Yeming Wen
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BatchEnsemble: an alternative approach to efficient ensemble and lifelong learning
Y Wen, D Tran, J Ba
International Conference on Learning Representations (ICLR), 2020
Benchmarking model-based reinforcement learning
T Wang, X Bao, I Clavera, J Hoang, Y Wen, E Langlois, S Zhang, G Zhang, ...
arXiv preprint arXiv:1907.02057, 2019
Flipout: efficient pseudo-independent weight perturbations on mini-batches
Y Wen, P Vicol, J Ba, D Tran, R Grosse
International Conference on Learning Representations (ICLR), 2018
Efficient and scalable bayesian neural nets with rank-1 factors
M Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ...
International Conference on Machine Learning (ICML), 2020
Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
arXiv preprint arXiv:2106.04015, 2021
Combining ensembles and data augmentation can harm your calibration
Y Wen, G Jerfel, R Muller, MW Dusenberry, J Snoek, ...
International Conference on Learning Representations (ICLR), 2020
An empirical study of stochastic gradient descent with structured covariance noise
Y Wen, K Luk, M Gazeau, G Zhang, H Chan, J Ba
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
A simple approach to improve single-model deep uncertainty via distance-awareness
JZ Liu, S Padhy, J Ren, Z Lin, Y Wen, G Jerfel, Z Nado, J Snoek, D Tran, ...
Journal of Machine Learning Research (JMLR), 2023
Natural language to code generation in interactive data science notebooks
P Yin, WD Li, K Xiao, A Rao, Y Wen, K Shi, J Howland, P Bailey, ...
ACL 2023, 2022
Neural program generation modulo static analysis
R Mukherjee, Y Wen, D Chaudhari, TW Reps, S Chaudhuri, C Jermaine
Advances in Neural Information Processing Systems (NeurIPS), 2021
A language-agent approach to formal theorem-proving
A Thakur, Y Wen, S Chaudhuri
arXiv preprint arXiv:2310.04353, 2023
Batched low-rank adaptation of foundation models
Y Wen, S Chaudhuri
International Conference on Learning Representations (ICLR), 2023
Grounding data science code generation with input-output specifications
Y Wen, P Yin, K Shi, H Michalewski, S Chaudhuri, A Polozov
arXiv preprint arXiv:2402.08073, 2024
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