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Statistical Learning

4076 directly classified papers

Papers per year

Papers

Integrating Prior Knowledge in Contrastive Learning with Kernel ICML 2023
Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group AISTATS 2023
A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds JMLR 2023
An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks JMLR 2023
“Plus/minus the learning rate”: Easy and Scalable Statistical Inference with SGD AISTATS 2023
Differentially Private Hypothesis Testing for Linear Regression JMLR 2023
Distribution Free Prediction Sets for Node Classification ICML 2023
Mixed-Effect Thompson Sampling AISTATS 2023
Semantic Similarity Covariance Matrix Shrinkage EMNLP 2023
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity AISTATS 2023
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds JMLR 2023
Optimal and Private Learning from Human Response Data AISTATS 2023
On the Capacity Limits of Privileged ERM AISTATS 2023
Instance-Dependent Generalization Bounds via Optimal Transport JMLR 2023
Semi-Verified PAC Learning from the Crowd AISTATS 2023
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models JMLR 2023
Is interpolation benign for random forest regression? AISTATS 2023
A Case of Exponential Convergence Rates for SVM AISTATS 2023
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark EMNLP 2023
Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare AISTATS 2023
A Statistical Perspective on Retrieval-Based Models ICML 2023
Proper Losses for Discrete Generative Models ICML 2023
Bayesian Variable Selection in a Million Dimensions AISTATS 2023
Random Forests for Change Point Detection JMLR 2023
Re-Examining Summarization Evaluation across Multiple Quality Criteria EMNLP 2023