Statistical Learning

On Estimation in Latent Variable Models ICML 2021 On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective CVPR 2021 On Learning Continuous Pairwise Markov Random Fields AISTATS 2021 On Linear Identifiability of Learned Representations ICML 2021 Online false discovery rate control for anomaly detection in time series NeurIPS 2021 Online stochastic gradient descent on non-convex losses from high-dimensional inference JMLR 2021 On Optimal Interpolation in Linear Regression NeurIPS 2021 On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification AISTATS 2021 On Robust Mean Estimation under Coordinate-level Corruption ICML 2021 On Statistical Bias In Active Learning: How and When to Fix It ICLR 2021 On the Estimation of Network Complexity: Dimension of Graphons JMLR 2021 On the Generalization Properties of Adversarial Training AISTATS 2021 On the Inherent Regularization Effects of Noise Injection During Training ICML 2021 On the Minimal Error of Empirical Risk Minimization COLT 2021 On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression AISTATS 2021 On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests JMLR 2021 On the Power of Differentiable Learning versus PAC and SQ Learning NeurIPS 2021 On the Privacy Properties of GAN-generated Samples AISTATS 2021 On the Role of Data in PAC-Bayes Bounds AISTATS 2021 On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians ALT 2021 On the Stability of System Rankings at WMT EMNLP 2021 On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix L4DC 2021 On User Interfaces for Large-Scale Document-Level Human Evaluation of Machine Translation Outputs EACL 2021 On Variational Inference in Biclustering Models ICML 2021 Optimal Bounds between f-Divergences and Integral Probability Metrics JMLR 2021