21 citations · 49 across the 8 of their papers we have counts for
6 papers · 1 filter
InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate
Zhengyang Hu, Yanzhi Chen, Hanxiang Ren +5
Measuring statistical dependency between high-dimensional random variables is a fundamental task in data science and machine learning. Neural mutual information (MI) estimators off…
Neural Mutual Information Estimation with Vector Copulas
Yanzhi Chen, Zijing Ou, Adrian Weller +1
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 networ…
On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective
Shoaib Ahmed Siddiqui, Yanzhi Chen, Juyeon Heo +2
Recent works have successfully applied Large Language Models (LLMs) to function modeling tasks. However, the reasons behind this success remain unclear. In this work, we propose a…
Mutual Information Multinomial Estimation
Yanzhi Chen, Zijing Ou, Adrian Weller +1
Estimating mutual information (MI) is a fundamental yet challenging task in data science and machine learning. This work proposes a new estimator for mutual information. Our main d…
Scalable Infomin Learning
Yanzhi Chen, Weihao Sun, Yingzhen Li +1
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…
Do Concept Bottleneck Models Learn as Intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt +3
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets. Such models aim to incorporate pre-specified, high-level concepts into the learning pr…