Papers

2025

1. 

   Concept Lancet: Representation Decomposition and Transplant for Diffusion-Based Image Editing

   Jinqi Luo, TD, Kwan Ho Ryan Chan, Hancheng Min, Chris Callison-Burch, and René Vidal

   In Under Review, 2025

   Cite

   @inproceedings{luo2025concept,
     title = {Concept Lancet: Representation Decomposition and Transplant for Diffusion-Based Image Editing},
     author = {Luo, Jinqi and Ding, Tianjiao and Chan, Kwan Ho Ryan and Min, Hancheng and Callison-Burch, Chris and Vidal, Ren{\'e}},
     booktitle = {Under Review},
     year = {2025},
     tags = {trustworthy-ml;foundation-models;representation-learning}
   }

2. 

   Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction

   Ziyang Wu, TD^*, Yifu Lu^*, Druv Pai, Jingyuan Zhang, Weida Wang, Yaodong Yu, Yi Ma, and Benjamin D. Haeffele

   In Under Review, 2025

   Top 1.4% submissions Paper Cite

   Reviewer ratings: 8,8,8,6 out of 10, top 162/11670 ≈ 1.4% of all submissions

   @inproceedings{wu2025token,
     title = {Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction},
     author = {Wu, Ziyang and Ding, Tianjiao and Lu, Yifu and Pai, Druv and Zhang, Jingyuan and Wang, Weida and Yu, Yaodong and Ma, Yi and Haeffele, Benjamin D.},
     booktitle = {Under Review},
     year = {2025},
     tags = {efficient-ml;optimization;foundation-models;theory}
   }

2024

1. 

   PaCE: Parsimonious Concept Engineering for Large Language Models

   Jinqi Luo^*, TD^*, Kwan Ho Ryan Chan, Darshan Thaker, Aditya Chattopadhyay, Chris Callison-Burch, and René Vidal

   In Annual Conference on Neural Information Processing Systems, 2024

   Paper Video Code Cite

   @inproceedings{luo2024pace,
     title = {PaCE: Parsimonious Concept Engineering for Large Language Models},
     author = {Luo, Jinqi and Ding, Tianjiao and Chan, Kwan Ho Ryan and Thaker, Darshan and Chattopadhyay, Aditya and Callison-Burch, Chris and Vidal, Ren{\'e}},
     booktitle = {Annual Conference on Neural Information Processing Systems},
     year = {2024},
     tags = {trustworthy-ml;efficient-ml;foundation-models;representation-learning}
   }

2. 

   Geometric Analysis of Nonlinear Manifold Clustering

   Nimita Shinde^*, TD^*, Daniel P. Robinson, and René Vidal

   In Annual Conference on Neural Information Processing Systems, 2024

   Top 3% posters Paper Cite

   Reviewer ratings: 8,7,6 out of 10, top 117/3650 ≈ 3% of posters

   @inproceedings{shinde2024geometric,
     title = {Geometric Analysis of Nonlinear Manifold Clustering},
     author = {Shinde, Nimita and Ding, Tianjiao and Robinson, Daniel P. and Vidal, Ren{\'e}},
     booktitle = {Annual Conference on Neural Information Processing Systems},
     year = {2024},
     tags = {unsupervised-ml;theory;optimization}
   }

3. 

   Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models

   Tianzhe Chu^*, Shengbang Tong^*, TD^*, Xili Dai, Benjamin D. Haeffele, René Vidal, and Yi Ma

   In International Conference on Learning Representations, 2024

   Paper Code Cite

   @inproceedings{chu2024image,
     title = {Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models},
     author = {Chu, Tianzhe and Tong, Shengbang and Ding, Tianjiao and Dai, Xili and Haeffele, Benjamin D. and Vidal, Ren{\'e} and Ma, Yi},
     booktitle = {International Conference on Learning Representations},
     year = {2024},
     tags = {unsupervised-ml;foundation-models;representation-learning}
   }

4. 

   HARD: Hyperplane ARrangement Descent

   TD^*, Liangzu Peng^*, and René Vidal

   In Conference on Parsimony and Learning, 2024

   Oral Paper Code Cite

   Oral presentation

   @inproceedings{ding2024hard,
     title = {HARD: Hyperplane ARrangement Descent},
     author = {Ding, Tianjiao and Peng, Liangzu and Vidal, Ren{\'e}},
     booktitle = {Conference on Parsimony and Learning},
     year = {2024},
     tags = {trustworthy-ml;unsupervised-ml;theory;optimization}
   }

2023

1. 

   Outlier-Robust Orthogonal Regression on Manifolds

   TD^*, Liangzu Peng^*, and René Vidal

   In OpenReview, 2023

   Paper Cite

   @inproceedings{ding2023outlier,
     title = {Outlier-Robust Orthogonal Regression on Manifolds},
     author = {Ding, Tianjiao and Peng, Liangzu and Vidal, Ren{\'e}},
     booktitle = {OpenReview},
     year = {2023},
     tags = {trustworthy-ml;efficient-ml;unsupervised-ml;theory;geometric-vision;optimization}
   }

2. 

   Unsupervised Manifold Linearizing and Clustering

   TD, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, and Benjamin D. Haeffele

   In International Conference on Computer Vision, 2023

   CPAL Spotlight Paper Cite

   CPAL Spotlight

   @inproceedings{ding2023unsupervised,
     title = {Unsupervised Manifold Linearizing and Clustering},
     author = {Ding, Tianjiao and Tong, Shengbang and Chan, Kwan Ho Ryan and Dai, Xili and Ma, Yi and Haeffele, Benjamin D.},
     booktitle = {International Conference on Computer Vision},
     year = {2023},
     tags = {unsupervised-ml;representation-learning}
   }

2022

1. 

   Understanding Doubly Stochastic Clustering

   TD, Derek Lim, René Vidal, and Benjamin D. Haeffele

   In International Conference on Machine Learning, 2022

   Oral Paper Cite

   Oral presentation

   @inproceedings{ding2022understanding,
     title = {Understanding Doubly Stochastic Clustering},
     author = {Ding, Tianjiao and Lim, Derek and Vidal, Ren{\'e} and Haeffele, Benjamin D.},
     booktitle = {International Conference on Machine Learning},
     year = {2022},
     tags = {unsupervised-ml;trustworthy-ml;optimization;theory}
   }

2. 

   Efficient Maximal Coding Rate Reduction by Variational Forms

   Christina Baek^*, Ziyang Wu^*, Kwan Ho Ryan Chan, TD, Yi Ma, and Benjamin D. Haeffele

   In IEEE Conference on Computer Vision and Pattern Recognition, 2022

   Paper Cite

   @inproceedings{baek2022efficient,
     title = {Efficient Maximal Coding Rate Reduction by Variational Forms},
     author = {Baek, Christina and Wu, Ziyang and Chan, Kwan Ho Ryan and Ding, Tianjiao and Ma, Yi and Haeffele, Benjamin D.},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
     year = {2022},
     tags = {efficient-ml;optimization;theory;representation-learning}
   }

2021

1. 

   Boosting RANSAC via Dual Principal Component Pursuit

   Yunchen Yang, Xinyue Zhang, TD, Daniel P. Robinson, René Vidal, and Manolis C. Tsakiris

   arXiv preprint arXiv:2110.02918, 2021

   Paper Cite

   @article{yang2021boosting,
     title = {Boosting RANSAC via Dual Principal Component Pursuit},
     author = {Yang, Yunchen and Zhang, Xinyue and Ding, Tianjiao and Robinson, Daniel P. and Vidal, Ren{\'e} and Tsakiris, Manolis C.},
     journal = {arXiv preprint arXiv:2110.02918},
     year = {2021},
     tags = {trustworthy-ml;efficient-ml;geometric-vision}
   }

2020

1. 

   Robust Homography Estimation via Dual Principal Component Pursuit

   TD, Yunchen Yang, Zhihui Zhu, Daniel P. Robinson, René Vidal, Laurent Kneip, and Manolis C. Tsakiris

   In IEEE Conference on Computer Vision and Pattern Recognition, 2020

   Paper Video Code Cite

   @inproceedings{ding2020robust,
     title = {Robust Homography Estimation via Dual Principal Component Pursuit},
     author = {Ding, Tianjiao and Yang, Yunchen and Zhu, Zhihui and Robinson, Daniel P. and Vidal, Ren{\'e} and Kneip, Laurent and Tsakiris, Manolis C.},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
     pages = {6080--6089},
     year = {2020},
     tags = {trustworthy-ml;efficient-ml;unsupervised-ml;geometric-vision;theory;optimization}
   }

2019

1. 

   Noisy Dual Principal Component Pursuit

   Tianyu Ding^*, Zhihui Zhu^*, TD, Yunchen Yang, Daniel P. Robinson, Manolis C. Tsakiris, and René Vidal

   In International Conference on Machine Learning, 2019

   Oral Paper Code Cite

   Oral presentation

   @inproceedings{ding2019noisy,
     title = {Noisy Dual Principal Component Pursuit},
     author = {Ding, Tianyu and Zhu, Zhihui and Ding, Tianjiao and Yang, Yunchen and Robinson, Daniel P. and Tsakiris, Manolis C. and Vidal, Ren{\'e}},
     booktitle = {International Conference on Machine Learning},
     pages = {1617--1625},
     year = {2019},
     tags = {unsupervised-ml;efficient-ml;trustworthy-ml;geometric-vision;optimization;theory}
   }

2018

1. 

   Learning to Parse Wireframes in Images of Man-Made Environments

   Kun Huang, Yifan Wang, Zihan Zhou, TD, Shenghua Gao, and Yi Ma

   In IEEE Conference on Computer Vision and Pattern Recognition, 2018

   Paper Code Cite

   @inproceedings{huang2018learning,
     title = {Learning to Parse Wireframes in Images of Man-Made Environments},
     author = {Huang, Kun and Wang, Yifan and Zhou, Zihan and Ding, Tianjiao and Gao, Shenghua and Ma, Yi},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
     pages = {626--635},
     year = {2018},
     tags = {efficient-ml;geometric-vision;representation-learning}
   }