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

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.

• Large-Scale Gaussian Processes via Alternating Projection
  Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss and Jacob R. Gardner
  Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024)
  [arXiv] [code]


• The Behavior and Convergence of Local Bayesian Optimization
  Kaiwen Wu, Kyurae Kim, Roman Garnett and Jacob R. Gardner
  Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
  Spotlight Presentation
  [arXiv] [code]


• 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 36 (NeurIPS 2023)
  [arXiv] [code]


• Local Bayesian Optimization via Maximizing Probability of Descent
  Quan Nguyen*, Kaiwen Wu*, Jacob R. Gardner and Roman Garnett
  Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
  Oral Presentation
  [arXiv] [code]


• 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]


• Stronger and Faster Wasserstein Adversarial Attacks
  Kaiwen Wu, Allen Houze Wang and Yaoliang Yu
  Proceedings of the 37th 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
  Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
  [proceedings] [code]

Miscellaneous

I have reviewed (or will review) for the following conferences: AAAI 2021, AISTATS 2021, ICML 2023, NeurIPS 2023, ICLR 2024, AISTATS 2024, ICML 2024.

I write notes when I have time.

• Concepts in Smooth Manifolds and Differential Geometry
• Normalization in GP
• L1 Norm Projection

A useful website calculating (an upper bound of) the Erdős number.