Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning, Matthew Ashman*, Chao Ma*, Agrin Hilmkil, Joel Jennings, Cheng Zhang, The 11th International Conference on Learning Representations (ICLR) 2023
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo, Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato. Advances in Neural Information Processing Systems, 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, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang. NeurIPS 2022 Workshop on Causality for Real-world Impact
A Causal AI Suite for Decision-Making, Emre Kiciman, Eleanor Wiske Dillon, Darren Edge, Adam Foster, Joel Jennings, Chao Ma, Robert Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang. NeurIPS 2022 Workshop on Causality for Real-world Impact
Advances in Bayesian Machine Learning: From Uncertainty to Decision Making, Chao Ma. PhD thesis. University of Cambridge, 2023
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis, Weijie He, Xiaohao Mao, Chao Ma, Yu Hang, José Miguel Hernández-Lobato, Ting Chen. ACM Web Conference (WWW2022)
Functional Variational Inference based on Stochastic Process Generators Chao Ma and José Miguel Hernández-Lobato. Advances in Neural Information Processing Systems, 2021.
Identifiable Generative Models for Missing Not at Random Data Imputation Chao Ma and Cheng Zhang. Advances in Neural Information Processing Systems, 2021.
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato, and Cheng Zhang. Advances in Neural Information Processing Systems, 2020.
Variational Implicit Processes Chao Ma, Yingzhen Li, and José Miguel Hernández-Lobato. Proceedings of the 36th International Conference on Machine Learning, 2019. [Long Oral, 4%][Talk]
EDDI: Efficient Dynamic Discovery of High-value Information with Partial VAE Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, and Cheng Zhang. Proceedings of the 36th International Conference on Machine Learning, 2019. [Long Oral, 4%][Talk]
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard Turner, José Miguel Hernández-Lobato, and Cheng Zhang. Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, 2020.
Bayesian EDDI: Sequential Variable Selection with Bayesian Partial VAE Chao Ma*, Wenbo Gong*, Sebastian Tschiatschek, Sebastian Nowozin, José Miguel Hernández-Lobato, and Cheng Zhang. Real-world Sequential Decision Making workshop, International Conference on Machine Learning, 2019.
FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks Weijie He, Xiaohao Mao, Chao Ma, José Miguel Hernández-Lobato, and Ting Chen. submitted, 2020
An accurate European option pricing model under Fractional Stable Process based on Feynman Path Integral Chao Ma, Qinghua Ma, Haixiang Yao, Tiancheng Hou. Physica A, 2018.
Partial VAE for Hybrid Recommender System Chao Ma*, Wenbo Gong*, José Miguel Hernández-Lobato, Noam Koenigstein, Sebastian Nowozin, and Cheng Zhang. Neural Information Processing Systems Workshop on Bayesian Deep Learning, 2018.
Black-box Stein Divergence Minimization For Learning Latent Variable Models Chao Ma, David Barber. Neural Information Processing Systems Workshop on Advances in Approximate Bayesian Inference, 2017.
A Lyapunov-type inequality for a fractional differential equation with Hadamard derivative Qinghua Ma, Chao Ma, Jinxun Wang. Journal of Mathematical Inequalities, 2017.
Chao Ma 