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)

Variational Implicit Processes. - Chao Ma 019; Yingzhen Li; José Miguel Hernández-Lobato (ICML, 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)

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)

FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks. - Weijie He; Xiaohao Mao; Chao Ma 019; José Miguel Hernández-Lobato; Ting Chen 006 (CoRR, 2020)

Automatic Mapping of Landslides by the ResU-Net. - Wenwen Qi; Mengfei Wei; Wentao Yang; Chong Xu; Chao Ma 019 (Remote. Sens., 2020)

Functional Variational Inference based on Stochastic Process Generators. - Chao Ma 019; José Miguel Hernández-Lobato (NeurIPS, 2021)

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

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. - Ignacio Peis; Chao Ma 019; José Miguel Hernández-Lobato (NeurIPS, 2022)

BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis. - Weijie He; Xiaohao Mao; Chao Ma 019; Yu Huang; José Miguel Hernández-Lobato; Ting Chen 006 (WWW, 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)

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)

Research on cooperative UAV countermeasure strategy based on interception triangle. - Shuyu Liu; Ting Chen 006; Tiantian Zhao; Shanshan Liu; Chao Ma 019 (ICMLCA, 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)

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

AVID: Adapting Video Diffusion Models to World Models. - Marc Rigter; Tarun Gupta 002; Agrin Hilmkil; Chao Ma 019 (CoRR, 2024)

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training. - Chao Ma 019; Wenbo Gong 001; Meyer Scetbon; Edward Meeds (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)

Gradient Multi-Normalization for Stateless and Scalable LLM Training. - Meyer Scetbon; Chao Ma 019; Wenbo Gong 001; Edward Meeds (CoRR, 2025)

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension. - Wenbo Gong 001; Meyer Scetbon; Chao Ma 019; Edward Meeds (CoRR, 2025)