Existing Literature about Machine Unlearning

Overview

Machine Unlearning Papers

2021

Brophy and Lowd. Machine Unlearning for Random Forests. In ICML 2021.

Bourtoule et al. Machine Unlearning. In IEEE Symposium on Security and Privacy 2021.

Gupta et al. Adaptive Machine Unlearning. In Neurips 2021.

Huang et al. Unlearnable Examples: Making Personal Data Unexploitable. In ICLR 2021.

Neel et al. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. In ALT 2021.

Schelter et al. HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. In SIGMOD 2021.

Sekhari et al. Remember What You Want to Forget: Algorithms for Machine Unlearning. In Neurips 2021.

arXiv

Chen et al. Graph Unlearning. In arXiv 2021.

Chen et al. Machine unlearning via GAN. In arXiv 2021.

Fu et al. Bayesian Inference Forgetting. In arXiv 2021.

He et al. DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks. In arXiv 2021.

Khan and Swaroop. Knowledge-Adaptation Priors. In arXiv 2021.

Marchant et al. Hard to Forget: Poisoning Attacks on Certified Machine Unlearning . In arXiv 2021.

Parne et al. Machine Unlearning: Learning, Polluting, and Unlearning for Spam Email. In arXiv 2021.

Tarun et al. Fast Yet Effective Machine Unlearning . In arXiv 2021.

Ullah et al. Machine Unlearning via Algorithmic Stability. In arXiv 2021.

Wang et al. Federated Unlearning via Class-Discriminative Pruning . In arXiv 2021.

Warnecke et al. Machine Unlearning for Features and Labels. In arXiv 2021.

Zeng et al. Learning to Refit for Convex Learning Problems In arXiv 2021.

2020

Guo et al. Certified Data Removal from Machine Learning Models. In ICML 2020.

Golatkar et al. Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks. In CVPR 2020.

Wu et. al DeltaGrad: Rapid Retraining of Machine Learning Models. In ICML 2020.

arXiv

Aldaghri et al. Coded Machine Unlearning. In arXiv 2020.

Baumhauer et al. Machine Unlearning: Linear Filtration for Logit-based Classifiers. In arXiv 2020.

Garg et al. Formalizing Data Deletion in the Context of the Right to be Forgotten. In arXiv 2020.

Chen et al. When Machine Unlearning Jeopardizes Privacy. In arXiv 2020.

Felps et al. Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale. In arXiv 2020.

Golatkar et al. Mixed-Privacy Forgetting in Deep Networks. In arXiv 2020.

Golatkar et al. Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations. In arXiv 2020.

Izzo et al. Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations. In arXiv 2020.

Liu et al. Learn to Forget: User-Level Memorization Elimination in Federated Learning. In arXiv 2020.

Sommer et al. Towards Probabilistic Verification of Machine Unlearning. In arXiv 2020.

Yiu et al. Learn to Forget: User-Level Memorization Elimination in Federated Learning. In arXiv 2020.

Yu et al. Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary. In arXiv 2020.

2019

Chen et al. A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine. In Cluster Computing 2019.

Ginart et al. Making AI Forget You: Data Deletion in Machine Learning. In NeurIPS 2019.

Schelter. “Amnesia” – Towards Machine Learning Models That Can Forget User Data Very Fast. In AIDB 2019.

Shintre et al. Making Machine Learning Forget. In APF 2019.

Du et al. Lifelong Anomaly Detection Through Unlearning. In CCS 2019.

Wang et al. Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. In IEEE Symposium on Security and Privacy 2019.

arXiv

Tople te al. Analyzing Privacy Loss in Updates of Natural Language Models. In arXiv 2019.

2018

Cao et al. Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning. In ASIACCS 2018.

European Union. GDPR, 2018.

State of California. California Consumer Privacy Act, 2018.

Veale et al. Algorithms that remember: model inversion attacks and data protection law. In The Royal Society 2018.

Villaronga et al. Humans Forget, Machines Remember: Artificial Intelligence and the Right to Be Forgotten. In Computer Law & Security Review 2018.

2017

Kwak et al. Let Machines Unlearn--Machine Unlearning and the Right to be Forgotten. In SIGSEC 2017.

Shokri et al. Membership Inference Attacks Against Machine Learning Models. In SP 2017.

Before 2017

Cao and Yang. Towards Making Systems Forget with Machine Unlearning. In IEEE Symposium on Security and Privacy 2015.

Tsai et al. Incremental and decremental training for linear classification. In KDD 2014.

Karasuyama and Takeuchi. Multiple Incremental Decremental Learning of Support Vector Machines. In NeurIPS 2009.

Duan et al. Decremental Learning Algorithms for Nonlinear Langrangian and Least Squares Support Vector Machines. In OSB 2007.

Romero et al. Incremental and Decremental Learning for Linear Support Vector Machines. In ICANN 2007.

Tveit et al. Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients. In DaWaK 2003.

Tveit and Hetland. Multicategory Incremental Proximal Support Vector Classifiers. In KES 2003.

Cauwenberghs and Poggio. Incremental and Decremental Support Vector Machine Learning. In NeurIPS 2001.

Canada. PIPEDA, 2000.

Owner
Jonathan Brophy
PhD student at UO.
Jonathan Brophy
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel

536 Dec 20, 2022
PyTorch Implementation of DSB for Score Based Generative Modeling. Experiments managed using Hydra.

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling This repository contains the implementation for the paper Diffusion

James Thornton 50 Jan 03, 2023
Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

HeyangXue1997 103 Dec 23, 2022
Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"

Contrastive Self-Supervised Learning on CIFAR-10 Paper "Towards the Generalization of Contrastive Self-Supervised Learning", Weiran Huang, Mingyang Yi

Weiran Huang 13 Nov 30, 2022
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Python library with Neural Networks for Image Segmentation based on Keras and TensorFlow. The main features of this library are: High level API (just

Pavel Yakubovskiy 4.2k Jan 09, 2023
Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
Plug and play transformer you can find network structure and official complete code by clicking List

Plug-and-play Module Plug and play transformer you can find network structure and official complete code by clicking List The following is to quickly

8 Mar 27, 2022
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Introduction This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset

Tao Ruijie 277 Dec 31, 2022
Selene is a Python library and command line interface for training deep neural networks from biological sequence data such as genomes.

Selene is a Python library and command line interface for training deep neural networks from biological sequence data such as genomes.

Troyanskaya Laboratory 323 Jan 01, 2023
A set of tools to pre-calibrate and calibrate (multi-focus) plenoptic cameras (e.g., a Raytrix R12) based on the libpleno.

COMPOTE: Calibration Of Multi-focus PlenOpTic camEra. COMPOTE is a set of tools to pre-calibrate and calibrate (multifocus) plenoptic cameras (e.g., a

ComSEE - Computers that SEE 4 May 10, 2022
Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

Vis'Yerres SGDF - Modules Woob Vous avez le sentiment que l'intranet des Scouts

Thomas Touhey (pas un pseudonyme) 3 Dec 24, 2022
[Official] Exploring Temporal Coherence for More General Video Face Forgery Detection(ICCV 2021)

Exploring Temporal Coherence for More General Video Face Forgery Detection(FTCN) Yinglin Zheng, Jianmin Bao, Dong Chen, Ming Zeng, Fang Wen Accepted b

57 Dec 28, 2022
Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

IterMVS official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo' Introduction IterMVS is a novel lear

Fangjinhua Wang 127 Jan 04, 2023
Controlling the MicriSpotAI robot from scratch

Abstract: The SpotMicroAI project is designed to be a low cost, easily built quadruped robot. The design is roughly based off of Boston Dynamics quadr

Florian Wilk 405 Jan 05, 2023
Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021) This repository is for the following paper: "Investigating Attention

52 Nov 19, 2022
Conditional Gradients For The Approximately Vanishing Ideal

Conditional Gradients For The Approximately Vanishing Ideal Code for the paper: Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Appro

IOL Lab @ ZIB 0 May 25, 2022
The audio-video synchronization of MKV Container Format is exploited to achieve data hiding

The audio-video synchronization of MKV Container Format is exploited to achieve data hiding, where the hidden data can be utilized for various management purposes, including hyper-linking, annotation

Maxim Zaika 1 Nov 17, 2021
Camera-caps - Examine the camera capabilities for V4l2 cameras

camera-caps This is a graphical user interface over the v4l2-ctl command line to

Jetsonhacks 25 Dec 26, 2022
Differentiable simulation for system identification and visuomotor control

gradsim gradSim: Differentiable simulation for system identification and visuomotor control gradSim is a unified differentiable rendering and multiphy

105 Dec 18, 2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

[ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training The Unreasonable Effectiveness of

VITA 44 Dec 23, 2022