Kaiwen Wu
PhD Student
Computer and Information Science
University of Pennsylvania
Email: kaiwenwu@seas.upenn.edu
Links to
GitHub,
Twitter,
Google Scholar
How to pronounce my first name
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About Me
I am a third-year PhD student in computer and information science at the University of Pennsylvania, advised by Jacob Gardner.
Previously, I completed my MMath degree in computer science at the University of Waterloo, where I worked on trustworthy machine learning with Yaoliang Yu.
I did my undergraduate at Nanjing University.
I am interested in machine learning and optimization.
My recent work focuses on scaling up computation in probabilistic machine learning.
Specifically, I work on Gaussian processes, variational inference, and Bayesian optimization.
I have side interests in convex optimization and deep generative modeling.
Publications
* indicates equal contribution. See Google Scholar for a complete list of publications.
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Understanding Stochastic Natural Gradient Variational Inference
Kaiwen Wu and Jacob R. Gardner
International Conference on Machine Learning (ICML 2024)
[arXiv]
[code]
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Large-Scale Gaussian Processes via Alternating Projection
Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss and Jacob R. Gardner
International Conference on Artificial Intelligence and Statistics (AISTATS 2024)
[arXiv]
[code]
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The Behavior and Convergence of Local Bayesian Optimization
Kaiwen Wu, Kyurae Kim, Roman Garnett and Jacob R. Gardner
Advances in Neural Information Processing Systems (NeurIPS 2023)
Spotlight Presentation
[arXiv]
[code]
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On the Convergence of Black-Box Variational Inference
Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma and Jacob R. Gardner
Advances in Neural Information Processing Systems (NeurIPS 2023)
[arXiv]
[code]
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Local Bayesian Optimization via Maximizing Probability of Descent
Quan Nguyen*, Kaiwen Wu*, Jacob R. Gardner and Roman Garnett
Advances in Neural Information Processing Systems (NeurIPS 2022)
Oral Presentation
[arXiv]
[code]
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Newton-type Methods for Minimax Optimization
Guojun Zhang, Kaiwen Wu, Pascal Poupart and Yaoliang Yu
ICML 21 Workshop on Beyond First-Order Methods in ML Systems
[arXiv]
[code]
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Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu, Allen Houze Wang and Yaoliang Yu
International Conference on Machine Learning (ICML 2020)
[arXiv]
[code]
-
On Minimax Optimality of GANs for Robust Mean Estimation
Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang and Yaoliang Yu
International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
[proceedings]
[code]
Miscellaneous
I have a blog.
I write notes when I have time.
My writing pet peeves
- Leave latex compilation errors unfixed in overleaf. It is a felony.
- Orphans.
- It's UPenn, not Upenn.
I have reviewed (or will review) for the following conferences: AAAI 2021, AISTATS 2021, ICML 2023, NeurIPS 2023, ICLR 2024, AISTATS 2024, ICML 2024, NeurIPS 2024