Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
ICLR 2023 | May 2023
Publications
Publications
Key publications:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez Lobato, Sebastian Nowozin, Cheng Zhang
International Conference on Machine Learning | June 2019
View Publication
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
View Publication
Other Publications:
Publications by Year
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A Causal AI Suite for Decision-Making
Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang
NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
Optimal Transport for Causal Discovery
Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang
ICLR 2022 | April 2022
Simultaneous Missing Value Imputation and Structure Learning with Groups
Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
2022 Neural Information Processing Systems | February 2022
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman (emrek), Amit Sharma, Martin Kukla, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
MSR-TR-2022-1 | February 2022
Published by Microsoft
Local Constraint-Based Causal Discovery under Selection Bias
Philip Versteeg, Cheng Zhang, Joris Mooij
Conference on Causal Learning and Reasoning (CLeaR) | January 2022
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Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
NeurIPS | December 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
NeurIPS | December 2021
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma, Cheng Zhang, Vasilis Syrgkanis, Emre Kiciman
MSR-TR-2021-15 | July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
Optimal transport for causal discovery
Ruibo Tu, Kun Zhang, Hedvig Kjellstrom, Cheng Zhang
| July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
Probabilistic DAG Search
Julia Grosse, Cheng Zhang, Philipp Hennig
Conference on Uncertainty in Artificial Intelligence (UAI) 2021 | July 2021
VICAUSE: Simultaneous missing value imputation and causal discovery
Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltos Allamanis, Cheng Zhang
MSR-TR-2021-14 | July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
Meta-Learning for Variational Inference
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
International Conference on Artificial Intelligence and Statistics | April 2021
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev, Yordan Zaykov, Pashmina Cameron, Jose Miguel Hernandez-Lobato, Richard E. Turner, Richard G. Baraniuk, Simon Peyton Jones, Craig Barton, Simon Woodhead, Cheng Zhang
NeurIPS Competition | April 2021
To appear in PMLR proceeding of NeurIPS competition
Educational Question Mining At Scale: Prediction, Analysis and Personalization
Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Jones Simon Peyton, Richard G. Baraniuk, Cheng Zhang
Symposium on Educational Advances in Artificial Intelligence (AAAI-EAAI) | February 2021
Estimating α-Rank by Maximizing Information Gain.
Tabish Rashid, Cheng Zhang, Kamil Ciosek
AAAI Conference on Artificial Intelligence (AAAI 2021) | January 2021
-
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2020 Neural Information Processing Systems | December 2020
A Causal View on Robustness of Neural Networks
Cheng Zhang, Kun Zhang, Yingzhen Li
2020 Neural Information Processing Systems | December 2020
PDF PDF PDF Publication Publication Publication Publication Publication Publication Publication Publication
Diagnostic Questions: The NeurIPS 2020 Education Challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, Jose Miguel Hernandez Lobato, Richard Turner, Richard Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
July 2020
-
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, Jose Miguel Hernandez Lobato, Sebastian Tschiatschek
Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Advances in Variational Inference
Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, Stephan Mandt
IEEE Transactions on Pattern Analysis and Machine Intelligence | August 2019, Vol 41(8): pp. 2008-2026
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez Lobato, Sebastian Nowozin, Cheng Zhang
International Conference on Machine Learning | June 2019
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
arXiv:1906.00429 | June 2019
Published by arXiv
A Causal View on Robustness of Neural Networks
Cheng Zhang, Yingzhen Li
June 2019
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang
June 2019
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, Jose Miguel Hern´andez-Lobato, Yordan Zaykov, Cheng Zhang
May 2019
Causal Discovery in the Presence of Missing Data
Cheng Zhang, Ruibo Tu, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang
International Conference on Artificial Intelligence and Statistics (AISTATS) | April 2019
Editor(s): Kamalika Chaudhuri, Masashi Sugiyama
Publications by Research Area
-
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
ICLR 2023 | May 2023
A Causal AI Suite for Decision-Making
Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang
NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
Optimal Transport for Causal Discovery
Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang
ICLR 2022 | April 2022
Simultaneous Missing Value Imputation and Structure Learning with Groups
Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
2022 Neural Information Processing Systems | February 2022
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman (emrek), Amit Sharma, Martin Kukla, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
MSR-TR-2022-1 | February 2022
Published by Microsoft
Local Constraint-Based Causal Discovery under Selection Bias
Philip Versteeg, Cheng Zhang, Joris Mooij
Conference on Causal Learning and Reasoning (CLeaR) | January 2022
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
NeurIPS | December 2021
Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
NeurIPS | December 2021
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma, Cheng Zhang, Vasilis Syrgkanis, Emre Kiciman
MSR-TR-2021-15 | July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
VICAUSE: Simultaneous missing value imputation and causal discovery
Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltos Allamanis, Cheng Zhang
MSR-TR-2021-14 | July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
Optimal transport for causal discovery
Ruibo Tu, Kun Zhang, Hedvig Kjellstrom, Cheng Zhang
| July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
Probabilistic DAG Search
Julia Grosse, Cheng Zhang, Philipp Hennig
Conference on Uncertainty in Artificial Intelligence (UAI) 2021 | July 2021
Meta-Learning for Variational Inference
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
International Conference on Artificial Intelligence and Statistics | April 2021
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev, Yordan Zaykov, Pashmina Cameron, Jose Miguel Hernandez-Lobato, Richard E. Turner, Richard G. Baraniuk, Simon Peyton Jones, Craig Barton, Simon Woodhead, Cheng Zhang
NeurIPS Competition | April 2021
To appear in PMLR proceeding of NeurIPS competition
Educational Question Mining At Scale: Prediction, Analysis and Personalization
Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Jones Simon Peyton, Richard G. Baraniuk, Cheng Zhang
Symposium on Educational Advances in Artificial Intelligence (AAAI-EAAI) | February 2021
Estimating α-Rank by Maximizing Information Gain.
Tabish Rashid, Cheng Zhang, Kamil Ciosek
AAAI Conference on Artificial Intelligence (AAAI 2021) | January 2021
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2020 Neural Information Processing Systems | December 2020
A Causal View on Robustness of Neural Networks
Cheng Zhang, Kun Zhang, Yingzhen Li
2020 Neural Information Processing Systems | December 2020
PDF PDF PDF Publication Publication Publication Publication Publication Publication Publication Publication
Diagnostic Questions: The NeurIPS 2020 Education Challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, Jose Miguel Hernandez Lobato, Richard Turner, Richard Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
July 2020
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, Jose Miguel Hernandez Lobato, Sebastian Tschiatschek
Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Advances in Variational Inference
Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, Stephan Mandt
IEEE Transactions on Pattern Analysis and Machine Intelligence | August 2019, Vol 41(8): pp. 2008-2026
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez Lobato, Sebastian Nowozin, Cheng Zhang
International Conference on Machine Learning | June 2019
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
arXiv:1906.00429 | June 2019
Published by arXiv
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang
June 2019
A Causal View on Robustness of Neural Networks
Cheng Zhang, Yingzhen Li
June 2019
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, Jose Miguel Hern´andez-Lobato, Yordan Zaykov, Cheng Zhang
May 2019
Causal Discovery in the Presence of Missing Data
Cheng Zhang, Ruibo Tu, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang
International Conference on Artificial Intelligence and Statistics (AISTATS) | April 2019
Editor(s): Kamalika Chaudhuri, Masashi Sugiyama
-
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, Jose Miguel Hern´andez-Lobato, Yordan Zaykov, Cheng Zhang
May 2019
Publications by Type
-
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
ICLR 2023 | May 2023
A Causal AI Suite for Decision-Making
Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang
NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
Optimal Transport for Causal Discovery
Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang
ICLR 2022 | April 2022
Simultaneous Missing Value Imputation and Structure Learning with Groups
Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
2022 Neural Information Processing Systems | February 2022
Local Constraint-Based Causal Discovery under Selection Bias
Philip Versteeg, Cheng Zhang, Joris Mooij
Conference on Causal Learning and Reasoning (CLeaR) | January 2022
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
NeurIPS | December 2021
Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
NeurIPS | December 2021
Probabilistic DAG Search
Julia Grosse, Cheng Zhang, Philipp Hennig
Conference on Uncertainty in Artificial Intelligence (UAI) 2021 | July 2021
Meta-Learning for Variational Inference
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
International Conference on Artificial Intelligence and Statistics | April 2021
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev, Yordan Zaykov, Pashmina Cameron, Jose Miguel Hernandez-Lobato, Richard E. Turner, Richard G. Baraniuk, Simon Peyton Jones, Craig Barton, Simon Woodhead, Cheng Zhang
NeurIPS Competition | April 2021
To appear in PMLR proceeding of NeurIPS competition
Educational Question Mining At Scale: Prediction, Analysis and Personalization
Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Jones Simon Peyton, Richard G. Baraniuk, Cheng Zhang
Symposium on Educational Advances in Artificial Intelligence (AAAI-EAAI) | February 2021
Estimating α-Rank by Maximizing Information Gain.
Tabish Rashid, Cheng Zhang, Kamil Ciosek
AAAI Conference on Artificial Intelligence (AAAI 2021) | January 2021
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2020 Neural Information Processing Systems | December 2020
A Causal View on Robustness of Neural Networks
Cheng Zhang, Kun Zhang, Yingzhen Li
2020 Neural Information Processing Systems | December 2020
PDF PDF PDF Publication Publication Publication Publication Publication Publication Publication Publication
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, Jose Miguel Hernandez Lobato, Sebastian Tschiatschek
Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | December 2019
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez Lobato, Sebastian Nowozin, Cheng Zhang
International Conference on Machine Learning | June 2019
Causal Discovery in the Presence of Missing Data
Cheng Zhang, Ruibo Tu, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang
International Conference on Artificial Intelligence and Statistics (AISTATS) | April 2019
Editor(s): Kamalika Chaudhuri, Masashi Sugiyama
-
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman (emrek), Amit Sharma, Martin Kukla, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
MSR-TR-2022-1 | February 2022
Published by Microsoft
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma, Cheng Zhang, Vasilis Syrgkanis, Emre Kiciman
MSR-TR-2021-15 | July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
VICAUSE: Simultaneous missing value imputation and causal discovery
Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltos Allamanis, Cheng Zhang
MSR-TR-2021-14 | July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
Optimal transport for causal discovery
Ruibo Tu, Kun Zhang, Hedvig Kjellstrom, Cheng Zhang
| July 2021
Published by ICML 2021 workshop on the Neglected Assumptions in Causal Inference
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
arXiv:1906.00429 | June 2019
Published by arXiv
-
Diagnostic Questions: The NeurIPS 2020 Education Challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, Jose Miguel Hernandez Lobato, Richard Turner, Richard Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
July 2020
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang
June 2019
A Causal View on Robustness of Neural Networks
Cheng Zhang, Yingzhen Li
June 2019
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, Jose Miguel Hern´andez-Lobato, Yordan Zaykov, Cheng Zhang
May 2019
-
Advances in Variational Inference
Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, Stephan Mandt
IEEE Transactions on Pattern Analysis and Machine Intelligence | August 2019, Vol 41(8): pp. 2008-2026