Willie Neiswanger
Machine learning at Stanford ⊃ Computer Science ⊃ SAIL / StatsML
I am a postdoc in computer science at Stanford University, working with Stefano Ermon and affiliated with the StatsML Group, Stanford AI Lab, and SLAC.
Incoming Assistant Professor at USC Viterbi, Department of Computer Science, starting Jan 2024.
Prospective students: I am recruiting PhD students (current application cycle) and postdocs who wish to do work at the intersection of machine learning, decision making, generative AI, and AI-for-science! If you are interested, please feel free to reach out via email, and apply here by December 15.
Research: I develop machine learning methods to perform efficient optimization and experimental design in costly real-world settings, where resources are limited. My work spans topics in active learning, uncertainty quantification, Bayesian decision making, and reinforcement learning. I apply these methods downstream to solve problems in science and engineering, for example in the physical sciences and machine learning systems.
I have also worked on distributed algorithms for scalable machine learning, and I develop/maintain software libraries for multilevel optimization, uncertainty quantification, AutoML, and Bayesian optimization.
Education: I completed my PhD in Machine Learning at Carnegie Mellon University, where I was advised by Eric Xing and collaborated with Jeff Schneider and Barnabas Poczos.
Previously, I studied at Columbia University, where I worked with Chris Wiggins and Frank Wood.
News
- Oct 20, 2023 New paper (+ code) on algorithms and systems for scalable meta learning in NeurIPS 2023.
- Oct 20, 2023 New paper on offline model-based optimization through co-teaching in NeurIPS 2023.
- July 28, 2023 Co-organized the Differentiable Almost Everything Workshop at ICML 2023.
- Mar 23, 2023 Co-organized the Modern Adaptive Experimental Design and Active Learning Online Reading Group.
- Jan 20, 2023 New paper on automatic differentiation for multilevel optimization in ICLR 2023 (notable-top-5%).
- Jan 20, 2023 New paper on policy identification for active reinforcement learning in ICLR 2023 (notable-top-5%).
- Jan 20, 2023 New paper on a framework to combine weak supervision and generative modeling in ICLR 2023.
- Jan 1, 2023 New paper on offline imitation learning with suboptimal demonstrations in AAAI 2023.
- Dec 7, 2022 New paper on uncertainty quantification with pre-trained language models in EMNLP 2022.
- Dec 2, 2022 Invited talk at the Workshop on Gaussian Processes and Decision-making Systems at NeurIPS 2022.
- Oct 10, 2022 New paper (+ code) on trajectory information planning for exploration in RL in NeurIPS 2022.
- Oct 10, 2022 New paper on decision-theoretic entropies for generalizing Bayesian optimization in NeurIPS 2022.
- July 22, 2022 Co-organized the Real World Experiment Design and Active Learning Workshop at ICML 2022.
- May 15, 2022 New paper (+ website) on likelihood-free Bayesian optimization in ICML 2022 (long talk).
- May 15, 2022 New paper on a modular conformal calibration framework for UQ in ICML 2022.
- Jan 28, 2022 New paper (+ blog post) on experimental design and reinforcement learning in ICLR 2022.
- Jan 1, 2022 New paper (+ website) on large-scale object counting in satellite images, in AAAI 2022 (oral).
- Oct 15, 2021 New paper (+ code) on quantile methods for calibrated uncertainty quantification in NeurIPS 2021.
- Oct 15, 2021 Two papers on explainable machine learning and personalized benchmarking in NeurIPS 2021.
- July 14, 2021 Our paper on Pollux was awarded the Jay Lepreau Best Paper Award at OSDI'21.
- June 10, 2021 New paper (+ website) on Bayesian Algorithm Execution (BAX) and InfoBAX, in ICML 2021.
- June 1, 2021 I co-organized the Machine Learning for Data (Creation, Privacy, Bias) Workshop at ICML 2021.
- Apr 1, 2021 New paper (+ AdaptDL) on Pollux, a deep learning cluster scheduler/tuner, in OSDI 2021.
- Mar 16, 2021 New paper (+ code) on uncertainty quantification with martingales for GPs in ALT 2021.
- Mar 9, 2021 New paper on active classification for catalyst discovery in the Journal of Chemical Physics.
- Jan 12, 2021 New paper (+ code) on a framework for interactive weak supervision in ICLR 2021.
- Dec 22, 2020 Released Uncertainty Toolbox, for predictive UQ, calibration, metrics, and visualization.
- Dec 2, 2020 New paper (+ code) on BANANAS, a method for neural architecture search, in AAAI 2021.
Projects
- Betty An automatic differentiation library for multilevel optimization and meta-learning.
- Bayesian Algorithm Execution (BAX) Extending Bayesian optimization to computable function properties defined by algorithms.
- Uncertainty Toolbox A toolbox for predictive uncertainty quantification, calibration, metrics, and visualization.
- Naszilla A python library for neural architecture search.
- AdaptDL A resource-adaptive cluster scheduler for deep learning training.
- CASL Project An open toolkit for composable, automatic, and scalable learning.
- ProBO A framework for using probabilistic programming in Bayesian optimization.
- Bayesian Optimization and DOE NASBOT for neural architecture search, MPS for design of experiments, and Dragonfly.
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Prior Swapping
Efficient algorithms for incorporating prior information, post-inference.
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Embarrassingly Parallel VI
Communication-free distributed variational inference in nonconjugate models.
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Embarrassingly Parallel MCMC
Asymptotically exact, communication-free distributed
posterior sampling.
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Fast Function-based Regression
Fast distribution-to-real and function-to-function nonparametric regression.
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GPU for Time-varying PYPs
Generalized Polya urn for time-varying Pitman-Yor processes.
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Parallel Frank-Wolfe Optimization
Asynchronous parallel block-coordinate Frank-Wolfe optimization algorithm.
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LRO Models for Link Prediction
Latent random offset model for interpretable citation prediction and exploration.
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DDP Object Tracking and Modeling
Dependent Dirichlet process mixtures for unsupervised object detection and tracking.
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Cell Motility Analysis
TIAM: the tool for integrative analysis of cell motility.