13 citations · 30 across the 9 of their papers we have counts for
11 papers
Bridging Stochastic Flow Maps and Boltzmann Generators with Normalizing Flows
Louis Grenioux, RuiKang OuYang, Luhuan Wu
Generating independent, equilibrium samples of molecular systems at scale remains a central obstacle in computational statistical mechanics. Boltzmann Generators address this by pa…
Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization
Minhuan Li, Jiequn Han, Pilar Cossio +1
A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experimental measurements. While sequence-…
Reverse Diffusion Sequential Monte Carlo Samplers
Luhuan Wu, Yi Han, Christian A. Naesseth +1
We propose a novel sequential Monte Carlo (SMC) method for sampling from unnormalized target distributions based on a reverse denoising diffusion process. While recent diffusion-ba…
Bayesian Invariance Modeling of Multi-Environment Data
Luhuan Wu, Mingzhang Yin, Yixin Wang +2
Invariant prediction [Peters et al., 2016] analyzes feature/outcome data from multiple environments to identify invariant features - those with a stable predictive relationship to…
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
Luhuan Wu, Sinead Williamson
In this paper, we approach the problem of uncertainty quantification in deep learning through a predictive framework, which captures uncertainty in model parameters by specifying o…
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth +2
Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily…
Denoising Deep Generative Models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu +3
Likelihood-based deep generative models have recently been shown to exhibit pathological behaviour under the manifold hypothesis as a consequence of using high-dimensional densitie…
Variational Nearest Neighbor Gaussian Process
Luhuan Wu, Geoff Pleiss, John Cunningham
Variational approximations to Gaussian processes (GPs) typically use a small set of inducing points to form a low-rank approximation to the covariance matrix. In this work, we inst…
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
Luhuan Wu, Andrew Miller, Lauren Anderson +3
We examine the general problem of inter-domain Gaussian Processes (GPs): problems where the GP realization and the noisy observations of that realization lie on different domains.…
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman +2
Scalable Gaussian Process methods are computationally attractive, yet introduce modeling biases that require rigorous study. This paper analyzes two common techniques: early trunca…
Particle Smoothing Variational Objectives
Antonio Khalil Moretti, Zizhao Wang, Luhuan Wu +2
A body of recent work has focused on constructing a variational family of filtered distributions using Sequential Monte Carlo (SMC). Inspired by this work, we introduce Particle Sm…