ASTRA: Understanding the practical impact of robustness for probabilistic programs
ASTRA: Understanding the practical impact of robustness for probabilistic programs
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发表时间：
2023
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通讯作者：
Zixin Huang;Saikat Dutta;Sasa Misailovic
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作者：
Zixin Huang;Saikat Dutta;Sasa Misailovic
我们提出了第一个系统的鲁棒性转换的有效性研究的24个不同的概率程序代表广义线性模型，混合模型和时间序列模型。我们从文献中评估了每个模型的五个鲁棒性转换。我们量化和提出的见解（1）后验预测精度的提高和（2）鲁棒艾德程序的执行时间开销，在存在三个输入噪声模型。为了自动评估各种鲁棒性转换，我们开发了ASTRA -一种新的框架，用于量化概率程序的鲁棒性，并探索鲁棒性和执行时间之间的权衡。我们的实验结果表明，现有的转换通常只适用于特定的噪声模型，可以显着增加执行时间，并与推理算法有非平凡的相互作用。
We present the first systematic study of effectiveness of robustness transformations on a diverse set of 24 probabilistic programs representing generalized linear models, mixture models, and time-series models. We evaluate five robustness transformations from literature on each model. We quantify and present insights on (1) the improvement of the posterior prediction accuracy and (2) the execution time overhead of the robustified programs, in the presence of three input noise models. To automate the evaluation of various robustness transformations, we developed ASTRA – a novel framework for quantifying the robustness of probabilistic programs and exploring the trade-offs between robustness and execution time. Our experimental results indicate that the existing transformations are often suitable only for specific noise models, can significantly increase execution time, and have non-trivial interaction with the inference algorithms.