activity
20162020
most citedPicasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

23 citations · 83 across the 6 of their papers we have counts for

collaborators

14 papers

stat.ML202023 cited

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

Jason Ge, Xingguo Li, Haoming Jiang +4

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear reg…

stat.ML2019

Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks

Kean Ming Tan, Junwei Lu, Tong Zhang +1

Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental set…

cs.CL201915 cited

Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement

Zi-Yi Dou, Zhaopeng Tu, Xing Wang +3

With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine transl…

math.OC201918 cited

Sharp Analysis for Nonconvex SGD Escaping from Saddle Points

Cong Fang, Zhouchen Lin, Tong Zhang

In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an -appr…

cs.LG2018

Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents

Kaiqing Zhang, Zhuoran Yang, Han Liu +2

Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challen…

stat.ML2018

A convex formulation for high-dimensional sparse sliced inverse regression

Kean Ming Tan, Zhaoran Wang, Tong Zhang +2

Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of informatio…