Weiyang Liu


Google Scholar   Github   Twitter


University of Cambridge
Max Planck Institute for Intelligent Systems

About Me

I conduct research at Cambridge and MPI Tübingen with Adrian Weller and Bernhard Schölkopf. Previously, I spent wonderful years at Georgia Tech. I have also spent time at Google Brain, Nvidia, 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. More recently, I become very passionate about foundation models (how to simulate human-level intelligence) and 3D/4D generative modeling (how to recreate and simulate the physical world).

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 am on the academic job market this upcoming year. Feel free to reach out if there is a good fit!

    - Focus on creating novel ideas, not publishing papers
    - Follow curiosity and passion, not trends
    - Ideas are not owned, but come with debts to those who came before
    - Ideas become stronger when shared, discussed and criticized
    - Life is surprisingly short, so solve problems that interest and excite you most. Most importantly, have fun!
    - It is good to be quick, but it is more important to be deep
    - Think like an amateur, do as an expert
    - This is not only about how to do research, but also how to live your life


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 always learn from and get inspired by them. I am fortunate to work with (alphabetical order):

   - Zhen Liu (PhD student at University of Montreal)
   - Zeju Qiu (Master student at Technical University of Munich)
   - Longhui Yu (Master student at Peking University)

Recent Highlight

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

arXiv | code | project | bib

      title={Controlling Text-to-Image Diffusion by Orthogonal Finetuning},
      author={Qiu, Zeju and Liu, Weiyang and Feng, Haiwen and Xue, Yuxuan and Feng, Yao 
        and Liu, Zhen and Zhang, Dan and Weller, Adrian and Schölkopf, Bernhard},

Pairwise Similarity Learning is SimPLE
Yandong Wen*, Weiyang Liu*, Yao Feng, Bhiksha Raj, Rita Singh, Adrian Weller, Michael J. Black, Bernhard Schölkopf

ICCV 2023

arXiv | code | bib

      title={Pairwise Similarity Learning is SimPLE},
      author={Wen, Yandong and Liu, Weiyang and Feng, Yao and Raj, Bhiksha and Singh, Rita
        and Weller, Adrian and Black, Michael and Schölkopf, Bernhard},


Last updated on 12th August 2023.
This site has been visisted Free Web Counter times in total.