970 citations · 1.1k across the 12 of their papers we have counts for
12 papers
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…
Polynomial Time Algorithms for Dual Volume Sampling
Chengtao Li, Stefanie Jegelka, Suvrit Sra
We study dual volume sampling, a method for selecting k columns from an n x m short and wide matrix (n <= k <= m) such that the probability of selection is proportional to the volu…
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Zi Wang, Chengtao Li, Stefanie Jegelka +1
Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box fun…
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
Chengtao Li, Stefanie Jegelka, Suvrit Sra
We study probability measures induced by set functions with constraints. Such measures arise in a variety of real-world settings, where prior knowledge, resource limitations, or ot…
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…
Gauss quadrature for matrix inverse forms with applications
Chengtao Li, Suvrit Sra, Stefanie Jegelka
We present a framework for accelerating a spectrum of machine learning algorithms that require computation of bilinear inverse forms , where is a positive defin…
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…