Mathematics & Optimization

A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models AISTATS 2021 A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples ICML 2021 A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration JMLR 2021 A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving AAAI 2021 A Spectral Analysis of Dot-product Kernels AISTATS 2021 A Statistical Taylor Theorem and Extrapolation of Truncated Densities COLT 2021 A Stein Goodness-of-test for Exponential Random Graph Models AISTATS 2021 A Stochastic Newton Algorithm for Distributed Convex Optimization NeurIPS 2021 A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting ICML 2021 A Study of Condition Numbers for First-Order Optimization AISTATS 2021 A surprisal–duration trade-off across and within the world’s languages EMNLP 2021 A Surrogate Objective Framework for Prediction+Programming with Soft Constraints NeurIPS 2021 Asymptotically Best Causal Effect Identification with Multi-Armed Bandits NeurIPS 2021 Asymptotically Exact and Fast Gaussian Copula Models for Imputation of Mixed Data Types ACML 2021 Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time ALT 2021 Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors JMLR 2021 Asymptotics of Ridge(less) Regression under General Source Condition AISTATS 2021 Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection NeurIPS 2021 Asynchronous Decentralized Online Learning NeurIPS 2021 Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction ICML 2021 Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge ICML 2021 Asynchronous Stochastic Optimization Robust to Arbitrary Delays NeurIPS 2021 As You Like It: Localization via Paired Comparisons JMLR 2021 A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions ICML 2021 A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines JMLR 2021