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Yanzhi Chen

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Neural Mutual Information Estimation with Vector Copulas

Yanzhi Chen
Zijing Ou
Adrian Weller
Michael Gutmann

Estimating mutual information (MI) is a fundamental task in data science and machine learning. Existing estimators mainly rely on either highly flexible models (e. g. , neural networks), which require large amounts of data, or overly simplified models (e. g. , Gaussian copula), which fail to capture complex distributions. Drawing upon recent vector copula theory, we propose a principled interpolation between these two extremes to achieve a better trade-off between complexity and capacity. Experiments on state-of-the-art synthetic benchmarks and real-world data with diverse modalities demonstrate the advantages of the proposed method.

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ICML Conference 2023 Conference Paper

Is Learning Summary Statistics Necessary for Likelihood-free Inference?

Yanzhi Chen
Michael U. Gutmann
Adrian Weller

Likelihood-free inference (LFI) is a set of techniques for inference in implicit statistical models. A longstanding question in LFI has been how to design or learn good summary statistics of data, but this might now seem unnecessary due to the advent of recent end-to-end (i. e. neural network-based) LFI methods. In this work, we rethink this question with a new method for learning summary statistics. We show that learning sufficient statistics may be easier than direct posterior inference, as the former problem can be reduced to a set of low-dimensional, easy-to-solve learning problems. This suggests us to explicitly decouple summary statistics learning from posterior inference in LFI. Experiments on diverse inference tasks with different data types validate our hypothesis.

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NeurIPS Conference 2022 Conference Paper

Scalable Infomin Learning

Yanzhi Chen
weihao sun
Yingzhen Li
Adrian Weller

The task of infomin learning aims to learn a representation with high utility while being uninformative about a specified target, with the latter achieved by minimising the mutual information between the representation and the target. It has broad applications, ranging from training fair prediction models against protected attributes, to unsupervised learning with disentangled representations. Recent works on infomin learning mainly use adversarial training, which involves training a neural network to estimate mutual information or its proxy and thus is slow and difficult to optimise. Drawing on recent advances in slicing techniques, we propose a new infomin learning approach, which uses a novel proxy metric to mutual information. We further derive an accurate and analytically computable approximation to this proxy metric, thereby removing the need of constructing neural network-based mutual information estimators. Compared to baselines, experiments on algorithmic fairness, disentangled representation learning and domain adaptation verify that our method can more effectively remove unwanted information with limited time budget.

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ICLR Conference 2021 Conference Paper

Neural Approximate Sufficient Statistics for Implicit Models

Yanzhi Chen
Dinghuai Zhang
Michael U. Gutmann
Aaron C. Courville
Zhanxing Zhu

We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representations of the data with the help of deep neural networks. The infomax learning procedure does not need to estimate any density or density ratio. We apply our approach to both traditional approximate Bayesian computation and recent neural likelihood methods, boosting their performance on a range of tasks.

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ICML Conference 2020 Conference Paper

On Breaking Deep Generative Model-based Defenses and Beyond

Yanzhi Chen
Renjie Xie
Zhanxing Zhu

Deep neural networks have been proven to be vulnerable to the so-called adversarial attacks. Recently there have been efforts to defend such attacks with deep generative models. These defenses often predict by inverting the deep generative models rather than simple feedforward propagation. Such defenses are difficult to attack due to the obfuscated gradients caused by inversion. In this work, we propose a new white-box attack to break these defenses. The idea is to view the inversion phase as a dynamical system, through which we extract the gradient w. r. t the image by backtracking its trajectory. An amortized strategy is also developed to accelerate the attack. Experiments show that our attack better breaks state-of-the-art defenses (e. g DefenseGAN, ABS) than other attacks (e. g BPDA). Additionally, our empirical results provide insights for understanding the weaknesses of deep generative model defenses.

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