Statistical Learning

Information theoretic limits of learning a sparse rule NeurIPS 2020 Information Theoretic Optimal Learning of Gaussian Graphical Models COLT 2020 Information-Theoretic Understanding of Population Risk Improvement with Model Compression AAAI 2020 In search of robust measures of generalization NeurIPS 2020 Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model EMNLP 2020 Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? ICLR 2020 Is Local SGD Better than Minibatch SGD? ICML 2020 Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? NeurIPS 2020 Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing EMNLP 2020 Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing ICML 2020 Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds ACL 2020 Kernel Alignment Risk Estimator: Risk Prediction from Training Data NeurIPS 2020 Kernel Conditional Density Operators AISTATS 2020 Kernel Conditional Moment Test via Maximum Moment Restriction UAI 2020 Learning a Single Neuron with Gradient Methods COLT 2020 Learning Bounds for Risk-sensitive Learning NeurIPS 2020 Learning Deep Kernels for Non-Parametric Two-Sample Tests ICML 2020 Learning discrete distributions: user vs item-level privacy NeurIPS 2020 Learning discrete distributions with infinite support NeurIPS 2020 Learning Entangled Single-Sample Distributions via Iterative Trimming AISTATS 2020 Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model COLT 2020 Learning Fair Representations for Kernel Models AISTATS 2020 Learning Gaussian Graphical Models via Multiplicative Weights AISTATS 2020 Learning Kernel Tests Without Data Splitting NeurIPS 2020 Learning Latent Space Energy-Based Prior Model NeurIPS 2020