Zhen Liu
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PhD candidate @ Mila and Université de Montréal
Email : [firstname].[lastname].2[at]umontreal.ca
I am a PhD candidate in computer science at Mila and Université de Montréal, advised by Liam Paull and Yoshua Bengio. I received my M.S. and B.S. degrees in computer science with a minor in economics from Georgia Institute of Technology.
I am currently visiting at Max Planck Institute for Intelligent Systems, working with Michael J. Black and Bernhard Schölkopf.
News: I will join School of Data Science at CUHK-Shenzhen and lead CUHK-SZ GenLab as an assistant professor in Spring 2025.
I am actively looking for self-motivated PhD / MPhil students (Spring / Fall '25) and research assistants (see below for research directions). Please fill out the Google Form and then drop me an email through zliuacademia@gmail.com if interested.
I study representations and machine learning methods to build models and agents with
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
Preprint, 2024
PuzzleAvatar: Assembling 3D Avatars from Personal Albums
Yuliang Xiu, Yufei Ye, Zhen Liu, Dimitrios Tzionas, Michael J. Black
ACM Transactions on Graphics (SIGGRAPH Asia), 2024
Ghost on the Shell: An Expressive Representation of General 3D Shapes
Zhen Liu, Yao Feng*, Yuliang Xiu*, Weiyang Liu, Liam Paull, Michael J. Black†, Bernhard Schölkopf†
International Conference on Learning Representations (ICLR), 2024 (Oral)
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
International Conference on Learning Representations (ICLR), 2024
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
Neural Information Processing Systems (NeurIPS), 2023
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron Courville, Alessandro Sordoni
Transactions on Machine Learning Research (TMLR), 2023
MeshDiffusion: Score-based Generative 3D Mesh Modeling
Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, Weiyang Liu
International Conference on Learning Representations (ICLR), 2023 (Notable-top-25%)
Iterative Teaching by Data Hallucination
Zeju Qiu*, Weiyang Liu*, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Continual Learning by Modeling Intra-Class Variation
Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, Weiyang Liu
Transactions on Machine Learning Research (TMLR), 2023
Structural Causal 3D Reconstruction
Weiyang Liu*, Zhen Liu*, Liam Paull, Adrian Weller, Bernhard Schölkopf
European Conference on Computer Vision (ECCV), 2022
Generative Flow Networks for Discrete Probabilistic Modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio
International Conference on Machine Learning (ICML), 2022
Iterative Teaching by Label Synthesis
Weiyang Liu*, Zhen Liu*, Hanchen Wang*, Liam Paull, Bernhard Schölkopf, Adrian Weller.
Neural Information Processing Systems (NeurIPS), 2021 (Spotlight)
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Mingde Zhao*, Zhen Liu*, Sitao Luan*, Shuyuan Zhang*, Doina Precup, Yoshua Bengio.
Neural Information Processing Systems (NeurIPS), 2021
Orthogonal Over-parametrized Training
Weiyang Liu*, Rongmei Lin*, Zhen Liu, James M Rehg, Liam Paull, Li Xiong, Adrian Weller, Le Song.
Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Oral)
Learning with Hyperspherical Uniformity
Weiyang Liu*, Rongmei Lin*, Zhen Liu*, Li Xiong, Bernhard Schölkopf, Adrian Weller.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Neural similarity learning
Weiyang Liu*, Zhen Liu*, James M Rehg, Le Song.
Neural Information Processing Systems (NeurIPS), 2019
Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai*, Zhen Liu*, Hanjun Dai*, Niao He, Arthur Gretton, Le Song, Dale Schuurmans.
Neural Information Processing Systems (NeurIPS), 2019
Coupled Variational Bayes via Optimization Embedding
Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song.
Neural Information Processing Systems (NeurIPS), 2018
Learning towards Minimum Hyperspherical Energy
Weiyang Liu*, Rongmei Lin*, Zhen Liu*, Lixin Liu*, Zhiding Yu, Bo Dai, Le Song.
Neural Information Processing Systems (NeurIPS), 2018
Decoupled Network
Weiyang Liu*, Zhen Liu*, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James Rehg, Le Song.
Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Spotlight)
Towards Black-box Iterative Machine Teaching
Weiyang Liu*, Bo Dai*, Xingguo Li, Zhen Liu, James Rehg, Le Song.
International Conference on Machine Learning (ICML), 2018
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song.
International Conference on Machine Learning (ICML), 2018
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots.
International Conference on Robotics and Automation (ICRA), 2018
One Shot Learning for Semantic Segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots.
The British Machine Vision Conference (BMVC), 2017