17 citations · 33 across the 15 of their papers we have counts for
17 papers
FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff +2
Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the pretrained distribution. Fey…
Multi-Mask Diffusion Language Models for Few-Step Generation
Sijin Chen, Yinuo Ren, Heyang Zhao +3
Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging. In MDMs, all forward trajectories…
SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate
Lifu Wei, Yinuo Ren, Naichen Shi +1
Data assimilation (DA) addresses the problem of sequentially estimating the state of a dynamical system from noisy and incomplete observations. In this work, we employ a diffusion…
Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
Chenyang Wang, Weizhong Wang, Yinuo Ren +2
iffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve sample quality on task-specific obj…
DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models
Yinuo Ren, Wenhao Gao, Lexing Ying +2
We study inference-time scaling for diffusion models, where the goal is to adapt a pre-trained model to new target distributions without retraining. Existing guidance-based methods…
Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach
Haoxuan Chen, Yinuo Ren, Martin Renqiang Min +2
Diffusion models (DMs) have proven to be effective in modeling high-dimensional distributions, leading to their widespread adoption for representing complex priors in Bayesian inve…
A Unified Approach to Analysis and Design of Denoising Markov Models
Yinuo Ren, Grant M. Rotskoff, Lexing Ying
Probabilistic generative models based on measure transport, such as diffusion and flow-based models, are often formulated in the language of Markovian stochastic dynamics, where th…
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
Yinuo Ren, Haoxuan Chen, Yuchen Zhu +5
Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis.…
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework
Yinuo Ren, Tesi Xiao, Michael Shavlovsky +2
In LLM alignment and many other ML applications, one often faces the Multi-Objective Fine-Tuning (MOFT) problem, i.e., fine-tuning an existing model with datasets labeled w.r.t. di…
How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff +1
Discrete diffusion models have gained increasing attention for their ability to model complex distributions with tractable sampling and inference. However, the error analysis for d…
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
Haoxuan Chen, Yinuo Ren, Lexing Ying +1
Diffusion models have become a leading method for generative modeling of both image and scientific data. As these models are costly to train and \emph{evaluate}, reducing the infer…
Understanding the Generalization Benefits of Late Learning Rate Decay
Yinuo Ren, Chao Ma, Lexing Ying
Why do neural networks trained with large learning rates for a longer time often lead to better generalization? In this paper, we delve into this question by examining the relation…
Statistical Spatially Inhomogeneous Diffusion Inference
Yinuo Ren, Yiping Lu, Lexing Ying +1
Inferring a diffusion equation from discretely-observed measurements is a statistical challenge of significant importance in a variety of fields, from single-molecule tracking in b…
Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
Yinuo Ren, Tesi Xiao, Tanmay Gangwani +4
Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications. We introduce a novel interacting particle method for MOO…
High-dimensional density estimation with tensorizing flow
Yinuo Ren, Hongli Zhao, Yuehaw Khoo +1
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data. The method is based on tensor-train and flow-based gene…
A semigroup method for high dimensional committor functions based on neural network
Haoya Li, Yuehaw Khoo, Yinuo Ren +1
This paper proposes a new method based on neural networks for computing the high-dimensional committor functions that satisfy Fokker-Planck equations. Instead of working with parti…
Hermite spectral method for Fokker-Planck-Landau equation modeling collisional plasma
Ruo Li, Yinuo Ren, Yanli Wang
We propose an Hermite spectral method for the Fokker-Planck-Landau (FPL) equation. Both the distribution functions and the collision terms are approximated by series expansions of…