Cheng Zhang - RankMe

103 papers A* 23A 5B 1Journal 59Unranked 11

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Amortized Inference of Causal Models via Conditional Fixed-Point Iterations. Trans. Mach. Learn. Res. Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon
2024	A*	`conf`	A Fixed-Point Approach for Causal Generative Modeling. ICML Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
2024	A*	`ed.`	Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024. NeurIPS Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang
2024	J	`jnl`	CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models. CoRR Ruibo Tu, Hedvig Kjellström, Gustav Eje Henter, Cheng Zhang
2024	J	`jnl`	Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework. CoRR Ruibo Tu, Zineb Senane, Lele Cao, Cheng Zhang, Hedvig Kjellström, Gustav Eje Henter
2024	J	`jnl`	Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 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
2024	J	`jnl`	FiP: a Fixed-Point Approach for Causal Generative Modeling. CoRR Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
2024	J	`jnl`	The Essential Role of Causality in Foundation World Models for Embodied AI. CoRR 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
2024	A*	`conf`	Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention. ICML Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma
2024	J	`jnl`	Zero-Shot Learning of Causal Models. CoRR Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon
2023	A*	`conf`	BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. NeurIPS Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong
2023	J	`jnl`	BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. CoRR Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong
2023	A*	`conf`	CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
2023	J	`jnl`	CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
2023	A*	`conf`	Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. ICLR Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
2023	J	`jnl`	Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. CoRR Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
2023	J	`jnl`	Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis. CoRR Ruibo Tu, Chao Ma, Cheng Zhang
2023	—	`ed.`	Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. CLeaR Mihaela van der Schaar, Cheng Zhang, Dominik Janzing
2023	J	`jnl`	Learn the Time to Learn: Replay Scheduling in Continual Learning. Trans. Mach. Learn. Res. Marcus Klasson, Hedvig Kjellström, Cheng Zhang
2023	A*	`conf`	Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. ICLR Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
2023	J	`jnl`	Towards Causal Foundation Model: on Duality between Causal Inference and Attention. CoRR Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
2023	J	`jnl`	Understanding Causality with Large Language Models: Feasibility and Opportunities. CoRR Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan
2022	A*	`conf`	CoRGi: Content-Rich Graph Neural Networks with Attention. KDD Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zhang, Miltiadis Allamanis
2022	J	`jnl`	Deep End-to-end Causal Inference. CoRR Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
2022	J	`jnl`	Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. CoRR Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster
2022	J	`jnl`	Instructions and Guide: Causal Insights for Learning Paths in Education. CoRR Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang
2022	J	`jnl`	Learn the Time to Learn: Replay Scheduling in Continual Learning. CoRR Marcus Klasson, Hedvig Kjellström, Cheng Zhang
2022	A*	`conf`	Optimal Transport for Causal Discovery. ICLR Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang
2022	J	`jnl`	Optimal transport for causal discovery. CoRR Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang
2022	J	`jnl`	Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise. CoRR Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
2022	A*	`conf`	Simultaneous Missing Value Imputation and Structure Learning with Groups. NeurIPS Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
2021	J	`jnl`	Causally Constrained Data Synthesis for Private Data Release. CoRR Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
2021	J	`jnl`	Contextual HyperNetworks for Novel Feature Adaptation. CoRR Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
2021	J	`jnl`	DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions. CoRR Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman
2021	A*	`conf`	Educational Question Mining At Scale: Prediction, Analysis and Personalization. AAAI Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang
2021	A*	`conf`	Estimating α-Rank by Maximizing Information Gain. AAAI Tabish Rashid, Cheng Zhang, Kamil Ciosek
2021	J	`jnl`	Estimating α-Rank by Maximizing Information Gain. CoRR Tabish Rashid, Cheng Zhang, Kamil Ciosek
2021	J	`jnl`	Identifiable Generative Models for Missing Not at Random Data Imputation. CoRR Chao Ma, Cheng Zhang
2021	A*	`conf`	Identifiable Generative models for Missing Not at Random Data Imputation. NeurIPS Chao Ma, Cheng Zhang
2021	A	`conf`	Meta-Learning Divergences for Variational Inference. AISTATS Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
2021	J	`jnl`	Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR 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
2021	A*	`conf`	Sparse Uncertainty Representation in Deep Learning with Inducing Weights. NeurIPS Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
2021	J	`jnl`	Sparse Uncertainty Representation in Deep Learning with Inducing Weights. CoRR Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
2021	J	`jnl`	VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups. CoRR Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltiadis Allamanis, Cheng Zhang
2020	A*	`conf`	A Causal View on Robustness of Neural Networks. NeurIPS Cheng Zhang, Kun Zhang, Yingzhen Li
2020	J	`jnl`	A Causal View on Robustness of Neural Networks. CoRR Cheng Zhang, Kun Zhang, Yingzhen Li
2020	J	`jnl`	A Study on Efficiency in Continual Learning Inspired by Human Learning. CoRR Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang
2020	A*	`conf`	AMRL: Aggregated Memory For Reinforcement Learning. ICLR Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann
2020	J	`jnl`	DRIFT: Deep Reinforcement Learning for Functional Software Testing. CoRR 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
2020	J	`jnl`	Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR 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
2020	J	`jnl`	Hide-and-Seek Privacy Challenge. CoRR James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar
2020	—	`conf`	Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification. NeurIPS (Competition and Demos) James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar
2020	J	`jnl`	How Do Fair Decisions Fare in Long-term Qualification? CoRR Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang
2020	A*	`conf`	How do fair decisions fare in long-term qualification? NeurIPS Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang
2020	J	`jnl`	Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. CoRR Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Cheng Zhang
2020	J	`jnl`	Meta-Learning for Variational Inference. CoRR Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
2020	J	`jnl`	Reinforcement Learning with Efficient Active Feature Acquisition. CoRR Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek
2020	—	`conf`	Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) 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
2020	—	`ed.`	Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019. AABI Cheng Zhang, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang
2020	J	`jnl`	Using Variational Multi-view Learning for Classification of Grocery Items. Patterns Marcus Klasson, Cheng Zhang, Hedvig Kjellström
2020	A*	`conf`	VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2020	J	`jnl`	VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang
2019	A	`conf`	A Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels. WACV Marcus Klasson, Cheng Zhang, Hedvig Kjellström
2019	J	`jnl`	A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels. CoRR Marcus Klasson, Cheng Zhang, Hedvig Kjellström
2019	A*	`conf`	Active Mini-Batch Sampling Using Repulsive Point Processes. AAAI Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi
2019	J	`jnl`	Advances in Variational Inference. IEEE Trans. Pattern Anal. Mach. Intell. Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt
2019	A	`conf`	Causal Discovery in the Presence of Missing Data. AISTATS Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang
2019	A*	`conf`	EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. ICML Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang
2019	A*	`conf`	Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
2019	J	`jnl`	Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. CoRR Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
2019	—	`conf`	HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	J	`jnl`	Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
2019	A*	`conf`	Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	J	`jnl`	Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. CoRR Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang
2019	A*	`conf`	Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. NeurIPS Ruibo Tu, Kun Zhang, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang
2019	J	`jnl`	Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. CoRR Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang
2019	—	`conf`	Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation. MLHC Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang
2019	—	`ed.`	Symposium on Advances in Approximate Bayesian Inference, AABI 2018, Montréal, QC, Canada, December 2, 2018. AABI Francisco J. R. Ruiz, Cheng Zhang, Dawen Liang, Thang D. Bui
2019	J	`jnl`	Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2018	J	`jnl`	Active Mini-Batch Sampling using Repulsive Point Processes. CoRR Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi
2018	J	`jnl`	Causal discovery in the presence of missing data. CoRR Ruibo Tu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström, Kun Zhang
2018	B	`conf`	Continuous Word Embedding Fusion via Spectral Decomposition. CoNLL Tianfan Fu, Cheng Zhang, Stephan Mandt
2018	J	`jnl`	EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. CoRR Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang
2018	J	`jnl`	Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation. CoRR Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang
2018	—	`conf`	Simultaneous measurement imputation and outcome prediction for Achilles tendon rupture rehabilitation. AIH@IJCAI Charles Hamesse, Hedvig Kjellström, Paul Ackermann, Cheng Zhang
2017	J	`jnl`	Advances in Variational Inference. CoRR Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt
2017	A	`conf`	Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes. UAI Cheng Zhang, Hedvig Kjellström, Stephan Mandt
2017	J	`jnl`	Causality Refined Diagnostic Prediction. CoRR Marcus Klasson, Kun Zhang, Bo C. Bertilson, Cheng Zhang, Hedvig Kjellström
2017	—	`conf`	Perturbative Black Box Variational Inference. NIPS Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2017	J	`jnl`	Perturbative Black Box Variational Inference. CoRR Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2017	J	`jnl`	Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification. CoRR Cheng Zhang, Hedvig Kjellström, Stephan Mandt
2016	J	`jnl`	Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation. CoRR An Qu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström
2016	—	`conf`	Diagnostic Prediction Using Discomfort Drawings with IBTM. MLHC Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson
2016	J	`jnl`	Diagnostic Prediction Using Discomfort Drawings with IBTM. CoRR Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson
2016	J	`jnl`	Diagnostic Prediction Using Discomfort Drawings. CoRR Cheng Zhang, Hedvig Kjellström, Bo C. Bertilson
2016	J	`jnl`	Inter-Battery Topic Representation Learning. CoRR Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek
2016	—	`conf`	Inter-battery Topic Representation Learning. ECCV (8) Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek
2016	—	`—`	Structured Representation Using Latent Variable Models. Cheng Zhang
2014	—	`conf`	How to Supervise Topic Models. ECCV Workshops (2) Cheng Zhang, Hedvig Kjellström
2013	A	`conf`	Contextual modeling with labeled multi-LDA. IROS Cheng Zhang, Dan Song, Hedvig Kjellström
2013	—	`conf`	Factorized Topic Models ICLR (Workshop Poster) Cheng Zhang, Carl Henrik Ek, Hedvig Kjellström
2013	J	`jnl`	Multi-Class Detection and Segmentation of Objects in Depth CoRR Cheng Zhang, Hedvig Kjellström
2013	—	`conf`	Supervised Hierarchical Dirichlet Processes with Variational Inference. ICCV Workshops Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian T. Pokorny, Hedvig Kjellström