I am a PhD student in UCL supervised by Prof. David Barber and Prof. Brooks Paige.
I am interested in methodologies and applications of probabilistic modelling.
mail@mingtian.ai
Selected Publications and Preprints
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Moment Matching Denoising Gibbs Sampling
ArXiv preprint
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
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Spread Flows for Manifold Modelling
AISTATS 2023
Mingtian Zhang, Yitong Sun, Steven McDonagh and Chen Zhang
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Generalization Gap in Amortized Inference
NeurIPS 2022
Mingtian Zhang, Peter Hayes and David Barber
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Towards Healing the Blindness of Score Matching
Score-Based Methods Workshop, NeurIPS 2022
Mingtian Zhang, Oscar Keys, Peter Hayes, David Barber, Brooks Paige and François-Xavier Briol
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Out-of-Distribution Detection with Class Ratio Estimation
Machine Learning Safety Workshop, NeurIPS 2022
Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun and Steven McDonagh
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Integrated Weak Learning
ArXiv preprint
Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz and David Barber
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Improving VAE-based Representation Learning
ArXiv preprint
Mingtian Zhang, Tim Xiao, Brooks Paige and David Barber
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Parallel Neural Local Lossless Compression
ArXiv preprint
Mingtian Zhang, James Townsend, Ning Kang and David Barber
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On the Out-of-Distribution Generalization
of Probabilistic Image Modelling
NeurIPS 2021
Mingtian Zhang, Andi Zhang and Steven McDonagh
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Active Forgetting of Negative Transfer in Continual Learning
NeurIPS 2021
Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Kaisheng Ma, Chenglong Bao, Jun Zhu and Yi Zhong
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Solipsistic Reinforcement Learning
Self-Supervision for Reinforcement Learning Workshop, ICLR 2021
Mingtian Zhang, Peter Hayes, Tim Xiao, Andi Zhang and David Barber
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Spread Divergence
ICML 2020
Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib and David Barber
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Variational f-Divergence Minimization
Information Theory and Machine Learning Workshop, NeurIPS 2019
Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu and David Barber