Yeming Wen

According to our database, Yeming Wen authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models.

Yeming Wen, Swarat Chaudhuri

CoRR, 2024

Grounding Data Science Code Generation with Input-Output Specifications.

Yeming Wen, Pengcheng Yin, Kensen Shi, Henryk Michalewski, Swarat Chaudhuri, Alex Polozov

CoRR, 2024

Batched Low-Rank Adaptation of Foundation Models.

Yeming Wen, Swarat Chaudhuri

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness.

Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

J. Mach. Learn. Res., 2023

A Language-Agent Approach to Formal Theorem-Proving.

Amitayush Thakur, Yeming Wen, Swarat Chaudhuri

CoRR, 2023

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

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

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, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran

CoRR, 2021

Neural Program Generation Modulo Static Analysis.

Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Christopher M. Jermaine

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Combining Ensembles and Data Augmentation Can Harm Your Calibration.

Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Proceedings of the 37th International Conference on Machine Learning, 2020

BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning.

Yeming Wen, Dustin Tran, Jimmy Ba

Proceedings of the 8th International Conference on Learning Representations, 2020

An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise.

Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Benchmarking Model-Based Reinforcement Learning.

Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric D. Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba

CoRR, 2019

Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise.

Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba

CoRR, 2019

2018

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches.

Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse

Proceedings of the 6th International Conference on Learning Representations, 2018