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Statistical Learning

4076 directly classified papers

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Papers

Conic Scan-and-Cover algorithms for nonparametric topic modeling NIPS 2017
Hierarchical Methods of Moments NIPS 2017
Early stopping for kernel boosting algorithms: A general analysis with localized complexities NIPS 2017
Discovering Potential Correlations via Hypercontractivity NIPS 2017
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features NIPS 2017
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting AISTATS 2017
Fast rates with high probability in exp-concave statistical learning AISTATS 2017
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent AISTATS 2017
Attributing Hacks AISTATS 2017
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation AISTATS 2017
Learning Theory for Conditional Risk Minimization AISTATS 2017
Minimax Density Estimation for Growing Dimension AISTATS 2017
Consistent and Efficient Nonparametric Different-Feature Selection AISTATS 2017
Minimax Gaussian Classification & Clustering AISTATS 2017
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging EMNLP 2017
Statistical Cost Sharing NIPS 2017
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions NIPS 2017
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma NIPS 2017
Unbiased estimates for linear regression via volume sampling NIPS 2017
Variance-based Regularization with Convex Objectives NIPS 2017
Generalization Properties of Learning with Random Features NIPS 2017
A Minimax Optimal Algorithm for Crowdsourcing NIPS 2017
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent NIPS 2017
On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks JMLR 2017
Communication-efficient Sparse Regression JMLR 2017