Statistical Learning

Optimized Covariance Design for AB Test on Social Network under Interference NeurIPS 2023 PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction NeurIPS 2023 PAC-Bayesian Learning of Optimization Algorithms AISTATS 2023 PAC-Bayesian Offline Contextual Bandits With Guarantees ICML 2023 PAC Verification of Statistical Algorithms COLT 2023 PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces IJCAI 2023 Perfectly Secure Steganography Using Minimum Entropy Coupling ICLR 2023 Phase transition for detecting a small community in a large network ICLR 2023 Physics-Guided Active Learning of Environmental Flow Fields L4DC 2023 Pitch distributions in a very large corpus of spontaneous Finnish speech Interspeech 2023 Pitfalls of Gaussians as a noise distribution in NCE ICLR 2023 Pivotal Estimation of Linear Discriminant Analysis in High Dimensions JMLR 2023 “Plus/minus the learning rate”: Easy and Scalable Statistical Inference with SGD AISTATS 2023 Pointwise sampling uncertainties on the Precision-Recall curve AISTATS 2023 Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models ICML 2023 Posterior Contraction for Deep Gaussian Process Priors JMLR 2023 Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NeurIPS 2023 Posterior Tracking Algorithm for Classification Bandits AISTATS 2023 Precise asymptotic generalization for multiclass classification with overparameterized linear models NeurIPS 2023 Prediction with Incomplete Data under Agnostic Mask Distribution Shift IJCAI 2023 Principled Approaches for Private Adaptation from a Public Source AISTATS 2023 Privacy-preserving Sparse Generalized Eigenvalue Problem AISTATS 2023 Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations COLT 2023 Private Distribution Learning with Public Data: The View from Sample Compression NeurIPS 2023 Probabilistically robust conformal prediction UAI 2023