Yeming Wen
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 2020, 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 2018, 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, 2782-2792, 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, 3621-3631, 2020
Combining ensembles and data augmentation can harm your calibration
Y Wen, G Jerfel, R Muller, MW Dusenberry, J Snoek, ...
International Conference on Learning Representations 2021, 2020
Neural Program Generation Modulo Static Analysis
R Mukherjee, Y Wen, D Chaudhari, TW Reps, S Chaudhuri, C Jermaine
Advances in Neural Information Processing Systems (2021), 2021
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 23, 1-63, 2022
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, ...
arXiv preprint arXiv:2212.09248, 2022
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