Statistical Learning

Statistical Foundations of Prior-Data Fitted Networks ICML 2023 Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory NeurIPS 2023 Statistical Indistinguishability of Learning Algorithms ICML 2023 Statistical Inference for Fisher Market Equilibrium ICLR 2023 Statistical Inference for Noisy Incomplete Binary Matrix JMLR 2023 Statistical Inference on Multi-armed Bandits with Delayed Feedback ICML 2023 Statistical Learning under Heterogeneous Distribution Shift ICML 2023 Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference NeurIPS 2023 Statistically Valid Variable Importance Assessment through Conditional Permutations NeurIPS 2023 Statistical Robustness of Empirical Risks in Machine Learning JMLR 2023 Stepdown SLOPE for Controlled Feature Selection AAAI 2023 Structured Prediction with Stronger Consistency Guarantees NeurIPS 2023 Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation ICML 2023 SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking NeurIPS 2023 Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms UAI 2023 Superlim: A Swedish Language Understanding Evaluation Benchmark EMNLP 2023 T-Cal: An Optimal Test for the Calibration of Predictive Models JMLR 2023 Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems ICML 2023 The Aggregation–Heterogeneity Trade-off in Federated Learning COLT 2023 The ELBO of Variational Autoencoders Converges to a Sum of Entropies AISTATS 2023 The Geometry and Calculus of Losses JMLR 2023 The Linearity of the Effect of Surprisal on Reading Times across Languages EMNLP 2023 The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data NeurIPS 2023 The noise level in linear regression with dependent data NeurIPS 2023 The One-Inclusion Graph Algorithm is not Always Optimal COLT 2023