Spread Divergences. - David Barber; Mingtian Zhang; Raza Habib; Thomas Bird (CoRR, 2018)

Variational f-divergence Minimization. - Mingtian Zhang; Thomas Bird; Raza Habib; Tianlin Xu; David Barber (CoRR, 2019)

Wasserstein Robust Reinforcement Learning. - Mohammed Amin Abdullah 001; Hang Ren; Haitham Bou-Ammar; Vladimir Milenkovic; Rui Luo 001; Mingtian Zhang; Jun Wang 012 (CoRR, 2019)

Spread Divergence. - Mingtian Zhang; Peter Hayes; Thomas Bird; Raza Habib; David Barber (ICML, 2020)

AFEC: Active Forgetting of Negative Transfer in Continual Learning. - Liyuan Wang; Mingtian Zhang; Zhongfan Jia; Qian Li 040; Chenglong Bao; Kaisheng Ma; Jun Zhu 001; Yi Zhong (NeurIPS, 2021)

On the Out-of-distribution Generalization of Probabilistic Image Modelling. - Mingtian Zhang; Andi Zhang 001; Steven McDonagh (NeurIPS, 2021)

Flow Based Models For Manifold Data. - Mingtian Zhang; Yitong Sun; Steven McDonagh; Chen Zhang (CoRR, 2021)

Generalization Gap in Amortized Inference. - Mingtian Zhang; Peter Hayes; David Barber (NeurIPS, 2022)

Parallel Neural Local Lossless Compression. - Mingtian Zhang; James Townsend; Ning Kang 001; David Barber (CoRR, 2022)

Improving VAE-based Representation Learning. - Mingtian Zhang; Tim Z. Xiao; Brooks Paige; David Barber (CoRR, 2022)

Out-of-Distribution Detection with Class Ratio Estimation. - Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yitong Sun; Steven McDonagh (CoRR, 2022)

Integrated Weak Learning. - Peter Hayes; Mingtian Zhang; Raza Habib; Jordan Burgess; Emine Yilmaz; David Barber (CoRR, 2022)

Towards Healing the Blindness of Score Matching. - Mingtian Zhang; Oscar Key; Peter Hayes; David Barber; Brooks Paige; François-Xavier Briol (CoRR, 2022)

Spread Flows for Manifold Modelling. - Mingtian Zhang; Yitong Sun; Chen Zhang; Steven McDonagh (AISTATS, 2023)

Moment Matching Denoising Gibbs Sampling. - Mingtian Zhang; Alex Hawkins-Hooker; Brooks Paige; David Barber (NeurIPS, 2023)

Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. - Liyuan Wang; Xingxing Zhang 001; Qian Li 040; Mingtian Zhang; Hang Su 006; Jun Zhu 001; Yi Zhong (Nat. Mac. Intell., 2023)

Diffusive Gibbs Sampling. - Wenlin Chen; Mingtian Zhang; Brooks Paige; José Miguel Hernández-Lobato; David Barber (ICML, 2024)

Active Preference Learning for Large Language Models. - William Muldrew; Peter Hayes; Mingtian Zhang; David Barber (ICML, 2024)

Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective. - Andi Zhang 001; Mingtian Zhang; Damon Wischik (NeurIPS, 2024)

Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. - Jake Cunningham; Giorgio Giannone; Mingtian Zhang; Marc Peter Deisenroth (NeurIPS, 2024)

Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning. - Mingtian Zhang; Shawn Lan; Peter Hayes; David Barber (CoRR, 2024)

Diffusion Model With Optimal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (CoRR, 2024)

Training Neural Samplers with Reverse Diffusive KL Divergence. - Jiajun He 003; Wenlin Chen; Mingtian Zhang; David Barber; José Miguel Hernández-Lobato (AISTATS, 2025)

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (ICLR, 2025)

Efficient and Privacy-Preserving Ride Matching Over Road Networks Against Malicious ORH Server. - Mingtian Zhang; Anjia Yang; Jian Weng 001; Min-Rong Chen; Huang Zeng; Yi Liu 053; Xiaoli Liu; Zhihua Xia (IEEE Trans. Inf. Forensics Secur., 2025)

Towards Training One-Step Diffusion Models Without Distillation. - Mingtian Zhang; Jiajun He 003; Wenlin Chen; Zijing Ou; José Miguel Hernández-Lobato; Bernhard Schölkopf; David Barber (CoRR, 2025)