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Willie Neiswanger
Willie Neiswanger
Postdoc, Computer Science, Stanford University
在 cs.stanford.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Neural architecture search with bayesian optimisation and optimal transport
K Kandasamy, W Neiswanger, J Schneider, B Poczos, E Xing
Proceedings of the 32nd International Conference on Neural Information …, 2018
6712018
Asymptotically Exact, Embarrassingly Parallel MCMC
W Neiswanger, C Wang, E Xing
4232014
Bananas: Bayesian optimization with neural architectures for neural architecture search
C White, W Neiswanger, Y Savani
Proceedings of the AAAI Conference on Artificial Intelligence 35, 2021
3202021
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly
K Kandasamy, KR Vysyaraju, W Neiswanger, B Paria, CR Collins, ...
Journal of Machine Learning Research 21 (81), 1-27, 2020
2122020
Methods for comparing uncertainty quantifications for material property predictions
K Tran, W Neiswanger, J Yoon, Q Zhang, E Xing, ZW Ulissi
Machine Learning: Science and Technology 1 (2), 025006, 2020
1682020
Pollux: Co-adaptive cluster scheduling for goodput-optimized deep learning
A Qiao, SK Choe, SJ Subramanya, W Neiswanger, Q Ho, H Zhang, ...
15th {USENIX} Symposium on Operating Systems Design and Implementation …, 2021
1392021
Chembo: Bayesian optimization of small organic molecules with synthesizable recommendations
K Korovina, S Xu, K Kandasamy, W Neiswanger, B Poczos, J Schneider, ...
International Conference on Artificial Intelligence and Statistics, 3393-3403, 2020
1362020
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
B Boecking, W Neiswanger, E Xing, A Dubrawski
International Conference on Learning Representations (ICLR), 2021
832021
Beyond pinball loss: Quantile methods for calibrated uncertainty quantification
Y Chung, W Neiswanger, I Char, J Schneider
Proceedings of the 35th International Conference on Neural Information …, 2021
822021
Uncertainty toolbox: an open-source library for assessing, visualizing, and improving uncertainty quantification
Y Chung, I Char, H Guo, J Schneider, W Neiswanger
arXiv preprint arXiv:2109.10254, 2021
792021
A study on encodings for neural architecture search
C White, W Neiswanger, S Nolen, Y Savani
Advances in Neural Information Processing Systems 33, 2020
752020
Synthetic benchmarks for scientific research in explainable machine learning
Y Liu, S Khandagale, C White, W Neiswanger
arXiv preprint arXiv:2106.12543, 2021
562021
Offline contextual bayesian optimization
I Char, Y Chung, W Neiswanger, K Kandasamy, AO Nelson, M Boyer, ...
Advances in Neural Information Processing Systems 32, 2019
52*2019
Fast distribution to real regression
J Oliva, W Neiswanger, B Póczos, J Schneider, E Xing
Artificial Intelligence and Statistics, 706-714, 2014
502014
Parallel and distributed block-coordinate frank-wolfe algorithms
YX Wang, V Sadhanala, W Dai, W Neiswanger, S Sra, E Xing
International Conference on Machine Learning, 1548-1557, 2016
492016
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Y Xiao, PP Liang, U Bhatt, W Neiswanger, R Salakhutdinov, LP Morency
Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
442022
Durable interactions of T cells with T cell receptor stimuli in the absence of a stable immunological synapse
V Mayya, E Judokusumo, EA Shah, CG Peel, W Neiswanger, D Depoil, ...
Cell reports 22 (2), 340-349, 2018
442018
The dependent Dirichlet process mixture of objects for detection-free tracking and object modeling
W Neiswanger, F Wood, E P Xing
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2014
44*2014
Post-inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making
W Neiswanger
Carnegie Mellon University, 2019
33*2019
Generalized P {\'o} lya Urn for Time-Varying Pitman-Yor Processes
F Caron, W Neiswanger, F Wood, A Doucet, M Davy
Journal of Machine Learning Research 18 (27), 1-32, 2017
332017
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