399 citations · 3.4k across the 95 of their papers we have counts for
10 papers · 1 filter
Stochastic Recursive Momentum for Policy Gradient Methods
Huizhuo Yuan, Xiangru Lian, Ji Liu +1
In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced po…
Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization
Huizhuo Yuan, Xiangru Lian, Ji Liu
Stochastic compositional optimization arises in many important machine learning tasks such as value function evaluation in reinforcement learning and portfolio management. The obje…
Variance Reduced methods for Non-convex Composition Optimization
Liu Liu, Ji Liu, Dacheng Tao
This paper explores the non-convex composition optimization in the form including inner and outer finite-sum functions with a large number of component functions. This problem aris…
Duality-free Methods for Stochastic Composition Optimization
Liu Liu, Ji Liu, Dacheng Tao
We consider the composition optimization with two expected-value functions in the form of ,…
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Wei Qian, Wending Li, Yasuhiro Sogawa +3
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using…
Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem
Jie Zhong, Yijun Huang, Ji Liu
This paper considers the multi-armed thresholding bandit problem -- identifying all arms whose expected rewards are above a predefined threshold via as few pulls (or rounds) as pos…
GaDei: On Scale-up Training As A Service For Deep Learning
Wei Zhang, Minwei Feng, Yunhui Zheng +8
Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who hav…
Exclusive Sparsity Norm Minimization with Random Groups via Cone Projection
Yijun Huang, Ji Liu
Many practical applications such as gene expression analysis, multi-task learning, image recognition, signal processing, and medical data analysis pursue a sparse solution for the…
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint
Ji Liu, Ryohei Fujimaki, Jieping Ye
We consider forward-backward greedy algorithms for solving sparse feature selection problems with general convex smooth functions. A state-of-the-art greedy method, the Forward-Bac…
Robust Dequantized Compressive Sensing
Ji Liu, Stephen J. Wright
We consider the reconstruction problem in compressed sensing in which the observations are recorded in a finite number of bits. They may thus contain quantization errors (from bein…