activity
20162018
most citedDistributional Adversarial Networks

12 citations · 13 across the 2 of their papers we have counts for

collaborators

6 papers

cs.LG2018

Improving Sequential Determinantal Point Processes for Supervised Video Summarization

Aidean Sharghi, Ali Borji, Chengtao Li +2

It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges a…

cs.LG2018

Representation Learning on Graphs with Jumping Knowledge Networks

Keyulu Xu, Chengtao Li, Yonglong Tian +3

Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose…

cs.LG2018

Robust GANs against Dishonest Adversaries

Zhi Xu, Chengtao Li, Stefanie Jegelka

Robustness of deep learning models is a property that has recently gained increasing attention. We explore a notion of robustness for generative adversarial models that is pertinen…

cs.LG201712 cited

Distributional Adversarial Networks

Chengtao Li, David Alvarez-Melis, Keyulu Xu +2

We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination. Inspired by discrepancy measure…

cs.LG20161 cited

Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes

Chengtao Li, Stefanie Jegelka, Suvrit Sra

In this note we consider sampling from (non-homogeneous) strongly Rayleigh probability measures. As an important corollary, we obtain a fast mixing Markov Chain sampler for Determi…

cs.LG2016

Fast DPP Sampling for Nyström with Application to Kernel Methods

Chengtao Li, Stefanie Jegelka, Suvrit Sra

The Nyström method has long been popular for scaling up kernel methods. Its theoretical guarantees and empirical performance rely critically on the quality of the landmarks selecte…