Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. - Yeming Wen; Paul Vicol; Jimmy Ba; Dustin Tran; Roger B. Grosse (ICLR (Poster), 2018)

Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise. - Yeming Wen; Kevin Luk; Maxime Gazeau; Guodong Zhang 006; Harris Chan; Jimmy Ba (CoRR, 2019)

Benchmarking Model-Based Reinforcement Learning. - Tingwu Wang; Xuchan Bao; Ignasi Clavera; Jerrick Hoang; Yeming Wen; Eric D. Langlois 002; Shunshi Zhang 001; Guodong Zhang 006; Pieter Abbeel; Jimmy Ba (CoRR, 2019)

An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise. - Yeming Wen; Kevin Luk; Maxime Gazeau; Guodong Zhang 006; Harris Chan; Jimmy Ba (AISTATS, 2020)

BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. - Yeming Wen; Dustin Tran; Jimmy Ba (ICLR, 2020)

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. - Michael Dusenberry; Ghassen Jerfel; Yeming Wen; Yi-An Ma; Jasper Snoek; Katherine A. Heller; Balaji Lakshminarayanan; Dustin Tran (ICML, 2020)

Combining Ensembles and Data Augmentation Can Harm Your Calibration. - Yeming Wen; Ghassen Jerfel; Rafael Muller; Michael W. Dusenberry; Jasper Snoek; Balaji Lakshminarayanan; Dustin Tran (ICLR, 2021)

Neural Program Generation Modulo Static Analysis. - Rohan Mukherjee 001; Yeming Wen; Dipak Chaudhari; Thomas W. Reps; Swarat Chaudhuri; Christopher M. Jermaine (NeurIPS, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - Zachary Nado; Neil Band; Mark Collier; Josip Djolonga; Michael W. Dusenberry; Sebastian Farquhar; Angelos Filos; Marton Havasi; Rodolphe Jenatton; Ghassen Jerfel; Jeremiah Z. Liu; Zelda Mariet; et al. (CoRR, 2021)

Natural Language to Code Generation in Interactive Data Science Notebooks. - Pengcheng Yin; Wen-Ding Li; Kefan Xiao; Abhishek Rao; Yeming Wen; Kensen Shi; Joshua Howland; Paige Bailey; Michele Catasta; Henryk Michalewski; Oleksandr Polozov; Charles Sutton (ACL (1), 2023)

A Language-Agent Approach to Formal Theorem-Proving. - Amitayush Thakur; Yeming Wen; Swarat Chaudhuri (CoRR, 2023)

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. - Jeremiah Zhe Liu; Shreyas Padhy; Jie Ren 006; Zi Lin; Yeming Wen; Ghassen Jerfel; Zachary Nado; Jasper Snoek; Dustin Tran; Balaji Lakshminarayanan (J. Mach. Learn. Res., 2023)

Batched Low-Rank Adaptation of Foundation Models. - Yeming Wen; Swarat Chaudhuri (ICLR, 2024)

Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models. - Yeming Wen; Swarat Chaudhuri (NeurIPS, 2024)

Grounding Data Science Code Generation with Input-Output Specifications. - Yeming Wen; Pengcheng Yin; Kensen Shi; Henryk Michalewski; Swarat Chaudhuri; Alex Polozov (CoRR, 2024)