I am a machine learning researcher at Max Planck Institute for Intelligent Systems, Tübingen. In my PhD study, I was fortunate to be supervised by 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 time at Georgia Tech, Google Brain, Nvidia Research, and MERL.
I work primarily on principled modeling of inductive bias in learning algorithms. My research seeks to understand how inductive bias affects generalization, and to develop "light-yet-sweet" learning algorithms: (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 (causality, discrete knowledge) and how they can benefit generalization as a guiding principle. Recently, I have begun rethinking inductive bias in the era of foundation models, and developed a deep interest in large language models and generative modeling across visual, textual, and physical domains. More specifically, my current research focuses on (i) developing principled algorithms for training/adapting foundation models, and (ii) understanding how LLMs perform reasoning and eliciting it in formal/verifiable scenarios (math/symbolic reasoning).
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 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):
- Zeju Qiu (2024 - now)
- Ph.D. student at MPI for Intelligent Systems
- Tim Z. Xiao (2024 - now)
- Ph.D. student at University of Tübingen
VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models
Muchao Ye, Weiyang Liu, Pan He
CVPR 2025
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu, Tim Z. Xiao*, Weiyang Liu*, Yoshua Bengio, Dinghuai Zhang
ICLR 2025
arXiv | code | project | bib
Can Large Language Models Understand Symbolic Graphics Programs?
Zeju Qiu*, Weiyang Liu*, Haiwen Feng*, Zhen Liu**, Tim Z. Xiao**, Katherine M. Collins**, Joshua B. Tenenbaum, Adrian Weller, Michael J. Black, Bernhard Schölkopf
ICLR 2025 Spotlight
arXiv | code | project | openreview | bib
Verbalized Machine Learning: Revisiting Machine Learning with Language Models
Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu*
TMLR 2025
ICML 2024 workshop on In-context Learning
ICML 2024 workshop on LLMs and Cognition
arXiv | code | tutorial | openreview | talk | bib
Re-Thinking Inverse Graphics with Large Language Models
Peter Kulits*, Haiwen Feng*, Weiyang Liu, Victoria Abrevaya, Michael J. Black
TMLR 2024 Selected for presentation at ICLR 2025
arXiv | code | project | bib
Show selected / all by date / all by topic: Value-based Inductive Bias Modeling, Data-centric Learning, Generative Modeling
Verbalized Machine Learning: Revisiting Machine Learning with Language Models
Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu*
TMLR 2025
ICML 2024 workshop on In-context Learning
ICML 2024 workshop on LLMs and Cognition
arXiv | code | tutorial | openreview | talk | bib
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Weiyang Liu*, Zeju Qiu*, Yao Feng**, Yuliang Xiu**, Yuxuan Xue**, Longhui Yu**, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf
ICLR 2024
arXiv | code | project | huggingface | openreview | talk | slides | bib
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Longhui Yu, Weisen Jiang, Han Shi, J. Yu, Z. Liu, Yu Zhang, James Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu*
ICLR 2024 Spotlight
arXiv | code | project | huggingface | openreview | bib
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
Zeju Qiu*, Weiyang Liu*, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf
NeurIPS 2023
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap
Weiyang Liu*, Longhui Yu*, Adrian Weller, Bernhard Schölkopf
ICLR 2023
MeshDiffusion: Score-based Generative 3D Mesh Modeling
Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, Weiyang Liu*
Structural Causal 3D Reconstruction
Weiyang Liu*, Zhen Liu*, Liam Paull, Adrian Weller, Bernhard Schölkopf
ECCV 2022
SphereFace Revived: Unifying Hyperspherical Face Recognition
Weiyang Liu*, Yandong Wen*, Bhiksha Raj, Rita Singh, Adrian Weller
TPAMI 2022
arXiv | code | opensphere | IEEE Xplore | bib
SphereFace2: Binary Classification is All You Need for Deep Face Recognition
Yandong Wen*, Weiyang Liu*, Adrian Weller, Bhiksha Raj, Rita Singh
ICLR 2022 Spotlight
Iterative Teaching by Label Synthesis
Weiyang Liu*, Zhen Liu*, Hanchen Wang*, Liam Paull, Bernhard Schölkopf, Adrian Weller
NeurIPS 2021 Spotlight
Learning with Hyperspherical Uniformity
Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller
AISTATS 2021
Orthogonal Over-Parameterized Training
Weiyang Liu*, Rongmei Lin*, Zhen Liu, James Rehg, Liam Paull, Li Xiong, Le Song, Adrian Weller
CVPR 2021 Oral
Learning towards Minimum Hyperspherical Energy
Weiyang Liu*, Rongmei Lin*, Zhen Liu*, Lixin Liu*, Zhiding Yu, Bo Dai, Le Song
NeurIPS 2018
Deep Hyperspherical Learning
Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song
NIPS 2017 Spotlight
Iterative Machine Teaching
Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Yu Chen, Linda Smith, James Rehg, Le Song
ICML 2017
SphereFace: Deep Hypersphere Embedding for Face Recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song
CVPR 2017 Paper Digest Most Influential Paper