5 citations · 14 across the 13 of their papers we have counts for
8 papers · 1 filter
Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational Inference
Baojun Che, Yifan Chen, Daniel Zhengyu Huang +2
Black-box variational inference (BBVI) with Gaussian mixture families offers a flexible approach for approximating complex posterior distributions without requiring gradients of th…
Improving Monte Carlo Tree Search for Symbolic Regression
Zhengyao Huang, Daniel Zhengyu Huang, Tiannan Xiao +4
Symbolic regression aims to discover concise, interpretable mathematical expressions that satisfy desired objectives, such as fitting data, posing a highly combinatorial optimizati…
Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models
Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
Diffusion probabilistic models generate samples by learning to reverse a noise-injection process that transforms data into noise. A key development is the reformulation of the reve…
Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems
Baojun Che, Yifan Chen, Zhenghao Huan +2
This paper is concerned with the approximation of probability distributions known up to normalization constants, with a focus on Bayesian inference for large-scale inverse problems…
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesi…
Convergence Analysis of Probability Flow ODE for Score-based Generative Models
Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinni…
An operator learning perspective on parameter-to-observable maps
Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner
Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering. Operator learning provides data-driven surrogates that map bet…
On Training Derivative-Constrained Neural Networks
KaiChieh Lo, Daniel Huang
We refer to the setting where the (partial) derivatives of a neural network's (NN's) predictions with respect to its inputs are used as additional training signal as a derivative-c…