Willie Neiswanger
Orcid: 0000-0002-9619-5572Affiliations:
- Stanford University, USA
- Carnegie Mellon University, Machine Learning Department (PhD 2020)
According to our database1,
Willie Neiswanger
authored at least 62 papers
between 2012 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
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Online presence:
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on twitter.com
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on orcid.org
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on github.com
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on cs.cmu.edu
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on dl.acm.org
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Bibliography
2024
Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives.
Mach. Learn. Sci. Technol., March, 2024
CoRR, 2024
CoRR, 2024
DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models.
CoRR, 2024
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial Measurements.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022
2021
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification.
CoRR, 2021
Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation.
CoRR, 2021
Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation, 2021
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information.
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the 9th International Conference on Learning Representations, 2021
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
Proceedings of the Algorithmic Learning Theory, 2021
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Post-inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making.
PhD thesis, 2020
Mach. Learn. Sci. Technol., 2020
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly.
J. Mach. Learn. Res., 2020
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
CoRR, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
2018
Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful.
CoRR, 2018
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming.
CoRR, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
J. Mach. Learn. Res., 2017
Proceedings of the 34th International Conference on Machine Learning, 2017
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017
2016
Proceedings of the 33nd International Conference on Machine Learning, 2016
2015
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015
2014
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014
2012
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures
CoRR, 2012