| A survey on generative diffusion model H Cao, C Tan, Z Gao, Y Xu, G Chen, PA Heng, SZ Li arXiv preprint arXiv:2209.02646, 2022 | 382 | 2022 |
| L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise Y Xu, P Cao, Y Kong, Y Wang Advances in neural information processing systems 32, 2019 | 305* | 2019 |
| A theory of usable information under computational constraints Y Xu, S Zhao, J Song, R Stewart, S Ermon International Conference on Learning Representations (ICLR 2020), 2020 | 171 | 2020 |
| Can subnetwork structure be the key to out-of-distribution generalization? D Zhang, K Ahuja, Y Xu, Y Wang, A Courville International conference on machine learning, 12356-12367, 2021 | 105 | 2021 |
| Poisson Flow Generative Models Y Xu, Z Liu, M Tegmark, T Jaakkola Advances in Neural Information Processing Systems 35, 16782-16795, 2022 | 95 | 2022 |
| PFGM++: Unlocking the potential of physics-inspired generative models Y Xu, Z Liu, Y Tian, S Tong, M Tegmark, T Jaakkola International Conference on Machine Learning (ICML), 2023, 2023 | 61 | 2023 |
| Restart Sampling for Improving Generative Processes Y Xu, M Deng, X Cheng, Y Tian, Z Liu, T Jaakkola Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023 | 56 | 2023 |
| Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds P Cao, Y Xu, Y Kong, Y Wang International Conference on Learning Representations (ICLR 2019), 2018 | 56 | 2018 |
| Learning representations that support robust transfer of predictors Y Xu, T Jaakkola arXiv preprint arXiv:2110.09940, 2021 | 30 | 2021 |
| Stable target field for reduced variance score estimation in diffusion models Y Xu, S Tong, T Jaakkola International Conference on Learning Representations (ICLR 2023), 2023 | 25 | 2023 |
| Controlling directions orthogonal to a classifier Y Xu, H He, T Shen, T Jaakkola International Conference on Learning Representations (ICLR 2022), 2022 | 25 | 2022 |
| Tcgm: An information-theoretic framework for semi-supervised multi-modality learning X Sun, Y Xu, P Cao, Y Kong, L Hu, S Zhang, Y Wang Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 25 | 2020 |
| Particle Guidance: non-iid diverse sampling with diffusion models G Corso, Y Xu, V De Bortoli, R Barzilay, T Jaakkola arXiv preprint arXiv:2310.13102, 2023 | 23 | 2023 |
| Anytime sampling for autoregressive models via ordered autoencoding Y Xu, Y Song, S Garg, L Gong, R Shu, A Grover, S Ermon International Conference on Learning Representations (ICLR 2021), 2021 | 22 | 2021 |
| GenPhys: From physical processes to generative models Z Liu, D Luo, Y Xu, T Jaakkola, M Tegmark arXiv preprint arXiv:2304.02637, 2023 | 11 | 2023 |
| DisCo-Diff: Enhancing continuous diffusion models with discrete latents Y Xu, G Corso, T Jaakkola, A Vahdat, K Kreis arXiv preprint arXiv:2407.03300, 2024 | 9 | 2024 |
| Energy-based diffusion language models for text generation M Xu, T Geffner, K Kreis, W Nie, Y Xu, J Leskovec, S Ermon, A Vahdat arXiv preprint arXiv:2410.21357, 2024 | 8 | 2024 |
| Heavy-tailed diffusion models K Pandey, J Pathak, Y Xu, S Mandt, M Pritchard, A Vahdat, M Mardani arXiv preprint arXiv:2410.14171, 2024 | 4 | 2024 |
| Truncated Consistency Models S Lee, Y Xu, T Geffner, G Fanti, K Kreis, A Vahdat, W Nie arXiv preprint arXiv:2410.14895, 2024 | 1 | 2024 |
| Think While You Generate: Discrete Diffusion with Planned Denoising S Liu, J Nam, A Campbell, H Stärk, Y Xu, T Jaakkola, ... arXiv preprint arXiv:2410.06264, 2024 | 1 | 2024 |
Tommi JaakkolaMITVerified email at csail.mit.edu
