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.

Influence Functions with (Eigenvalue-corrected) Kronecker-Factored Approximate Curvature

pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation

A simple PyTorch implementation of influence functions.

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

👋 Influenciae is a Tensorflow Toolbox for Influence Functions

Data-efficient Fine-tuning for LLM-based Recommendation (SIGIR'24)

[CVPR 2023] Regularizing Second-Order Influences for Continual Learning

Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)

Influence Estimation for Gradient-Boosted Decision Trees

[EMNLP-2022 Findings] Code for paper “ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback”.

A simple Jax implementation of influence functions.

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.

Time series data contribution via influence functions

An Empirical Study of Memorization in NLP (ACL 2022)

Tiny Tutorial on https://arxiv.org/abs/1703.04730

Official implementation of "Deeper Understanding of Black-box Predictions via Generalized Influence Functions".

Source code for 'Understanding impacts of human feedback via influence functions'

This is an implementation of the paper ”Interpreting Twitter User Geolocation“.