for k-NN density and mode estimation
NeurIPS 2014
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
NeurIPS 2014
Estimation, Optimization, and Parallelism when Data is Sparse
NeurIPS 2013