Optimization
A Generic Acceleration Framework for Stochastic Composite Optimization
Specifically, we extend the Catalyst approach originally designed for deterministic objectives to the stochastic setting.
Learning Robust Options by Conditional Value at Risk Optimization
Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka
Neural Information Processing Systems http://nips.cc/
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
Neural Information Processing Systems http://nips.cc/
Model Inversion Networks for Model-Based Optimization
MINs can scale to high-dimensional input spaces and leverage offline logged data for both contextual and non-contextual optimization problems.
HONOR: Hybrid Optimization for NOn-convex Regularized problems
Recent years have witnessed the superiority of non-convex s parse learning formulations over their convex counterparts in both theory and pr actice. However, due to the non-convexity and non-smoothness of the regularizer, how to efficiently solve the non-convex optimization problem for large-scale data is still quite challenging. In this paper, we propose an efficient H ybrid O ptimization algorithm for NO n-convex R egularized problems (HONOR). Specifically, we develop a hybrid scheme which effectively integrates a Quasi-Newton (Q N) step and a Gradient Descent (GD) step. Our contributions are as follows: ( 1) HONOR incorporates the second-order information to greatly speed up th e convergence, while it avoids solving a regularized quadratic programming and o nly involves matrix-vector multiplications without explicitly forming the inv erse Hessian matrix.
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
Deeksha Adil, Richard Peng, Sushant Sachdeva
Neural Information Processing Systems http://nips.cc/
Sum-of-Squares Lower Bounds for Sparse PCA
This paper establishes a statistical versus computational trade-off for solving a basic high-dimensional machine learning problem via a basic convex relaxation method. Specifically, we consider the Sparse Principal Component Analysis (Sparse PCA) problem, and the family of Sum-of-Squares (SoS, aka Lasserre/Parillo) convex relaxations.
