Weiyang Liu
University of Cambridge
Max Planck Institute for Intelligent Systems
I am currently conducting research at Cambridge and MPI Tübingen with Adrian Weller and Bernhard Schölkopf. As a member of the advising team at MeshCapade, I also work closely with Michael J. Black. Previously, I spent wonderful years at Georgia Tech. I have also spent time at Google Brain, Nvidia Research, and MERL.
I work on principled modeling of inductive bias in machine learning. My research seeks to understand how inductive bias determines generalization, and to develop "light-yet-sweet" generalizable models: (i) light: conceptually simple in methodology and easy to implement in practice, (ii) sweet: having clear intuitions and non-trivial theoretical guarantees.
Over the years, I always find myself fascinated by geometric invariance, symmetry, structures (graph, causality) and how they can benefit generalization as a guiding principle. More recently, I become very passionate about foundation models (mental intelligence) and generative modeling of the physicual world (physical intelligence).
I always believe in two principles in my research: (i) insight must precede application, and (ii) everything should be made as simple as possible, but not simpler. I try to follow certain research values.
I will recruit PhD students and RAs from 2025 Fall. Feel free to drop me an email.
I take great pleasure to (co-)mentor a few talented and highly motivated students. Mentoring and working with junior students is truely a privilege, and I constantly learn from and get inspired by them. I am fortunate to work with (time-wise order):
- Yamei Chen (2024 - now)
- M.S. student at Technical University of Munich
- Zeju Qiu (2024 - now)
- Ph.D. student at MPI for Intelligent Systems
- Tim Z. Xiao (2024 - now)
- Ph.D. student at University of Tübingen
- Gege Gao (2023 - now)
- Ph.D. student at University of Tübingen
Alumni list (nothing is more rewarding than seeing my mentees succeed)
Verbalized Machine Learning: Revisiting Machine Learning with Language Models
Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu
Preprint 2024
Representational Alignment Supports Effective Machine Teaching
Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya, Nori Jacoby, Weiyang Liu, Theodore R. Sumers, Michalis Korakakis, Umang Bhatt, Mark Ho, Joshua B. Tenenbaum, Bradley C. Love, Zachary A. Pardos, Adrian Weller, Thomas L. Griffiths
Preprint 2024