statistical learning

On the generalization of learning algorithms that do not converge NeurIPS 2022 On the relation between statistical learning and perceptual distances ICLR 2022 On the Role of Channel Capacity in Learning Gaussian Mixture Models COLT 2022 Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation NeurIPS 2022 Recovering shared structure from multiple networks with unknown edge distributions JMLR 2022 Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment AAAI 2022 Rethinking Class-Prior Estimation for Positive-Unlabeled Learning ICLR 2022 Robust Estimation for Random Graphs COLT 2022 Robust Model Selection and Nearly-Proper Learning for GMMs NeurIPS 2022 Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables NeurIPS 2022 Stability of SGD: Tightness analysis and improved bounds UAI 2022 Statistical and computational thresholds for the planted k-densest sub-hypergraph problem AISTATS 2022 Statistical Learning and Inverse Problems: A Stochastic Gradient Approach NeurIPS 2022 Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap NeurIPS 2022 Testing Whether a Learning Procedure is Calibrated JMLR 2022 The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics NeurIPS 2022 What is a Good Metric to Study Generalization of Minimax Learners? NeurIPS 2022 A Computationally Efficient Method for Learning Exponential Family Distributions NeurIPS 2021 A constrained risk inequality for general losses AISTATS 2021 An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning NeurIPS 2021 A Statistical Extension of Byte-Pair Encoding ACL 2021 A statistical perspective on distillation ICML 2021 Differentially Private Sampling from Distributions NeurIPS 2021 Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals NeurIPS 2021 Efficient Truncated Linear Regression with Unknown Noise Variance NeurIPS 2021