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20142020
most citedDeep Hyperspherical Learning

56 citations · 349 across the 25 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.LG2020★ 8 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.ML2020★ 2 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.ML2020★ 23 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.LG2020★ 7 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.LG2020★ 9 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★ 4 cited

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…