Search Results for author: Yian Ma

Found 9 papers, 1 papers with code

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?

no code implementations27 Jul 2023 Kyurae Kim, Yian Ma, Jacob R. Gardner

We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification.

Variational Inference

Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach

no code implementations5 Jul 2023 Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang

The efficacy of modern generative models is commonly contingent upon the precision of score estimation along the diffusion path, with a focus on diffusion models and their ability to generate high-quality data samples.

Black-Box Variational Inference Converges

no code implementations24 May 2023 Kyurae Kim, Kaiwen Wu, Jisu Oh, Yian Ma, Jacob R. Gardner

We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference.

Bayesian Inference Variational Inference

Disentangled Multi-Fidelity Deep Bayesian Active Learning

1 code implementation7 May 2023 Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu

To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication.

Active Learning Gaussian Processes

On Optimal Early Stopping: Overparametrization versus Underparametrization

no code implementations29 Sep 2021 Ruoqi Shen, Liyao Gao, Yian Ma

Early stopping is a simple and widely used method to prevent over-training neural networks.

Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence

no code implementations30 Jun 2021 Ghassen Jerfel, Serena Wang, Clara Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan

We thus propose a novel combination of optimization and sampling techniques for approximate Bayesian inference by constructing an IS proposal distribution through the minimization of a forward KL (FKL) divergence.

Bayesian Inference Variational Inference

Langevin Dynamics as Nonparametric Variational Inference

no code implementations pproximateinference AABI Symposium 2019 Matthew D. Hoffman, Yian Ma

Variational inference (VI) and Markov chain Monte Carlo (MCMC) are approximate posterior inference algorithms that are often said to have complementary strengths, with VI being fast but biased and MCMC being slower but asymptotically unbiased.

Variational Inference

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