| Wasserstein of Wasserstein Loss for Learning Generative Models Y Dukler, W Li, A Tong Lin, G Montúfar International Conference on Machine Learning 97 (PMLR), 1716-1725, 2019 | 42 | 2019 |
| Optimization Theory for ReLU Neural Networks Trained with Normalization Layers Y Dukler, Q Gu, G Montufar International Conference on Machine Learning 119 (PMLR), 2751-2760, 2020 | 28 | 2020 |
| Theory for undercompressive shocks in tears of wine Y Dukler, H Ji, C Falcon, AL Bertozzi Physical Review Fluids 5 (3), 034002, 2020 | 15 | 2020 |
| Learning Expressive Prompting With Residuals for Vision Transformers R Das, Y Dukler, A Ravichandran, A Swaminathan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 10 | 2023 |
| SAFE: Machine unlearning with shard graphs Y Dukler, B Bowman, A Achille, A Golatkar, A Swaminathan, S Soatto Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 10 | 2023 |
| Automatic valve segmentation in cardiac ultrasound time series data Y Dukler, Y Ge, Y Qian, S Yamamoto, B Yuan, L Zhao, AL Bertozzi, ... Medical Imaging 2018: Image Processing 10574, 105741Y, 2018 | 8 | 2018 |
| Your representations are in the network: composable and parallel adaptation for large scale models Y Dukler, A Achille, H Yang, V Vivek, L Zancato, B Bowman, ... Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 4* | 2023 |
| DIVA: Dataset Derivative of a Learning Task Y Dukler, A Achille, G Paolini, A Ravichandran, M Polito, S Soatto International Conference on Learning Representations (ICLR) 2022, 2022 | 4 | 2022 |
| Wasserstein Diffusion Tikhonov Regularization AT Lin, Y Dukler, W Li, G Montúfar NeurIPS 2019 Workshop on Optimal Transport & Machine Learning (OTML), 2019 | 3 | 2019 |
| Tears of wine and shock dynamics Y Dukler, A Bertozzi, H Ji APS Meeting Abstracts, 2019 | | 2019 |
Guido F. MontufarUCLA, Mathematics and Statistics & Data Science, Max Planck Institute MiSVerified email at math.ucla.edu
