Black box influence function
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 … 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
Black box influence function
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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. 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, …
WebAppendix: Understanding Black-box Predictions via Influence Functions Pang Wei Koh 1Percy Liang A. Deriving the influence function I up,params For completeness, we … http://export.arxiv.org/pdf/1703.04730
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 … WebJun 10, 2024 · Idea: use Influence Functions to observe the influence of the test samples from the training samples. The degree of influence of a single training sample z on all model parameters θ is calculated as: Where ε is the weight of sample z relative to other training samples. If there are n samples, it can be interpreted as 1/n.
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WebMar 3, 2024 · Influence Functions for PyTorch. This is a PyTorch reimplementation of Influence Functions from the ICML2024 best paper: Understanding Black-box … psychological integrity 意味WebAppendix: Understanding Black-box Predictions via Influence Functions Pang Wei Koh 1Percy Liang A. Deriving the influence function I up,params For completeness, we provide a standard derivation of the influence function I up,params in the context of loss minimiza-tion (M-estimation). This derivation is based on asymp- psychological intake report sampleWebMay 1, 2024 · Up, params influence where 𝐻𝜃 ≝ 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. psychological interference in communicationWebMay 30, 2024 · Interpreting black box predictions using Fisher kernels. In Artificial Intelligence and Statistics (AISTATS), pages 3382-3390, 2024. Understanding black-box predictions via influence functions hospitals in punjab indiaWebOn linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging … hospitals in prince william county vaWebInfluence functions are a good alternative to deletion diagnostics, but only for models with a 2nd order diffentiable loss function with respect to its parameters, such as neural … psychological institutionWebJul 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 … psychological intelligence analysis