| Advances in Variational Inference C Zhang, J Butepage, H Kjellstrom, S Mandt IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018 | 580 | 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 | 117 | 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 | 106 | 2019 |
| Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification C Zhang, H Kjellström, S Mandt UAI, 2017 | 78* | 2017 |
| A Causal View on Robustness of Neural Networks C Zhang, K Zhang, Y Li | 49 | 2020 |
| 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 | 47* | 2020 |
| Causal discovery in the presence of missing data R Tu, C Zhang, P Ackermann, C Glymour, H Kjellström, K Zhang AISTATS, 2019 | 44 | 2019 |
| 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 | 43 | 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 | 38 | 2019 |
| Perturbative Black Box Variational Inference C Zhang*, R Bamler*, M Opper, S Mandt* NIPS, 2017 | 36* | 2017 |
| 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 | 33 | 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 | 33 | 2020 |
| 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 | 32 | 2019 |
| Partial VAE for Hybrid Recommender System C Ma, W Gong, JM Hernandez-Lobato, N Koenigstein, S Nowozin, ... NIPS Workshop on Bayesian Deep Learning, 2018 | 28 | 2018 |
| 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 | 24 | 2019 |
| 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 | 23 | 2021 |
| How to supervise topic models C Zhang, H Kjellström European Conference on Computer Vision (ECCVws 2014, GMCV), Zurich …, 2014 | 21 | 2014 |
| Supervised Hierarchical Dirichlet Processes with Variational Inference C Zhang, CH Ek, X Gratal, FT Pokorny, H Kjellstrom IEEE ICCV Workshop on InferPGM, 2013 | 21 | 2013 |
| Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation R Tu, K Zhang, BC Bertilson, H Kjellstöm, C Zhang NeurIPS, 2019 | 20 | 2019 |
| AMRL: AGGREGATED MEMORY FOR REINFORCEMENT LEARNING J Beck, K Ciosek, S Devlin, S Tschiatschek, C Zhang, K Hofmann ICLR, 2020 | 17 | 2020 |
Hedvig Kjellström (Sidenbladh)Professor at KTH, Principal AI Scientist at Silo AI, Guest Professor at SLU, Sweden在 kth.se 的电子邮件经过验证
