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ORCIDMingtian Zhang
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2020 – today
- 2025
[j2]Mingtian Zhang
, Anjia Yang, Jian Weng, Min-Rong Chen, Huang Zeng, Yi Liu, Xiaoli Liu, Zhihua Xia:
Efficient and Privacy-Preserving Ride Matching Over Road Networks Against Malicious ORH Server. IEEE Trans. Inf. Forensics Secur. 20: 2372-2386 (2025)- [c12]Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato:
Training Neural Samplers with Reverse Diffusive KL Divergence. AISTATS 2025: 5167-5175 - [c11]Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber:
Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. ICLR 2025 - [i22]Mingtian Zhang, Jiajun He, Wenlin Chen, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber:
Towards Training One-Step Diffusion Models Without Distillation. CoRR abs/2502.08005 (2025) - [i21]Leyang Wang, Mingtian Zhang, Zijing Ou, David Barber:
VarDiU: A Variational Diffusive Upper Bound for One-Step Diffusion Distillation. CoRR abs/2508.20646 (2025) - 2024
- [c10]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. ICML 2024 - [c9]William Muldrew, Peter Hayes, Mingtian Zhang, David Barber:
Active Preference Learning for Large Language Models. ICML 2024 - [c8]Andi Zhang, Mingtian Zhang, Damon Wischik:
Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective. NeurIPS 2024 - [c7]Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth:
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. NeurIPS 2024 - [i20]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. CoRR abs/2402.03008 (2024) - [i19]William Muldrew, Peter Hayes, Mingtian Zhang, David Barber:
Active Preference Learning for Large Language Models. CoRR abs/2402.08114 (2024) - [i18]Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber:
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning. CoRR abs/2402.12177 (2024) - [i17]Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber:
Diffusion Model With Optimal Covariance Matching. CoRR abs/2406.10808 (2024) - [i16]Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth:
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. CoRR abs/2408.09453 (2024) - [i15]Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato:
Training Neural Samplers with Reverse Diffusive KL Divergence. CoRR abs/2410.12456 (2024) - 2023
- [j1]Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong:
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. Nat. Mac. Intell. 5(12): 1356-1368 (2023) - [c6]Mingtian Zhang, Yitong Sun, Chen Zhang, Steven McDonagh:
Spread Flows for Manifold Modelling. AISTATS 2023: 11435-11456 - [c5]Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber:
Moment Matching Denoising Gibbs Sampling. NeurIPS 2023 - [i14]Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber:
Moment Matching Denoising Gibbs Sampling. CoRR abs/2305.11650 (2023) - [i13]Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong:
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence. CoRR abs/2308.14991 (2023) - 2022
- [c4]Mingtian Zhang, Peter Hayes, David Barber:
Generalization Gap in Amortized Inference. NeurIPS 2022 - [i12]Mingtian Zhang, James Townsend, Ning Kang, David Barber:
Parallel Neural Local Lossless Compression. CoRR abs/2201.05213 (2022) - [i11]Mingtian Zhang, Peter Hayes, David Barber:
Generalization Gap in Amortized Inference. CoRR abs/2205.11640 (2022) - [i10]Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber:
Improving VAE-based Representation Learning. CoRR abs/2205.14539 (2022) - [i9]Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun, Steven McDonagh:
Out-of-Distribution Detection with Class Ratio Estimation. CoRR abs/2206.03955 (2022) - [i8]Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz, David Barber:
Integrated Weak Learning. CoRR abs/2206.09496 (2022) - [i7]Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol:
Towards Healing the Blindness of Score Matching. CoRR abs/2209.07396 (2022) - 2021
- [c3]Mingtian Zhang, Andi Zhang, Steven McDonagh:
On the Out-of-distribution Generalization of Probabilistic Image Modelling. NeurIPS 2021: 3811-3823 - [c2]Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong:
AFEC: Active Forgetting of Negative Transfer in Continual Learning. NeurIPS 2021: 22379-22391 - [i6]Mingtian Zhang, Andi Zhang, Steven McDonagh:
On the Out-of-distribution Generalization of Probabilistic Image Modelling. CoRR abs/2109.02639 (2021) - [i5]Mingtian Zhang, Yitong Sun, Steven McDonagh, Chen Zhang:
Flow Based Models For Manifold Data. CoRR abs/2109.14216 (2021) - [i4]Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong:
AFEC: Active Forgetting of Negative Transfer in Continual Learning. CoRR abs/2110.12187 (2021) - 2020
- [c1]Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber:
Spread Divergence. ICML 2020: 11106-11116
2010 – 2019
- 2019
- [i3]Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber:
Variational f-divergence Minimization. CoRR abs/1907.11891 (2019) - [i2]Mohammed Amin Abdullah, Hang Ren, Haitham Bou-Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, Jun Wang:
Wasserstein Robust Reinforcement Learning. CoRR abs/1907.13196 (2019) - 2018
- [i1]David Barber, Mingtian Zhang, Raza Habib, Thomas Bird:
Spread Divergences. CoRR abs/1811.08968 (2018)
Coauthor Index
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