Cheng Zhang

According to our database, Cheng Zhang authored at least 75 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Deep End-to-end Causal Inference.

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

Trans. Mach. Learn. Res., 2024

Zero-Shot Learning of Causal Models.

Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon

CoRR, 2024

Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework.

Ruibo Tu, Zineb Senane, Lele Cao, Cheng Zhang, Hedvig Kjellström, Gustav Eje Henter

CoRR, 2024

FiP: a Fixed-Point Approach for Causal Generative Modeling.

Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma

CoRR, 2024

The Essential Role of Causality in Foundation World Models for Embodied AI.

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

CoRR, 2024

Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention.

Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Fixed-Point Approach for Causal Generative Modeling.

Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Learn the Time to Learn: Replay Scheduling in Continual Learning.

Marcus Klasson, Hedvig Kjellström, Cheng Zhang

Trans. Mach. Learn. Res., 2023

Towards Causal Foundation Model: on Duality between Causal Inference and Attention.

Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma

CoRR, 2023

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

CoRR, 2023

Understanding Causality with Large Language Models: Feasibility and Opportunities.

Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan

CoRR, 2023

Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis.

Ruibo Tu, Chao Ma, Cheng Zhang

CoRR, 2023

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design.

Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

Proceedings of the International Conference on Machine Learning, 2023

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning.

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise.

Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Instructions and Guide: Causal Insights for Learning Paths in Education.

Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang

CoRR, 2022

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation.

Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster

CoRR, 2022

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

CoRR, 2022

Simultaneous Missing Value Imputation and Structure Learning with Groups.

Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CoRGi: Content-Rich Graph Neural Networks with Attention.

Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zhang, Miltiadis Allamanis

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Optimal Transport for Causal Discovery.

Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups.

Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltiadis Allamanis, Cheng Zhang

CoRR, 2021

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions.

Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman

CoRR, 2021

Causally Constrained Data Synthesis for Private Data Release.

Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople

CoRR, 2021

Contextual HyperNetworks for Novel Feature Adaptation.

Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang

CoRR, 2021

Sparse Uncertainty Representation in Deep Learning with Inducing Weights.

Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Identifiable Generative models for Missing Not at Random Data Imputation.

Chao Ma, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Meta-Learning Divergences for Variational Inference.

Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Estimating α-Rank by Maximizing Information Gain.

Tabish Rashid, Cheng Zhang, Kamil Ciosek

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Educational Question Mining At Scale: Prediction, Analysis and Personalization.

Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Using Variational Multi-view Learning for Classification of Grocery Items.

Marcus Klasson, Cheng Zhang, Hedvig Kjellström

Patterns, 2020

Reinforcement Learning with Efficient Active Feature Acquisition.

Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek

CoRR, 2020

A Study on Efficiency in Continual Learning Inspired by Human Learning.

Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang

CoRR, 2020

Hide-and-Seek Privacy Challenge.

James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar

CoRR, 2020

Diagnostic Questions: The NeurIPS 2020 Education Challenge.

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

CoRR, 2020

DRIFT: Deep Reinforcement Learning for Functional Software Testing.

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

CoRR, 2020

Meta-Learning for Variational Inference.

Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang

CoRR, 2020

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders.

Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Cheng Zhang

CoRR, 2020

How do fair decisions fare in long-term qualification?

Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data.

Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification.

James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

A Causal View on Robustness of Neural Networks.

Cheng Zhang, Kun Zhang, Yingzhen Li

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge.

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

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

AMRL: Aggregated Memory For Reinforcement Learning.

Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Advances in Variational Inference.

Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Tightening Bounds for Variational Inference by Revisiting Perturbation Theory.

Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

CoRR, 2019

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model.

Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang

CoRR, 2019

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care.

Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang

CoRR, 2019

A Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels.

Marcus Klasson, Cheng Zhang, Hedvig Kjellström

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2019

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation.

Ruibo Tu, Kun Zhang, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck.

Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model.

Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation.

Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang

Proceedings of the Machine Learning for Healthcare Conference, 2019

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE.

Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang

Proceedings of the 36th International Conference on Machine Learning, 2019

Causal Discovery in the Presence of Missing Data.

Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals.

Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

Active Mini-Batch Sampling Using Repulsive Point Processes.

Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Causal discovery in the presence of missing data.

Ruibo Tu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström, Kun Zhang

CoRR, 2018

Simultaneous measurement imputation and outcome prediction for Achilles tendon rupture rehabilitation.

Charles Hamesse, Hedvig Kjellström, Paul Ackermann, Cheng Zhang

Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), 2018

Continuous Word Embedding Fusion via Spectral Decomposition.

Tianfan Fu, Cheng Zhang, Stephan Mandt

Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018

2017

Causality Refined Diagnostic Prediction.

Marcus Klasson, Kun Zhang, Bo C. Bertilson, Cheng Zhang, Hedvig Kjellström

CoRR, 2017

Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification.

Cheng Zhang, Hedvig Kjellström, Stephan Mandt

CoRR, 2017

Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes.

Cheng Zhang, Hedvig Kjellström, Stephan Mandt

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Perturbative Black Box Variational Inference.

Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Structured Representation Using Latent Variable Models.

Cheng Zhang

PhD thesis, 2016

Diagnostic Prediction Using Discomfort Drawings.

Cheng Zhang, Hedvig Kjellström, Bo C. Bertilson

CoRR, 2016

Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation.

An Qu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström

CoRR, 2016

Diagnostic Prediction Using Discomfort Drawings with IBTM.

Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson

Proceedings of the 1st Machine Learning in Health Care, 2016

Inter-battery Topic Representation Learning.

Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek

Proceedings of the Computer Vision - ECCV 2016, 2016

2014

How to Supervise Topic Models.

Cheng Zhang, Hedvig Kjellström

Proceedings of the Computer Vision - ECCV 2014 Workshops, 2014

2013

Multi-Class Detection and Segmentation of Objects in Depth

Cheng Zhang, Hedvig Kjellström

CoRR, 2013

Factorized Topic Models

Cheng Zhang, Carl Henrik Ek, Hedvig Kjellström

Proceedings of the 1st International Conference on Learning Representations, 2013

Contextual modeling with labeled multi-LDA.

Cheng Zhang, Dan Song, Hedvig Kjellström

Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

Supervised Hierarchical Dirichlet Processes with Variational Inference.

Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian T. Pokorny, Hedvig Kjellström

Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops, 2013