Statistical Learning

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables JMLR 2020 Learning Mixed Latent Tree Models JMLR 2020 Learning Mixtures of Graphs from Epidemic Cascades ICML 2020 Learning Near Optimal Policies with Low Inherent Bellman Error ICML 2020 Learning nonlinear dynamical systems from a single trajectory L4DC 2020 Learning Opinions in Social Networks ICML 2020 Learning Polynomials in Few Relevant Dimensions COLT 2020 Learning Robust Decision Policies from Observational Data NeurIPS 2020 Learning the Linear Quadratic Regulator from Nonlinear Observations NeurIPS 2020 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling ICML 2020 Learning with Labeled and Unlabeled Multi-Step Transition Data for Recovering Markov Chain from Incomplete Transition Data IJCAI 2020 Learning with Multiple Complementary Labels ICML 2020 Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms NeurIPS 2020 Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data AISTATS 2020 Less Is Better: Unweighted Data Subsampling via Influence Function AAAI 2020 Lexicalization of Probabilistic Linear Context-free Rewriting Systems ACL 2020 Linear predictor on linearly-generated data with missing values: non consistency and solutions AISTATS 2020 List-Decodable Mean Estimation via Iterative Multi-Filtering NeurIPS 2020 List Decodable Subspace Recovery COLT 2020 Locally Private Hypothesis Selection COLT 2020 Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms NeurIPS 2020 Local Regularizer Improves Generalization AAAI 2020 Logistic Regression for Massive Data with Rare Events ICML 2020 Logistic regression with peer-group effects via inference in higher-order Ising models AISTATS 2020 Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information JMLR 2020