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Statistical Learning

4076 directly classified papers

Papers per year

Papers

Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering JMLR 2017
Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models JMLR 2017
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles JMLR 2017
Computational Limits of A Distributed Algorithm for Smoothing Spline JMLR 2017
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging ICML 2017
Confidence Sets with Expected Sizes for Multiclass Classification JMLR 2017
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption ICML 2017
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery ICML 2017
Distributed Learning with Regularized Least Squares JMLR 2017
A Universal Analysis of Large-Scale Regularized Least Squares Solutions NIPS 2017
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods NIPS 2017
Matrix Norm Estimation from a Few Entries NIPS 2017
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation ICML 2017
Minimax Estimation of Kernel Mean Embeddings JMLR 2017
Learning Overcomplete HMMs NIPS 2017
Tests of Mutual or Serial Independence of Random Vectors with Applications JMLR 2017
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems NIPS 2017
Achieving Optimal Misclassification Proportion in Stochastic Block Models JMLR 2017
Density Estimation in Infinite Dimensional Exponential Families JMLR 2017
Fisher Consistency for Prior Probability Shift JMLR 2017
Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences JMLR 2017
Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning JMLR 2017
Learning a Ground Truth Ranking Using Noisy Approval Votes IJCAI 2017
On the Complexity of Learning from Label Proportions IJCAI 2017
Identifying a Minimal Class of Models for High--dimensional Data JMLR 2017