| Representation learning on graphs with jumping knowledge networks K Xu, C Li, Y Tian, T Sonobe, K Kawarabayashi, S Jegelka International conference on machine learning, 5453-5462, 2018 | 1993 | 2018 |
| Retrosynthesis prediction with conditional graph logic network H Dai, C Li, C Coley, B Dai, L Song Advances in Neural Information Processing Systems 32, 2019 | 167 | 2019 |
| Batched high-dimensional bayesian optimization via structural kernel learning Z Wang, C Li, S Jegelka, P Kohli Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 129 | 2017 |
| Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction W Lu, Q Wu, J Zhang, J Rao, C Li, S Zheng Advances in neural information processing systems 35, 7236-7249, 2022 | 96 | 2022 |
| Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search B Chen, C Li, H Dai, L Song International Conference on Machine Learning, 1608-1616, 2020 | 92 | 2020 |
| Fast dpp sampling for nystrom with application to kernel methods C Li, S Jegelka, S Sra International Conference on Machine Learning, 2061-2070, 2016 | 90 | 2016 |
| Efficient sampling for k-determinantal point processes C Li, S Jegelka, S Sra Artificial Intelligence and Statistics, 1328-1337, 2016 | 60 | 2016 |
| Improving Sequential Determinantal Point Processes for Supervised Video Summarization A Sharghi, A Borji, C Li, T Yang, B Gong Proceedings of the European Conference on Computer Vision (ECCV), 517-533, 2018 | 56 | 2018 |
| Randomized greedy inference for joint segmentation, POS tagging and dependency parsing Y Zhang, C Li, R Barzilay, K Darwish Proceedings of the 2015 Conference of the North American Chapter of the …, 2015 | 55 | 2015 |
| Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling C Li, S Sra, S Jegelka Advances in Neural Information Processing Systems, 4188-4196, 2016 | 48* | 2016 |
| Polynomial time algorithms for dual volume sampling C Li, S Jegelka, S Sra Advances in Neural Information Processing Systems 30, 2017 | 46* | 2017 |
| Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP S Zheng, T Zeng, C Li, B Chen, CW Coley, Y Yang, R Wu Nature Communications 13 (1), 3342, 2022 | 45 | 2022 |
| Distributional Adversarial Networks C Li, D Alvarez-Melis, K Xu, S Jegelka, S Sra arXiv preprint arXiv:1706.09549, 2017 | 42* | 2017 |
| Accelerated rational PROTAC design via deep learning and molecular simulations S Zheng, Y Tan, Z Wang, C Li, Z Zhang, X Sang, H Chen, Y Yang Nature Machine Intelligence 4 (9), 739-748, 2022 | 41 | 2022 |
| Structure-aware multimodal deep learning for drug–protein interaction prediction P Wang, S Zheng, Y Jiang, C Li, J Liu, C Wen, A Patronov, D Qian, ... Journal of chemical information and modeling 62 (5), 1308-1317, 2022 | 39 | 2022 |
| Neural program lattices C Li, D Tarlow, AL Gaunt, M Brockschmidt, N Kushman International conference on learning representations, 2016 | 37* | 2016 |
| Sentiment topic model with decomposed prior C Li, J Zhang, JT Sun, Z Chen Proceedings of the 2013 SIAM International Conference on Data Mining, 767-775, 2013 | 34 | 2013 |
| Gaussian quadrature for matrix inverse forms with applications C Li, S Sra, S Jegelka International Conference on Machine Learning, 1766-1775, 2016 | 21 | 2016 |
| Bayesian max-margin multi-task learning with data augmentation C Li, J Zhu, J Chen International Conference on Machine Learning, 415-423, 2014 | 19 | 2014 |
| Structured output learning with candidate labels for local parts C Li, J Zhang, Z Chen Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 6 | 2013 |
Stefanie JegelkaTUM and MITVerified email at mit.edu
