Obserwuj
Cheng Zhang
Cheng Zhang
Principal Researcher, Microsoft Research, Cambridge, UK
Zweryfikowany adres z kth.se - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Advances in Variational Inference
C Zhang, J Butepage, H Kjellstrom, S Mandt
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
7132018
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
1472019
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
1192019
Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification
C Zhang, H Kjellström, S Mandt
UAI, 2017
82*2017
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
67*2020
A Causal View on Robustness of Neural Networks
C Zhang, K Zhang, Y Li
622020
Causal discovery in the presence of missing data
R Tu, C Zhang, P Ackermann, C Glymour, H Kjellström, K Zhang
AISTATS, 2019
602019
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
562020
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
522019
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
502020
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
442019
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
422022
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
402019
Perturbative Black Box Variational Inference
C Zhang*, R Bamler*, M Opper, S Mandt*
NIPS, 2017
39*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
322021
Partial VAE for Hybrid Recommender System
C Ma, W Gong, JM Hernandez-Lobato, N Koenigstein, S Nowozin, ...
NIPS Workshop on Bayesian Deep Learning, 2018
322018
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
282019
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
R Tu, K Zhang, BC Bertilson, H Kjellstöm, C Zhang
NeurIPS, 2019
242019
How to supervise topic models
C Zhang, H Kjellström
Computer Vision-ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and …, 2015
222015
Supervised Hierarchical Dirichlet Processes with Variational Inference
C Zhang, CH Ek, X Gratal, FT Pokorny, H Kjellstrom
IEEE ICCV Workshop on InferPGM, 2013
222013
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