| Learning to Parse Wireframes in Images of Man-Made Environments K Huang, Y Wang, Z Zhou, T Ding, S Gao, Y Ma IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 213 | 2018 |
| Noisy Dual Principal Component Pursuit T Ding*, Z Zhu*, T Ding, Y Yang, R Vidal, MC Tsakiris, DP Robinson International Conference on Machine Learning (ICML), 2019 | 24 | 2019 |
| Robust Homography Estimation via Dual Principal Component Pursuit T Ding, Y Yang, Z Zhu, DP Robinson, R Vidal, L Kneip, MC Tsakiris IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 | 21 | 2020 |
| Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models T Chu*, S Tong*, T Ding*, X Dai, BD Haeffele, R Vidal, Y Ma International Conference on Learning Representations (ICLR), 2024 | 10 | 2024 |
| Efficient Maximal Coding Rate Reduction by Variational Forms C Baek*, Z Wu*, KHR Chan, T Ding, Y Ma, BD Haeffele IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 10 | 2022 |
| Understanding Doubly Stochastic Clustering T Ding, D Lim, R Vidal, BD Haeffele International Conference on Machine Learning (ICML), 2022 | 9 | 2022 |
| Unsupervised Manifold Linearizing and Clustering T Ding, S Tong, KHR Chan, X Dai, Y Ma, BD Haeffele IEEE International Conference on Computer Vision (ICCV), 2023 | 8 | 2023 |
| PaCE: Parsimonious Concept Engineering for Large Language Models J Luo*, T Ding*, KHR Chan, D Thaker, A Chattopadhyay, ... arXiv preprint arXiv:2406.04331, 2024 | 3 | 2024 |
| HARD: Hyperplane ARrangement Descent T Ding*, L Peng*, R Vidal Conference on Parsimony and Learning (CPAL), 2024 | | 2024 |
| Boosting RANSAC via Dual Principal Component Pursuit Y Yang, X Zhang, T Ding, DP Robinson, R Vidal, MC Tsakiris arXiv preprint arXiv:2110.02918, 2021 | | 2021 |
| Geometric Analysis of Nonlinear Manifold Clustering N Shinde, T Ding, D Robinson, R Vidal The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0 | | |
| Outlier-Robust Orthogonal Regression on Manifolds T Ding, L Peng, R Vidal | | |
Rene VidalRachleff University Professor, University of Pennsylvania; Amazon Scholar; Chief Scientist, NORCEVerified email at seas.upenn.edu
