Chao Ma

According to our database, Chao Ma authored at least 21 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• 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

AVID: Adapting Video Diffusion Models to World Models.

Marc Rigter, Tarun Gupta, Agrin Hilmkil, Chao Ma

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

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

Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma

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

Research on cooperative UAV countermeasure strategy based on interception triangle.

Shuyu Liu, Ting Chen, Tiantian Zhao, Shanshan Liu, Chao Ma

Proceedings of the 4th International Conference on Machine Learning and Computer Application, 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

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

BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis.

Weijie He, Xiaohao Mao, Chao Ma, Yu Huang, José Miguel Hernández-Lobato, Ting Chen

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo.

Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato

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

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

Functional Variational Inference based on Stochastic Process Generators.

Chao Ma, José Miguel Hernández-Lobato

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

2020

FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks.

Weijie He, Xiaohao Mao, Chao Ma, José Miguel Hernández-Lobato, Ting Chen

CoRR, 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

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

Variational Implicit Processes.

Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato

Proceedings of the 36th International Conference on Machine Learning, 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