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Statistical Learning

4076 directly classified papers

Papers per year

Papers

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences NIPS 2016
Clustering with Bregman Divergences: an Asymptotic Analysis NIPS 2016
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics JMLR 2016
A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces JMLR 2016
Loss Minimization and Parameter Estimation with Heavy Tails JMLR 2016
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability NIPS 2016
Large-scale probabilistic predictors with and without guarantees of validity NIPS 2015
On Estimating L_2^2 Divergence AISTATS 2015
Sparsistency of \ell_1-Regularized M-Estimators AISTATS 2015
Variance Reduction via Antithetic Markov Chains AISTATS 2015
Optimal Linear Estimation under Unknown Nonlinear Transform NIPS 2015
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso NIPS 2015
Closed-form Estimators for High-dimensional Generalized Linear Models NIPS 2015
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality NIPS 2015
A Unified Framework for Outlier-Robust PCA-like Algorithms ICML 2015
The Ladder: A Reliable Leaderboard for Machine Learning Competitions ICML 2015
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces NIPS 2015
Fast 2D Border Ownership Assignment CVPR 2015
Strong Consistency of the Prototype Based Clustering in Probabilistic Space JMLR 2015
Competitive Distribution Estimation: Why is Good-Turing Good NIPS 2015
Structured Estimation with Atomic Norms: General Bounds and Applications NIPS 2015
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs NIPS 2015
Regularization Path of Cross-Validation Error Lower Bounds NIPS 2015
Sum-of-Squares Lower Bounds for Sparse PCA NIPS 2015
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm JMLR 2015