This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
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Updated
Oct 29, 2023 - Python
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"
Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020
A simple PyTorch implementation of influence functions.
Influence Estimation for Gradient-Boosted Decision Trees
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
Influence Functions with (Eigenvalue-corrected) Kronecker-Factored Approximate Curvature
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
👋 Influenciae is a Tensorflow Toolbox for Influence Functions
Tiny Tutorial on https://arxiv.org/abs/1703.04730
Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)
Data-efficient Fine-tuning for LLM-based Recommendation (SIGIR'24)
This is a [Stable] PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
[CVPR 2023] Regularizing Second-Order Influences for Continual Learning
This is an implementation of the paper ”Interpreting Twitter User Geolocation“.
Time series data contribution via influence functions
This repo provides an implementation of the paper Interpreting Twitter User Geolocation.
[EMNLP-2022 Findings] Code for paper “ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback”.
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