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ORCIDCheng Zhang 0005
Person information
- affiliation: GenAI
- affiliation (former): Microsoft Research Cambridge, UK
- affiliation (former): Disney Research, Pittsburgh, PA, USA
- affiliation (former): Royal Institute of Technology (KTH), Department of Robotics, Perception and Learning, Stockholm, Sweden
- not to be confused with: Cheng Zhang 0002
- not to be confused with: Cheng Zhang 0014
Other persons with the same name
- Cheng Zhang — disambiguation page
- Cheng Zhang 0001
![[0000-0002-8277-5138]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-8277-5138")
— Fudan University, School of Management, Shanghai, China - Cheng Zhang 0002 — KTH Royal Institute of Technology, Stockholm, Sweden (and 2 more)
- Cheng Zhang 0003 — Chinese Academy of Sciences, National Space Science Center, Beijing, China
- Cheng Zhang 0004 — Southeast University, National Mobile Communications Research Laboratory, Nanjing, China (and 1 more)
- Cheng Zhang 0006 — Purdue University Northwest, Hammond, IN, USA (and 1 more)
- Cheng Zhang 0007 — Waseda University, Department of Computer Science and Communication Engineering, Tokyo, Japan
- Cheng Zhang 0008 — Shanghai University, Department of Mathematics, China
- Cheng Zhang 0009 — China University of Geosciences, School of Automation, Wuhan, China
- Cheng Zhang 0010 — Anhui University, Hefei, China (and 1 more)
- Cheng Zhang 0011 — Georgia Institute of Technology, Atlanta, GA, USA
- Cheng Zhang 0012 — Huawei Technologies Co., Ltd., Shenzhen, China (and 2 more)
- Cheng Zhang 0013 — École centrale de Nantes, France
- Cheng Zhang 0014 — Texas A&M University, Department of Computer Science & Engineering, College Station, TX, USA (and 2 more)
- Cheng Zhang 0015 — Xi'an Jiaotong-Liverpool University, Department of Civil Engineering, Suzhou, China (and 1 more)
- Cheng Zhang 0016 — University of Massachusetts, Lowell, MA, USA
- Cheng Zhang 0017 — Peking University, Beijing, China
- Cheng Zhang 0018 — West Texas A&M University, Department of Computer Information and Decision Management, Canyon, TX, USA
- Cheng Zhang 0019 — Tianjin University, Tianjin, China
- Cheng Zhang 0020 — Huazhong University of Science and Technology, China
Other persons with a similar name
- Zhang Cheng
- Zhanglin Cheng (aka: Zhang-Lin Cheng)
- Chengjun Zhang (aka: Cheng-Jun Zhang) — disambiguation page
- Chengming Zhang (aka: Cheng-Ming Zhang) — disambiguation page
- Chengwei Zhang (aka: Cheng-wei Zhang) — disambiguation page
- Chengyi Zhang (aka: Cheng-Yi Zhang) — disambiguation page
- Chengzhi Zhang (aka: Cheng-Zhi Zhang) — disambiguation page
- Pengcheng Zhang (aka: Peng-Cheng Zhang) — disambiguation page
- Yi-Cheng Zhang
- Chengye Zhang 0001 (aka: Cheng Ye Zhang 0001) — China University of Mining and Technology, College of Geoscience and Surveying Engineering, Beijing, China (and 1 more)
- show all similar names
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2020 – today
- 2024
[j4]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 2024 (2024)- [c39]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
A Fixed-Point Approach for Causal Generative Modeling. ICML 2024 - [c38]Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma:
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention. ICML 2024 - [e4]Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang:
Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024. 2024 [contents] - [i54]Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang:
The Essential Role of Causality in Foundation World Models for Embodied AI. CoRR abs/2402.06665 (2024) - [i53]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
FiP: a Fixed-Point Approach for Causal Generative Modeling. CoRR abs/2404.06969 (2024) - [i52]Ruibo Tu, Zineb Senane, Lele Cao, Cheng Zhang, Hedvig Kjellström, Gustav Eje Henter:
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework. CoRR abs/2406.08311 (2024) - [i51]Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon:
Zero-Shot Learning of Causal Models. CoRR abs/2410.06128 (2024) - [i50]Ruibo Tu, Hedvig Kjellström, Gustav Eje Henter, Cheng Zhang:
CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models. CoRR abs/2412.17970 (2024) - 2023
- [j3]Marcus Klasson, Hedvig Kjellström, Cheng Zhang:
Learn the Time to Learn: Replay Scheduling in Continual Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c37]Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski:
Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. ICLR 2023 - [c36]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. ICLR 2023 - [c35]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML 2023: 14445-14464 - [c34]Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong:
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. NeurIPS 2023 - [e3]Mihaela van der Schaar, Cheng Zhang, Dominik Janzing:
Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. Proceedings of Machine Learning Research 213, PMLR 2023 [contents] - [i49]Ruibo Tu, Chao Ma, Cheng Zhang:
Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis. CoRR abs/2301.13819 (2023) - [i48]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR abs/2302.14015 (2023) - [i47]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. CoRR abs/2303.12703 (2023) - [i46]Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan:
Understanding Causality with Large Language Models: Feasibility and Opportunities. CoRR abs/2304.05524 (2023) - [i45]Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong:
BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. CoRR abs/2307.13917 (2023) - [i44]Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma:
Towards Causal Foundation Model: on Duality between Causal Inference and Attention. CoRR abs/2310.00809 (2023) - 2022
- [c33]Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang:
Optimal Transport for Causal Discovery. ICLR 2022 - [c32]Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zhang, Miltiadis Allamanis:
CoRGi: Content-Rich Graph Neural Networks with Attention. KDD 2022: 773-783 - [c31]Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Simultaneous Missing Value Imputation and Structure Learning with Groups. NeurIPS 2022 - [i43]Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang:
Optimal transport for causal discovery. CoRR abs/2201.09366 (2022) - [i42]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. CoRR abs/2202.02195 (2022) - [i41]Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster:
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. CoRR abs/2207.05250 (2022) - [i40]Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang:
Instructions and Guide: Causal Insights for Learning Paths in Education. CoRR abs/2208.12610 (2022) - [i39]Marcus Klasson, Hedvig Kjellström, Cheng Zhang:
Learn the Time to Learn: Replay Scheduling in Continual Learning. CoRR abs/2209.08660 (2022) - [i38]Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski:
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise. CoRR abs/2210.14706 (2022) - 2021
- [c30]Tabish Rashid, Cheng Zhang, Kamil Ciosek:
Estimating α-Rank by Maximizing Information Gain. AAAI 2021: 5673-5681 - [c29]Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang:
Educational Question Mining At Scale: Prediction, Analysis and Personalization. AAAI 2021: 15669-15677 - [c28]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning Divergences for Variational Inference. AISTATS 2021: 4024-4032 - [c27]Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li:
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. NeurIPS 2021: 6515-6528 - [c26]Chao Ma, Cheng Zhang:
Identifiable Generative models for Missing Not at Random Data Imputation. NeurIPS 2021: 27645-27658 - [i37]Tabish Rashid, Cheng Zhang, Kamil Ciosek:
Estimating α-Rank by Maximizing Information Gain. CoRR abs/2101.09178 (2021) - [i36]Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang:
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2104.04034 (2021) - [i35]Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang:
Contextual HyperNetworks for Novel Feature Adaptation. CoRR abs/2104.05860 (2021) - [i34]Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople:
Causally Constrained Data Synthesis for Private Data Release. CoRR abs/2105.13144 (2021) - [i33]Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li:
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. CoRR abs/2105.14594 (2021) - [i32]Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman:
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions. CoRR abs/2108.13518 (2021) - [i31]Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltiadis Allamanis, Cheng Zhang:
VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups. CoRR abs/2110.08223 (2021) - [i30]Chao Ma, Cheng Zhang:
Identifiable Generative Models for Missing Not at Random Data Imputation. CoRR abs/2110.14708 (2021) - 2020
- [j2]Marcus Klasson, Cheng Zhang, Hedvig Kjellström:
Using Variational Multi-view Learning for Classification of Grocery Items. Patterns 1(8): 100143 (2020) - [c25]Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann:
AMRL: Aggregated Memory For Reinforcement Learning. ICLR 2020 - [c24]Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang:
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) 2020: 191-205 - [c23]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. NeurIPS 2020 - [c22]James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar:
Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification. NeurIPS (Competition and Demos) 2020: 206-215 - [c21]Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS 2020 - [c20]Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang:
How do fair decisions fare in long-term qualification? NeurIPS 2020 - [e2]Cheng Zhang, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang:
Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019. Proceedings of Machine Learning Research 118, PMLR 2020 [contents] - [i29]Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Cheng Zhang:
Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. CoRR abs/2003.05980 (2020) - [i28]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. CoRR abs/2005.01095 (2020) - [i27]Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR abs/2006.11941 (2020) - [i26]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning for Variational Inference. CoRR abs/2007.02912 (2020) - [i25]Luke Harries, Rebekah Storan Clarke, Timothy Chapman, Swamy V. P. L. N. Nallamalli, Levent Özgür, Shuktika Jain, Alex Leung, Steve Lim, Aaron Dietrich, José Miguel Hernández-Lobato, Tom Ellis, Cheng Zhang, Kamil Ciosek:
DRIFT: Deep Reinforcement Learning for Functional Software Testing. CoRR abs/2007.08220 (2020) - [i24]Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang:
Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2007.12061 (2020) - [i23]James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar:
Hide-and-Seek Privacy Challenge. CoRR abs/2007.12087 (2020) - [i22]Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang:
How Do Fair Decisions Fare in Long-term Qualification? CoRR abs/2010.11300 (2020) - [i21]Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang:
A Study on Efficiency in Continual Learning Inspired by Human Learning. CoRR abs/2010.15187 (2020) - [i20]Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek:
Reinforcement Learning with Efficient Active Feature Acquisition. CoRR abs/2011.00825 (2020)
2010 – 2019
- 2019
- [j1]Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt:
Advances in Variational Inference. IEEE Trans. Pattern Anal. Mach. Intell. 41(8): 2008-2026 (2019) - [c19]Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi:
Active Mini-Batch Sampling Using Repulsive Point Processes. AAAI 2019: 5741-5748 - [c18]Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI 2019: 1-8 - [c17]Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang:
Causal Discovery in the Presence of Missing Data. AISTATS 2019: 1762-1770 - [c16]Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. ICML 2019: 4234-4243 - [c15]Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang:
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation. MLHC 2019: 614-640 - [c14]Ruibo Tu, Kun Zhang, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang:
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. NeurIPS 2019: 12773-12784 - [c13]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS 2019: 13956-13968 - [c12]Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS 2019: 14791-14802 - [c11]Marcus Klasson, Cheng Zhang, Hedvig Kjellström:
A Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels. WACV 2019: 491-500 - [e1]Francisco J. R. Ruiz, Cheng Zhang, Dawen Liang, Thang D. Bui:
Symposium on Advances in Approximate Bayesian Inference, AABI 2018, Montréal, QC, Canada, December 2, 2018. Proceedings of Machine Learning Research 96, PMLR 2019 [contents] - [i19]Marcus Klasson, Cheng Zhang, Hedvig Kjellström:
A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels. CoRR abs/1901.00711 (2019) - [i18]Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang:
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. CoRR abs/1905.02599 (2019) - [i17]Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang:
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. CoRR abs/1906.01732 (2019) - [i16]Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR abs/1908.04537 (2019) - [i15]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR abs/1910.00069 (2019) - [i14]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. CoRR abs/1910.12911 (2019) - 2018
- [c10]Tianfan Fu, Cheng Zhang, Stephan Mandt:
Continuous Word Embedding Fusion via Spectral Decomposition. CoNLL 2018: 11-20 - [c9]Charles Hamesse, Hedvig Kjellström, Paul Ackermann, Cheng Zhang:
Simultaneous measurement imputation and outcome prediction for Achilles tendon rupture rehabilitation. AIH@IJCAI 2018: 82-86 - [i13]Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi:
Active Mini-Batch Sampling using Repulsive Point Processes. CoRR abs/1804.02772 (2018) - [i12]Ruibo Tu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström, Kun Zhang:
Causal discovery in the presence of missing data. CoRR abs/1807.04010 (2018) - [i11]Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. CoRR abs/1809.11142 (2018) - [i10]Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang:
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation. CoRR abs/1810.03435 (2018) - 2017
- [c8]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Perturbative Black Box Variational Inference. NIPS 2017: 5079-5088 - [c7]Cheng Zhang, Hedvig Kjellström, Stephan Mandt:
Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes. UAI 2017 - [i9]Cheng Zhang, Hedvig Kjellström, Stephan Mandt:
Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification. CoRR abs/1705.00607 (2017) - [i8]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Perturbative Black Box Variational Inference. CoRR abs/1709.07433 (2017) - [i7]Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt:
Advances in Variational Inference. CoRR abs/1711.05597 (2017) - [i6]Marcus Klasson, Kun Zhang, Bo C. Bertilson, Cheng Zhang, Hedvig Kjellström:
Causality Refined Diagnostic Prediction. CoRR abs/1711.10915 (2017) - 2016
- [b1]Cheng Zhang:
Structured Representation Using Latent Variable Models. Royal Institute of Technology, Stockholm, Sweden, 2016 - [c6]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek:
Inter-battery Topic Representation Learning. ECCV (8) 2016: 210-226 - [c5]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson:
Diagnostic Prediction Using Discomfort Drawings with IBTM. MLHC 2016: 226-238 - [i5]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek:
Inter-Battery Topic Representation Learning. CoRR abs/1605.06155 (2016) - [i4]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson:
Diagnostic Prediction Using Discomfort Drawings with IBTM. CoRR abs/1607.08206 (2016) - [i3]Cheng Zhang, Hedvig Kjellström, Bo C. Bertilson:
Diagnostic Prediction Using Discomfort Drawings. CoRR abs/1612.01356 (2016) - [i2]An Qu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström:
Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation. CoRR abs/1612.02490 (2016) - 2014
- [c4]Cheng Zhang, Hedvig Kjellström:
How to Supervise Topic Models. ECCV Workshops (2) 2014: 500-515 - 2013
- [c3]Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian T. Pokorny, Hedvig Kjellström:
Supervised Hierarchical Dirichlet Processes with Variational Inference. ICCV Workshops 2013: 254-261 - [c2]Cheng Zhang, Dan Song, Hedvig Kjellström:
Contextual modeling with labeled multi-LDA. IROS 2013: 2264-2271 - [c1]Cheng Zhang, Carl Henrik Ek, Hedvig Kjellström:
Factorized Topic Models. ICLR (Workshop Poster) 2013 - [i1]Cheng Zhang, Hedvig Kjellström:
Multi-Class Detection and Segmentation of Objects in Depth. CoRR abs/1301.5582 (2013)
Coauthor Index
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