activity
20162025
most citedBoosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records

25 citations · 104 across the 14 of their papers we have counts for

collaborators

17 papers

stat.ML2025

Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo

James Thornton, Louis Bethune, Ruixiang Zhang +3

Diffusion models may be formulated as a time-indexed sequence of energy-based models, where the score corresponds to the negative gradient of an energy function. As opposed to lear…

cs.CV20222 cited

f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation

Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2

Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational au…

cs.LG20221 cited

Learning Representation from Neural Fisher Kernel with Low-rank Approximation

Ruixiang Zhang, Shuangfei Zhai, Etai Littwin +1

In this paper, we study the representation of neural networks from the view of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher Kernel applied to neural…

cs.CL20211 cited

Regularized Training of Nearest Neighbor Language Models

Jean-Francois Ton, Walter Talbott, Shuangfei Zhai +1

Including memory banks in a natural language processing architecture increases model capacity by equipping it with additional data at inference time. In this paper, we build upon $…

cs.LG2021

Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks

Etai Littwin, Omid Saremi, Shuangfei Zhai +4

We analyze the learning dynamics of infinitely wide neural networks with a finite sized bottle-neck. Unlike the neural tangent kernel limit, a bottleneck in an otherwise infinite w…

cs.LG202120 cited

Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

Yue Wu, Shuangfei Zhai, Nitish Srivastava +4

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. However, existing Q-learning and ac…