Statistical Learning
Fast Model Debias with Machine Unlearning Ruizhe Chen 1, Jianfei Y ang
Such error correlations not only affect the model's ability to make robust predictions but also
ARTree: A Deep Autoregressive Model for Phylogenetic Inference
Designing flexible probabilistic models over tree topologies is important for developing efficient phylogenetic inference methods.
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence Ben Dai
Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks.
Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework.
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.