Publications

Few-Shot Composition Learning for Image Retrieval with Prompt Tuning
Junda Wu*, Rui Wang*, Handong Zhao, Ruiyi Zhang, Chaochao Lu, Shuai Li, Ricardo Henao. (*Equal Contribution)
in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023
[To appear]

Action-Sufficient State Representation Learning for Control with Structural Constraints
Biwei Huang*, Chaochao Lu*, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang. (*Equal Contribution)
in Proceedings of International Conference on Machine Learning (ICML), 2022
[Paper]

Invariant Causal Representation Learning for Generalization in Imitation and Reinforcement Learning
Chaochao Lu, José Miguel Hernández-Lobato, Bernhard Schölkopf.
in Proceedings of International Conference on Learning Representations (ICLR) Workshop on PAIR2Struct (Oral Presentation), 2022
[Paper]

Invariant Causal Representation Learning for Out-of-Distribution Generalization
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf.
in Proceedings of International Conference on Learning Representations (ICLR), 2022
[Paper]

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang.
in Proceedings of International Conference on Learning Representations (ICLR), 2022
[Paper]

Nonlinear Invariant Risk Minimization: A Causal Approach
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf.
Technical Report, Cambridge Machine Learning Group, 2021
[Paper]

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Chaochao Lu*, Biwei Huang*, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf. (*Equal Contribution)
in Proceedings of Neural Information Processing Systems Workshop on Offline Reinforcement Learning, Virtually, 2020
[Paper]

Interpreting Spatially Infinite Generative Models
Chaochao Lu, Richard Turner, Yingzhen Li, Nate Kushman.
in Proceedings of ICML Workshop on Human Interpretability in Machine Learning (WHI), Virtually, 2020
[Paper] [Video][Generated High-Resolution Samples]

Deconfounding Reinforcement Learning in Observational Settings
Chaochao Lu, Bernhard Schölkopf, José Miguel Hernández-Lobato.
Technical Report, Cambridge Machine Learning Group, 2018
[Paper]

Flexible Spatio-Temporal Networks for Video Prediction
Chaochao Lu, Michael Hirsch, Bernhard Schölkopf.
in Proceedings of IEEE Computer Society Conference on Computer Vision and Patter Recognition (CVPR), Honolulu, Hawaii, USA, 2017 
[Paper]

Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
Chaochao Lu, Xiaoou Tang.
in Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), Austin, Texas, USA, 2015 
Outstanding Student Paper Award
For the first time, the human-level performance in face verification on LFW is surpassed. 
[Paper] [Supplementary] Selected News: [Science News] [Nature News] [Discover] [The Register] [Physics arXiv Blog]
[Tech Xplore] [Technology] [Rootnotion] [I Programmer]

Learning the Face Prior for Bayesian Face Recognition
Chaochao Lu, Xiaoou Tang.
in Proceedings of European Conference on Computer Vision (ECCV), Zurich, Switzerland, 2014 
[Paper] [Poster] [Video]

Face Recognition Using Face Patch Networks
Chaochao Lu, Deli Zhao, Xiaoou Tang.
in Proceedings of IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 2013 
[Paper] [Poster]