Black box influence function

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August undefined, 2024

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 ... WebMar 14, 2024 · Influence functions to interpret input variations [Koh and Liang, 2024] and quantitative measures to capture an input's degree of influence [Datta et al., 2016] have diverged from the causality ...

How do we analyse a black-box function? ResearchGate

http://export.arxiv.org/pdf/1703.04730 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, … ctmh wild berry blast

Understanding Black-box Predictions via Influence Functions

WebApplying influence functions (and some Taylor-expansion approximations) we get: (1) I u p, p a r a m s ( z) = − H θ ^ − 1 ⋅ ∇ θ L ( z, θ ^) Where H θ ^ ∈ R p × p is the Hessian of … 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. WebJul 25, 2024 · This video introduces the technology: Influence function for unbiased recommendation, including introduces the existing bias problems in the recommendation systems, as well as the existing methods for addressing the problems, and the motivation and technical details of our methods, and future works of our methods. ... P. … earthquake in turkey region

Appendix: Understanding Black-box Predictions via …

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 … Web1. 1.Influence functions: definitions and theory 2.Efficiently calculating influence functions 3. Validations 4. Uses cases. Roadmap. 2. • Reviving an “old technique” from … ctmh yvonne bidgoodWebAug 22, 2024 · Understanding black-box predictions via influence functions. arXiv preprint arXiv:1703.04730 (2024). Google Scholar Digital Library; Josua Krause, Adam Perer, and Kenney Ng. 2016. Interacting with predictions: Visual inspection of black-box machine learning models. In Proceedings of the CHI Conference on Human Factors in Computing … ctmh website

"WebUnderstanding 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 ... " - Black box influence function

Black box influence function

Understanding Black-box Predictions via Influence Functions - PMLR

WebJan 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. WebUnderstanding Black-box Predictions via Influence Functions Pang Wei Koh & Perry Liang Presented by –Theo, Aditya, Patrick 1. ... •With influence function were able to tell that the 4 children in training were 30-40 times more influential and that the child indicator variable extremely

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WebAppendix: Understanding Black-box Predictions via Inﬂuence Functions Pang Wei Koh 1Percy Liang A. Deriving the inﬂuence function I up,params For completeness, we … 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., …

WebMar 14, 2024 · Influence functions to interpret input variations [Koh and Liang, 2024] and quantitative measures to capture an input's degree of influence [Datta et al., 2016] have … WebUnderstanding Black-box Predictions via Influence Functions. International Conference on Machine Learning (ICML), 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 training data, thereby identifying training points most ...

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 … WebSep 12, 2024 · Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. While influence estimates align well with leave-one-out retraining for linear models, recent works have shown this alignment is often poor in neural networks. In this work, we investigate the specific factors that cause ...

WebJun 25, 2024 · Influence functions, however, are not well-understood in the context of deep learning with non-convex loss functions. In this paper, we provide a comprehensive and large-scale empirical study of successes and failures of influence functions in neural network models trained on datasets such as Iris, MNIST, CIFAR-10 and ImageNet.

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 … ctmh wonderland layoutsWebMay 1, 2024 · Up, params influence where 𝐻෡𝜃 ≝ 1 𝑛 σ𝑖=1 𝑛 ∇ 𝜃 2 𝐿(𝑧, ෠𝜃) is the Hessian and is positive deﬁnite (PD) by assumption. In essence, we form a quadratic approximation to the empirical risk around ෠𝜃 and take a single Newton step; see appendix A for a derivation. earthquake in vijayawada today newshttp://proceedings.mlr.press/v70/koh17a/koh17a-supp.pdf earthquake in valley park moWebJul 17, 2024 · %0 Conference Paper %T Understanding Black-box Predictions via Influence Functions %A Pang Wei Koh %A Percy Liang %B Proceedings of the 34th … ctmh writing in the sandWebNov 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 … ctmh you are enough instructionsWebSep 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. ... earthquake in utah 2020Title: Selecting Robust Features for Machine Learning Applications using … ctmh workshop cutting guides