Publications & working papers

You can also find my articles on my Google Scholar profile.

Jinlin Lai*, Yuling Yao*. (2024). Predictive variational inference: Learn the predictively optimal posterior distribution. arXiv preprint arXiv:2410.14843. [link]

Jinlin Lai, Daniel Sheldon, Justin Domke. (2024). Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models. NeurIPS 2024, the Thirty-Eighth Annual Conference on Neural Information Processing Systems. [link] [code]

Jinlin Lai, Anustup Choudhury, Guan-Ming Su. (2024). Outdoor Scene Relighting with Diffusion Models. In Proceedings of the 27th International Conference on Pattern Recognition (ICPR), Kolkata, India. (to appear)

Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon. (2023). Automatically Marginalized MCMC in Probabilistic Programming. In Proceedings of the 40th International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA. PMLR 202, 2023. [link] [code]

Jinlin Lai, Daniel Sheldon. 2022. Automatic Inference with Pseudo-Marginal Hamiltonian Monte Carlo. ICML workshop Beyond Bayes: Paths Towards Universal Reasoning Systems. [link] [code]

Jinlin Lai, Justin Domke, Daniel Sheldon. (2022). Variational Marginal Particle Filters. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, Valencia, Spain. PMLR: Volume 151. [link] [code]

Jinlin Lai, Lixin Zou, Jiaxing Song. 2020. Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies. Offline Reinforcement Learning Workshop at Neural Information Processing Systems. [link]

Haowen Xu, Wenxiao Chen, Jinlin Lai, Zhihan Li, Youjian Zhao, Dan Pei. 2020. Shallow VAEs with RealNVP Prior can Perform as Well as Deep Hierarchical VAEs. ICONIP. [link] [preprint]