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The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of their historical actions by implementing machine-learning techniques.
Causal inference in statistics: An overview
Judea Pearl · 2009
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Local rule-based explanations of black box decision systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti · 2018
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Automated directed fairness testing
Sakshi Udeshi, Pryanshu Arora, and Sudipta Chattopadhyay · 2018
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A novel online incremental and decremental learning algorithm based on variable support vector machine
Yuantao Chen, Jie Xiong, Weihong Xu, and Jingwen Zuo · 2019
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On the separability of classes with the cross-entropy loss function
Rudrajit Das and Subhasis Chaudhuri · 2019
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Bridging adversarial robustness and gradient interpretability
Beomsu Kim, Junghoon Seo, and Taegyun Jeon · 2019
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Learning model-agnostic counterfactual explanations for tabular data
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
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What’s up with requirements engineering for artificial intelligence systems?
Khlood Ahmad, Muneera Bano, Mohamed Abdelrazek, Chetan Arora, and John Grundy · 2021
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Unlearnable examples: Making personal data unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang · 2021
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Neural-answering logical queries on knowledge graphs
Lihui Liu, Boxin Du, Heng Ji, ChengXiang Zhai, and Hanghang Tong · 2021
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Ssse: Efficiently erasing samples from trained machine learning models
Alexandra Peste, Dan Alistarh, and Christoph H Lampert · 2021
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A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
Ilia Stepin, Jose M. Alonso, Alejandro Catala, and Martín Pereira-Fariña · 2021
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Vitalhub: Robust, non-touch multi-user vital signs monitoring using depth camera-aided uwb
Zongxing Xie, Bing Zhou, Xi Cheng, Elinor Schoenfeld, and Fan Ye · 2021
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Meaningfully debugging model mistakes using conceptual counterfactual explanations
Abubakar Abid, Mert Yuksekgonul, and James Zou · 2022
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Multi-agent covering option discovery based on kronecker product of factor graphs
Jiayu Chen, Jingdi Chen, Tian Lan, and Vaneet Aggarwal · 2022
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A survey on bias in visual datasets
Simone Fabbrizzi, Symeon Papadopoulos, Eirini Ntoutsi, and Ioannis Kompatsiaris · 2022
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Revisiting frequency analysis against encrypted deduplication via statistical distribution
Jingwei Li, Guoli Wei, Jiacheng Liang, Yanjing Ren, Patrick PC Lee, and Xiaosong Zhang · 2022
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Focus: Flexible optimizable counterfactual explanations for tree ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned, and Maarten de Rijke · 2022
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A study of the attention abnormality in trojaned berts
Weimin Lyu, Songzhu Zheng, Tengfei Ma, and Chao Chen · 2022
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Learning individualized treatment rules with many treatments: A supervised clustering approach using adaptive fusion
Haixu Ma, Donglin Zeng, and Yufeng Liu · 2022
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Accelerating general-purpose lossless compression via simple and scalable parameterization
Yu Mao, Yufei Cui, Tei-Wei Kuo, and Chun Jason Xue · 2022
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Trace: A fast transformer-based general-purpose lossless compressor
Yu Mao, Yufei Cui, Tei-Wei Kuo, and Chun Jason Xue · 2022
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, and Eleni Triantafillou · 2023
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Riatig: Reliable and imperceptible adversarial text-to-image generation with natural prompts
Han Liu, Yuhao Wu, Shixuan Zhai, Bo Yuan, and Ning Zhang · 2023
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Attention-enhancing backdoor attacks against bert-based models
Weimin Lyu, Songzhu Zheng, Lu Pang, Haibin Ling, and Chao Chen · 2023
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Learning optimal group-structured individualized treatment rules with many treatments
Haixu Ma, Donglin Zeng, and Yufeng Liu · 2023
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Less is more: Understanding network bias in proof-of-work blockchains
Yifan Mao and Shaileshh Bojja Venkatakrishnan · 2023
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Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen · 2022
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Counterfactual interpolation augmentation (cia): A unified approach to enhance fairness and explainability of dnn
Yao Qiang, Chengyin Li, Marco Brocanelli, and Dongxiao Zhu · 2022
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Dice: Domain-attack invariant causal learning for improved data privacy protection and adversarial robustness
Qibing Ren, Yiting Chen, Yichuan Mo, Qitian Wu, and Junchi Yan · 2022
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Learning human driving behaviors with sequential causal imitation learning
Kangrui Ruan and Xuan Di · 2022
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Causal imitation learning via inverse reinforcement learning
Kangrui Ruan, Junzhe Zhang, Xuan Di, and Elias Bareinboim · 2022
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Contrastive boundary learning for point cloud segmentation
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, and Dacheng Tao · 2022
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Debiasing nlu models via causal intervention and counterfactual reasoning
Bing Tian, Yixin Cao, Yong Zhang, and Chunxiao Xing · 2022
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Fair machine unlearning: Data removal while mitigating disparities
Alex Oesterling, Jiaqi Ma, Flavio P Calmon, and Hima Lakkaraju · 2023
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Tkil: Tangent kernel optimization for class balanced incremental learning
Jinlin Xiang and Eli Shlizerman · 2023
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Medlens: Improve mortality prediction via medical signs selecting and regression
Xuesong Ye, Jun Wu, Chengjie Mou, and Weinan Dai · 2023
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Unleashing the power of self-supervised image denoising: A comprehensive review
Dan Zhang, Fangfang Zhou, Yuanzhou Wei, Xiao Yang, and Yuan Gu · 2023
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Stable and safe reinforcement learning via a barrier-lyapunov actor-critic approach
Liqun Zhao, Konstantinos Gatsis, and Antonis Papachristodoulou · 2023
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Xai meets biology: A comprehensive review of explainable ai in bioinformatics applications
Zhongliang Zhou, Mengxuan Hu, Mariah Salcedo, Nathan Gravel, Wayland Yeung, Aarya Venkat, Dongliang Guo, Jielu Zhang, Natarajan Kannan, and Sheng Li · 2023
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Task-driven causal feature distillation: Towards trustworthy risk prediction
Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li, and Sheng Li · 2024
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Dlora: Distributed parameter-efficient fine-tuning solution for large language model
Chao Gao and Sai Qian Zhang · 2024
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Ufid: A unified framework for input-level backdoor detection on diffusion models
Zihan Guan, Mengxuan Hu, Sheng Li, and Anil Vullikanti · 2024
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A hierarchical spatial transformer for massive point samples in continuous space
Wenchong He, Zhe Jiang, Tingsong Xiao, Zelin Xu, Shigang Chen, Ronald Fick, Miles Medina, and Christine Angelini · 2024
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 2024
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A combinatorial algorithm for approximating the optimal transport in the parallel and mpc settings
Nathaniel Lahn, Sharath Raghvendra, and Kaiyi Zhang · 2024
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Please tell me more: Privacy impact of explainability through the lens of membership inference attack
Han Liu, Yuhao Wu, Zhiyuan Yu, and Ning Zhang · 2024
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Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows
Xiaobo Ma, Abolfazl Karimpour, and Yao-Jan Wu · 2024
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To be forgotten or to be fair: Unveiling fairness implications of machine unlearning methods
Dawen Zhang, Shidong Pan, Thong Hoang, Zhenchang Xing, Mark Staples, Xiwei Xu, Lina Yao, Qinghua Lu, and Liming Zhu · 2024
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