Supervised Hierarchical Dirichlet Processes with Variational Inference. - Cheng Zhang 005; Carl Henrik Ek; Xavi Gratal; Florian T. Pokorny; Hedvig Kjellström (ICCV Workshops, 2013)

Contextual modeling with labeled multi-LDA. - Cheng Zhang 005; Dan Song; Hedvig Kjellström (IROS, 2013)

Factorized Topic Models - Cheng Zhang 005; Carl Henrik Ek; Hedvig Kjellström (ICLR (Workshop Poster), 2013)

Multi-Class Detection and Segmentation of Objects in Depth - Cheng Zhang 005; Hedvig Kjellström (CoRR, 2013)

How to Supervise Topic Models. - Cheng Zhang 005; Hedvig Kjellström (ECCV Workshops (2), 2014)

Inter-battery Topic Representation Learning. - Cheng Zhang 005; Hedvig Kjellström; Carl Henrik Ek (ECCV (8), 2016)

Diagnostic Prediction Using Discomfort Drawings with IBTM. - Cheng Zhang 005; Hedvig Kjellström; Carl Henrik Ek; Bo C. Bertilson (MLHC, 2016)

Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation. - An Qu; Cheng Zhang 005; Paul Ackermann; Hedvig Kjellström (CoRR, 2016)

Diagnostic Prediction Using Discomfort Drawings. - Cheng Zhang 005; Hedvig Kjellström; Bo C. Bertilson (CoRR, 2016)

Structured Representation Using Latent Variable Models. - Cheng Zhang 005 (2016)

Perturbative Black Box Variational Inference. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (NIPS, 2017)

Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes. - Cheng Zhang 005; Hedvig Kjellström; Stephan Mandt (UAI, 2017)

Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification. - Cheng Zhang 005; Hedvig Kjellström; Stephan Mandt (CoRR, 2017)

Causality Refined Diagnostic Prediction. - Marcus Klasson; Kun Zhang 001; Bo C. Bertilson; Cheng Zhang 005; Hedvig Kjellström (CoRR, 2017)

Continuous Word Embedding Fusion via Spectral Decomposition. - Tianfan Fu; Cheng Zhang 005; Stephan Mandt (CoNLL, 2018)

Simultaneous measurement imputation and outcome prediction for Achilles tendon rupture rehabilitation. - Charles Hamesse; Hedvig Kjellström; Paul Ackermann; Cheng Zhang 005 (AIH@IJCAI, 2018)

Causal discovery in the presence of missing data. - Ruibo Tu; Cheng Zhang 005; Paul Ackermann; Hedvig Kjellström; Kun Zhang 001 (CoRR, 2018)

Active Mini-Batch Sampling Using Repulsive Point Processes. - Cheng Zhang 005; Cengiz Öztireli; Stephan Mandt; Giampiero Salvi (AAAI, 2019)

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. - Chao Ma 019; Sebastian Tschiatschek; Yingzhen Li; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (AABI, 2019)

Causal Discovery in the Presence of Missing Data. - Ruibo Tu; Cheng Zhang 005; Paul Ackermann; Karthika Mohan; Hedvig Kjellström; Kun Zhang 001 (AISTATS, 2019)

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. - Chao Ma 019; Sebastian Tschiatschek; Konstantina Palla; José Miguel Hernández-Lobato; Sebastian Nowozin; Cheng Zhang 005 (ICML, 2019)

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation. - Charles Hamesse; Ruibo Tu; Paul Ackermann; Hedvig Kjellström; Cheng Zhang 005 (MLHC, 2019)

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Sebastian Nowozin; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (NeurIPS, 2019)

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. - Maximilian Igl; Kamil Ciosek; Yingzhen Li; Sebastian Tschiatschek; Cheng Zhang 005; Sam Devlin; Katja Hofmann (NeurIPS, 2019)

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. - Ruibo Tu; Kun Zhang 001; Bo C. Bertilson; Hedvig Kjellström; Cheng Zhang 005 (NeurIPS, 2019)

A Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels. - Marcus Klasson; Cheng Zhang 005; Hedvig Kjellström (WACV, 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 005 (CoRR, 2019)

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Richard E. Turner; Sebastian Nowozin; José Miguel Hernández-Lobato; Cheng Zhang 005 (CoRR, 2019)

Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (CoRR, 2019)

Advances in Variational Inference. - Cheng Zhang 005; Judith Bütepage; Hedvig Kjellström; Stephan Mandt (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

AMRL: Aggregated Memory For Reinforcement Learning. - Jacob Beck; Kamil Ciosek; Sam Devlin; Sebastian Tschiatschek; Cheng Zhang 005; Katja Hofmann (ICLR, 2020)

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. - Zichao Wang 001; 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 002; Cheng Zhang 005 (NeurIPS (Competition and Demos), 2020)

A Causal View on Robustness of Neural Networks. - Cheng Zhang 005; Kun Zhang 001; Yingzhen Li (NeurIPS, 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 005; Danielle Belgrave; Mihaela van der Schaar (NeurIPS (Competition and Demos), 2020)

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. - Chao Ma 019; Sebastian Tschiatschek; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (NeurIPS, 2020)

How do fair decisions fare in long-term qualification? - Xueru Zhang; Ruibo Tu; Yang Liu 018; Mingyan Liu; Hedvig Kjellström; Kun Zhang 001; Cheng Zhang 005 (NeurIPS, 2020)

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. - Zichao Wang 001; Sebastian Tschiatschek; Simon Woodhead 002; José Miguel Hernández-Lobato; Simon Peyton Jones; Cheng Zhang 005 (CoRR, 2020)

Meta-Learning for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (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 005; et al. (CoRR, 2020)

Diagnostic Questions: The NeurIPS 2020 Education Challenge. - Zichao Wang 001; 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 002; Cheng Zhang 005 (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 005; Danielle Belgrave; Mihaela van der Schaar (CoRR, 2020)

A Study on Efficiency in Continual Learning Inspired by Human Learning. - Philip J. Ball; Yingzhen Li; Angus Lamb; Cheng Zhang 005 (CoRR, 2020)

Reinforcement Learning with Efficient Active Feature Acquisition. - Haiyan Yin; Yingzhen Li; Sinno Jialin Pan; Cheng Zhang 005; Sebastian Tschiatschek (CoRR, 2020)

Using Variational Multi-view Learning for Classification of Grocery Items. - Marcus Klasson; Cheng Zhang 005; Hedvig Kjellström (Patterns, 2020)

Educational Question Mining At Scale: Prediction, Analysis and Personalization. - Zichao Wang 001; Sebastian Tschiatschek; Simon Woodhead 002; José Miguel Hernández-Lobato; Simon Peyton Jones; Richard G. Baraniuk; Cheng Zhang 005 (AAAI, 2021)

Estimating α-Rank by Maximizing Information Gain. - Tabish Rashid; Cheng Zhang 005; Kamil Ciosek (AAAI, 2021)

Meta-Learning Divergences for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (AISTATS, 2021)

Identifiable Generative models for Missing Not at Random Data Imputation. - Chao Ma 019; Cheng Zhang 005 (NeurIPS, 2021)

Sparse Uncertainty Representation in Deep Learning with Inducing Weights. - Hippolyt Ritter; Martin Kukla; Cheng Zhang 005; Yingzhen Li (NeurIPS, 2021)

Contextual HyperNetworks for Novel Feature Adaptation. - Angus Lamb; Evgeny Saveliev; Yingzhen Li; Sebastian Tschiatschek; Camilla Longden; Simon Woodhead 002; José Miguel Hernández-Lobato; Richard E. Turner; Pashmina Cameron; Cheng Zhang 005 (CoRR, 2021)

Causally Constrained Data Synthesis for Private Data Release. - Varun Chandrasekaran; Darren Edge; Somesh Jha; Amit Sharma 007; Cheng Zhang 005; Shruti Tople (CoRR, 2021)

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions. - Amit Sharma 007; Vasilis Syrgkanis; Cheng Zhang 005; Emre Kiciman (CoRR, 2021)

VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups. - Pablo Morales-Alvarez; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2021)

Optimal Transport for Causal Discovery. - Ruibo Tu; Kun Zhang 001; Hedvig Kjellström; Cheng Zhang 005 (ICLR, 2022)

CoRGi: Content-Rich Graph Neural Networks with Attention. - Jooyeon Kim; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Cheng Zhang 005; Miltiadis Allamanis (KDD, 2022)

Simultaneous Missing Value Imputation and Structure Learning with Groups. - Pablo Morales-Alvarez; Wenbo Gong 001; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (NeurIPS, 2022)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2022)

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. - Desi R. Ivanova; Joel Jennings; Cheng Zhang 005; Adam Foster 001 (CoRR, 2022)

Instructions and Guide: Causal Insights for Learning Paths in Education. - Wenbo Gong 001; Digory Smith; Zichao Wang 001; Craig Barton; Simon Woodhead 002; Nick Pawlowski; Joel Jennings; Cheng Zhang 005 (CoRR, 2022)

Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. - Wenbo Gong 001; Joel Jennings; Cheng Zhang 005; Nick Pawlowski (ICLR, 2023)

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. - Matthew Ashman; Chao Ma 019; Agrin Hilmkil; Joel Jennings; Cheng Zhang 005 (ICLR, 2023)

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. - Desi R. Ivanova; Joel Jennings; Tom Rainforth; Cheng Zhang 005; Adam Foster 001 (ICML, 2023)

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (NeurIPS, 2023)

Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis. - Ruibo Tu; Chao Ma 019; Cheng Zhang 005 (CoRR, 2023)

Understanding Causality with Large Language Models: Feasibility and Opportunities. - Cheng Zhang 005; Stefan Bauer; Paul Bennett; Jiangfeng Gao; Wenbo Gong 001; Agrin Hilmkil; Joel Jennings; Chao Ma 019; Tom Minka; Nick Pawlowski; James Vaughan (CoRR, 2023)

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (CoRR, 2023)

Towards Causal Foundation Model: on Duality between Causal Inference and Attention. - Jiaqi Zhang; Joel Jennings; Cheng Zhang 005; Chao Ma 019 (CoRR, 2023)

Learn the Time to Learn: Replay Scheduling in Continual Learning. - Marcus Klasson; Hedvig Kjellström; Cheng Zhang 005 (Trans. Mach. Learn. Res., 2023)

A Fixed-Point Approach for Causal Generative Modeling. - Meyer Scetbon; Joel Jennings; Agrin Hilmkil; Cheng Zhang 005; Chao Ma 019 (ICML, 2024)

Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention. - Jiaqi Zhang; Joel Jennings; Agrin Hilmkil; Nick Pawlowski; Cheng Zhang 005; Chao Ma 019 (ICML, 2024)

The Essential Role of Causality in Foundation World Models for Embodied AI. - Tarun Gupta 002; Wenbo Gong 001; Chao Ma 019; Nick Pawlowski; Agrin Hilmkil; Meyer Scetbon; Ade Famoti; Ashley Juan Llorens; Jianfeng Gao 001; Stefan Bauer; Danica Kragic; Bernhard Schölkopf; et al. (CoRR, 2024)

FiP: a Fixed-Point Approach for Causal Generative Modeling. - Meyer Scetbon; Joel Jennings; Agrin Hilmkil; Cheng Zhang 005; Chao Ma 019 (CoRR, 2024)

Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework. - Ruibo Tu; Zineb Senane; Lele Cao; Cheng Zhang 005; Hedvig Kjellström; Gustav Eje Henter (CoRR, 2024)

Zero-Shot Learning of Causal Models. - Divyat Mahajan; Jannes Gladrow; Agrin Hilmkil; Cheng Zhang 005; Meyer Scetbon (CoRR, 2024)

CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models. - Ruibo Tu; Hedvig Kjellström; Gustav Eje Henter; Cheng Zhang 005 (CoRR, 2024)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Agrin Hilmkil; Joel Jennings; et al. (Trans. Mach. Learn. Res., 2024)