52 citations · 212 across the 22 of their papers we have counts for
6 papers · 1 filter
First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data
Mao Ye, Lemeng Wu, Qiang Liu
We propose a family of First Hitting Diffusion Models (FHDM), deep generative models that generate data with a diffusion process that terminates at a random first hitting time. Thi…
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Mao Ye, Bo Liu, Stephen Wright +2
Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual…
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
Mao Ye, Ruichen Jiang, Haoxiang Wang +6
One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and…
Diffusion-based Molecule Generation with Informative Prior Bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu +2
AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine develo…
Bit Allocation using Optimization
Tongda Xu, Han Gao, Chenjian Gao +10
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC). First, we reveal a fundamental relationship between bit allocation in NVC and Semi-Amort…
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Xingchao Liu, Lemeng Wu, Mao Ye +1
Diffusion-based generative models have achieved promising results recently, but raise an array of open questions in terms of conceptual understanding, theoretical analysis, algorit…