Statistical Learning

On Differentially Private Sampling from Gaussian and Product Distributions NeurIPS 2023 One-Shot Federated Conformal Prediction ICML 2023 On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances ICML 2023 On Generalization Bounds for Projective Clustering NeurIPS 2023 On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs ICML 2023 On kernel-based statistical learning theory in the mean field limit NeurIPS 2023 On Learning Latent Models with Multi-Instance Weak Supervision NeurIPS 2023 On Learning Necessary and Sufficient Causal Graphs NeurIPS 2023 Online Boundary-Free Continual Learning by Scheduled Data Prior ICLR 2023 Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks JMLR 2023 On Model Selection Consistency of Lasso for High-Dimensional Ising Models AISTATS 2023 On Private and Robust Bandits NeurIPS 2023 On Second-Order Scoring Rules for Epistemic Uncertainty Quantification ICML 2023 On Testability and Goodness of Fit Tests in Missing Data Models UAI 2023 On the bias of K-fold cross validation with stable learners AISTATS 2023 On the Calibration of Probabilistic Classifier Sets AISTATS 2023 On the Capacity Limits of Privileged ERM AISTATS 2023 On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis NeurIPS 2023 On the Efficacy of Sampling Adapters ACL 2023 On the Estimation of Gaussian Mixture Copula Models ICML 2023 On the Existence of a Complexity in Fixed Budget Bandit Identification COLT 2023 On the Generalization Error of Stochastic Mirror Descent for Quadratically-Bounded Losses: an Improved Analysis NeurIPS 2023 On the Identifiability of Sparse ICA without Assuming Non-Gaussianity NeurIPS 2023 On the inconsistency of separable losses for structured prediction EACL 2023 On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits ICML 2023