970 citations · 1.1k across the 12 of their papers we have counts for
8 papers · 1 filter
AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design
Xinze Li, Penglei Wang, Tianfan Fu +4
Structure-based drug design (SBDD), which aims to generate molecules that can bind tightly to the target protein, is an essential problem in drug discovery, and previous approaches…
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…
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…
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…
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…
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…
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…
Efficient Sampling for k-Determinantal Point Processes
Chengtao Li, Stefanie Jegelka, Suvrit Sra
Determinantal Point Processes (DPPs) are elegant probabilistic models of repulsion and diversity over discrete sets of items. But their applicability to large sets is hindered by e…