activity
20172020
most citedDeep Hyperspherical Learning

56 citations · 172 across the 11 of their papers we have counts for

collaborators

17 papers

cs.LG20208 cited

Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning

Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

We consider the problem of factorizing a structured 3-way tensor into its constituent Canonical Polyadic (CP) factors. This decomposition, which can be viewed as a generalization o…

stat.ML20202 cited

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R

Xingguo Li, Tuo Zhao, Xiaoming Yuan +1

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, Lasso, and Dantzig selector) an…

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…

cs.LG20207 cited

Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality

Yi Zhang, Orestis Plevrakis, Simon S. Du +3

Adversarial training is a popular method to give neural nets robustness against adversarial perturbations. In practice adversarial training leads to low robust training loss. Howev…

cs.LG20209 cited

On Computation and Generalization of Generative Adversarial Imitation Learning

Minshuo Chen, Yizhou Wang, Tianyi Liu +4

Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies. Different from Reinforcement Learning (RL),…

eess.SP2020

On Recoverability of Randomly Compressed Tensors with Low CP Rank

Shahana Ibrahim, Xiao Fu, Xingguo Li

Our interest lies in the recoverability properties of compressed tensors under the \textit{canonical polyadic decomposition} (CPD) model. The considered problem is well-motivated i…