Black box influence function
WebFortunately, influence functions give us an efficient approximation. The idea is to compute the parameter change if z were upweighted by some small ϵ, giving us new parameters ^θϵ,z ≜ argminθ∈Θ(1− ϵ)1 n∑n i=1L(zi,θ)+ϵL(z,θ). A classic result tells us that the influence of upweighting z on the parameters ^θ is given by. WebAug 6, 2024 · In this paper, we use influence functions — a classic technique from robust statistics — to trace a model's prediction through the learning algorithm and back to its …
Black box influence function
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WebarXiv.org e-Print archive WebIn this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most …
Web1. 1.Influence functions: definitions and theory 2.Efficiently calculating influence functions 3. Validations 4. Uses cases. Roadmap. 2. • Reviving an “old technique” from … WebJan 1, 2024 · Consequently, the black-box function output in Step 6 includes uncertainty. Specifically, f ( x) = f ( x) + ɛ, where f ( x) is the output of the black-box system, and ɛ is a random variable that follows an unknown probability distribution with a mean of 0 and a variance of σ 2.
http://proceedings.mlr.press/v70/koh17a/koh17a-supp.pdf WebUnderstanding Black-box Predictions via Influence Functions 2. Approach Consider a prediction problem from some input space X (e.g., images) to an output space Y(e.g., labels). We are given training points z 1;:::;z n, where z i = (x i;y i) 2 XY . For a point z and parameters 2 , let L(z; ) be the loss, and let 1 n P n i=1 L(z i; ) be the em ...
WebJul 1, 2024 · In their seminal paper Understanding Black-box Predictions via Influence Functions , Koh & Liang (2024) first co-opted influence functions to the domain of machine learning. Since then, influence functions have been applied on a variety of machine learning tasks, including explaining model predictions, dataset relabelling and …
WebNov 1, 2024 · With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Often we want to … shoes influencersWebAppendix: Understanding Black-box Predictions via Influence Functions Pang Wei Koh 1Percy Liang A. Deriving the influence function I up,params For completeness, we … rachelle chang hawaiiWebMar 3, 2024 · Influence Functions for PyTorch. This is a PyTorch reimplementation of Influence Functions from the ICML2024 best paper: Understanding Black-box … rachelle chadwickWebJan 26, 2024 · The related techniques include influence function and LIME, which motivated our work. 3.1 Model Explanation Problem. Consider a black box decision system that is supported by some machine learning models, a prediction can be assigned to a test point. The parameters of the models are trained from the training dataset. rachelle chambersWebSep 26, 2024 · Understanding black-box predictions via influence functions XIE Ruiming 2. Outline • Background • Taylor's Formula • Newton's Method • Introduction • Influence Function • Definition • Efficiently Calculating Influence • Validation and Extensions • Use cases of influence function 3. ... rachelle cannon beachWebNov 1, 2024 · In this paper, we address this issue and propose second-order influence functions for identifying influential groups in test-time predictions. For linear models and across different sizes of ... shoes in german translationWebUnderstanding Black-box Predictions via Influence Functions Background information. 这篇论文是ICML 2024 best paper。工作做得不错，写的也好，非常严谨。两个作者都是Stanford的。 ... 对比了influence function的估计和leave-one-out retraining的差距，结果表明用共轭梯度法和近似方法得到的估计 ... rachelle chapman