9 citations · 10 across the 7 of their papers we have counts for
7 papers
Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation
Rong Tang, Lizhen Lin, Yun Yang
We consider a class of conditional forward-backward diffusion models for conditional generative modeling, that is, generating new data given a covariate (or control variable). To f…
A Bayesian sparse factor model with adaptive posterior concentration
Ilsang Ohn, Lizhen Lin, Yongdai Kim
In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the load…
Machine Learning and the Future of Bayesian Computation
Steven Winter, Trevor Campbell, Lizhen Lin +2
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they…
Intrinsic and extrinsic deep learning on manifolds
Yihao Fang, Ilsang Ohn, Vijay Gupta +1
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds. Specifically, extrinsic deep neural networks (eDNNs) pres…
Bayesian Optimal Two-sample Tests in High-dimension
Kyoungjae Lee, Kisung You, Lizhen Lin
We propose optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including g…
Robust Optimization and Inference on Manifolds
Lizhen Lin, Drew Lazar, Bayan Sarpabayeva +1
We propose a robust and scalable procedure for general optimization and inference problems on manifolds leveraging the classical idea of `median-of-means' estimation. This is motiv…