| Advances in Variational Inference C Zhang, J Butepage, H Kjellstrom, S Mandt IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018 | 813 | 2018 |
| Generalization in reinforcement learning with selective noise injection and information bottleneck M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann Advances in neural information processing systems 32, 2019 | 176 | 2019 |
| EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE C Ma, S Tschiatschek, K Palla, JMH Lobato, S Nowowzin, C Zhang ICML, 2019 | 137 | 2019 |
| How Do Fair Decisions Fare in Long-term Qualification? R Tu, X Zhang, Y Liu, H Kjellström, M Liu, K Zhang, C Zhang Thirty-fourth Conference on Neural Information Processing Systems, 2020 | 89* | 2020 |
| Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification C Zhang, H Kjellström, S Mandt UAI, 2017 | 86* | 2017 |
| A Causal View on Robustness of Neural Networks C Zhang, K Zhang, Y Li | 79 | 2020 |
| Instructions and guide for diagnostic questions: The neurips 2020 education challenge Z Wang, A Lamb, E Saveliev, P Cameron, Y Zaykov, ... arXiv preprint arXiv:2007.12061, 2020 | 74 | 2020 |
| Vaem: a deep generative model for heterogeneous mixed type data C Ma, S Tschiatschek, R Turner, JM Hernández-Lobato, C Zhang Advances in Neural Information Processing Systems 33, 11237-11247, 2020 | 72 | 2020 |
| Causal discovery in the presence of missing data R Tu, C Zhang, P Ackermann, C Glymour, H Kjellström, K Zhang AISTATS, 2019 | 72 | 2019 |
| Deep end-to-end causal inference T Geffner, J Antoran, A Foster, W Gong, C Ma, E Kiciman, A Sharma, ... arXiv preprint arXiv:2202.02195, 2022 | 68 | 2022 |
| A hierarchical grocery store image dataset with visual and semantic labels M Klasson, C Zhang, H Kjellstrom IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 | 67 | 2019 |
| Icebreaker: Element-wise efficient information acquisition with a bayesian deep latent gaussian model W Gong, S Tschiatschek, S Nowozin, RE Turner, JM Hernández-Lobato, ... Advances in neural information processing systems 32, 2019 | 48 | 2019 |
| Active Mini-Batch Sampling using Repulsive Point Processes C Zhang, C Öztireli, S Mandt, G Salvi The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI),, 2019 | 47 | 2019 |
| Perturbative Black Box Variational Inference C Zhang*, R Bamler*, M Opper, S Mandt* NIPS, 2017 | 40* | 2017 |
| Hide-and-seek privacy challenge: Synthetic data generation vs. patient re-identification J Jordon, D Jarrett, E Saveliev, J Yoon, P Elbers, P Thoral, A Ercole, ... NeurIPS 2020 Competition and Demonstration Track, 206-215, 2021 | 39 | 2021 |
| Partial VAE for Hybrid Recommender System C Ma, W Gong, JM Hernandez-Lobato, N Koenigstein, S Nowozin, ... NIPS Workshop on Bayesian Deep Learning, 2018 | 36 | 2018 |
| Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation R Tu, K Zhang, BC Bertilson, H Kjellstöm, C Zhang NeurIPS, 2019 | 34 | 2019 |
| Identifiable Generative Models for Missing Not at Random Data Imputation C Ma, C Zhang NeurIPS, 2021 | 31 | 2021 |
| Interpretable outcome prediction with sparse Bayesian neural networks in intensive care H Overweg, AL Popkes, A Ercole, Y Li, JM Hernández-Lobato, Y Zaykov, ... arXiv preprint arXiv:1905.02599, 2019 | 31 | 2019 |
| Understanding causality with large language models: Feasibility and opportunities C Zhang, S Bauer, P Bennett, J Gao, W Gong, A Hilmkil, J Jennings, C Ma, ... arXiv preprint arXiv:2304.05524, 2023 | 30 | 2023 |
Hedvig Kjellström (Sidenbladh)Professor at KTH Royal Institute of Technology, Sweden在 kth.se 的电子邮件经过验证
