Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models

分享嘉宾： Yufei Wu (吴雨霏)
单位： 鲁汶大学 (KU Leuven)

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报告简介

Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In this study, we test and improve the robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks. To this end, we conduct systematic analyses on a toy example and analyze both synthetic and real data using a popular cognitive model, the Drift Diffusion Models (DDM). First, we study the sensitivity of ABI to contaminants with tools from robust statistics: the empirical influence function and the breakdown point. Next, we propose a data augmentation or noise injection approach that incorporates contamination distribution into the data-generating process during training. We examine several candidate distributions and evaluate their performance and cost in terms of accuracy and efficiency loss relative to a standard estimator. Introducing contaminants from a Cauchy distribution during training considerably increases the robustness of the neural density estimator as measured by bounded influence functions and a much higher breakdown point.

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报告时间

北京时间 [GMT+8] 2025年1月16日 (周四) 20:30~21:30

会议信息： ZOOM 会议号：待确认

报告语言： 英文

主持人： 潘晚坷

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参考文献

Wu, Y., Radev, S., & Tuerlinckx, F. (2024). Testing and improving the robustness of amortized bayesian inference for cognitive models. arXiv. https://doi.org/10.48550/arXiv.2412.20586

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