On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder. - Haowen Xu; Wenxiao Chen; Jinlin Lai; Zhihan Li; Youjian Zhao; Dan Pei (CoRR, 2019)

Shallow VAEs with RealNVP Prior can Perform as Well as Deep Hierarchical VAEs. - Haowen Xu; Wenxiao Chen; Jinlin Lai; Zhihan Li; Youjian Zhao; Dan Pei (ICONIP (5), 2020)

Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies. - Jinlin Lai; Lixin Zou; Jiaxing Song (CoRR, 2020)

Variational Marginal Particle Filters. - Jinlin Lai; Justin Domke; Daniel Sheldon (AISTATS, 2022)

Automatically marginalized MCMC in probabilistic programming. - Jinlin Lai; Javier Burroni; Hui Guan 001; Daniel Sheldon (ICML, 2023)

Efficient Semi-Supervised Segmentation of Medical Images with RESA-Enhanced U-Net. - Saijun Nie; Tianyu Xiao; Jinlin Lai; Yuandong Li; Siye Wang (IC-NIDC, 2023)

Outdoor Scene Relighting with Diffusion Models. - Jinlin Lai; Anustup Choudhury; Guan-Ming Su (ICPR (6), 2024)

Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models. - Jinlin Lai; Justin Domke; Daniel R. Sheldon (NeurIPS, 2024)

Predictive variational inference: Learn the predictively optimal posterior distribution. - Jinlin Lai; Yuling Yao (CoRR, 2024)

Quasi-random Multi-Sample Inference for Large Language Models. - Aditya Parashar; Aditya Vikram Singh; Avinash Amballa; Jinlin Lai; Benjamin Rozonoyer (CoRR, 2024)

Active Measurement: Efficient Estimation at Scale. - Max Hamilton; Jinlin Lai; Wenlong Zhao; Subhransu Maji; Daniel Sheldon (CoRR, 2025)