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Machine learning models can fail on subgroups that are underrepresented during training.
“Robust statistics: the approach based on influence functions”
Frank Hampel, Elvezio Ronchetti, Peter Rousseeuw and Werner Stahel · 2011
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“Deep Residual Learning for Image Recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2015
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“Deep Learning Face Attributes in the Wild”
Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang · 2015
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“Understanding Black-box Predictions via Influence Functions”
Pang Koh and Percy Liang · 2017
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“A broad-coverage challenge corpus for sentence understanding through inference”
Adina Williams, Nikita Nangia and Samuel Bowman · 2017
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“Gender shades: Intersectional accuracy disparities in commercial gender classification”
Joy Buolamwini and Timnit Gebru · 2018
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Alina Kuznetsova et al · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani and David Lopez-Paz · 2019
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“Data shapley: Equitable valuation of data for machine learning”
Amirata Ghorbani and James Zou · 2019
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“Decoupling representation and classifier for long-tailed recognition”
Bingyi Kang et al · 2019
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“The pile: An 800gb dataset of diverse text for language modeling”
Leo Gao et al · 2020
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“StereoSet: Measuring stereotypical bias in pretrained language models”
Moin Nadeem, Anna Bethke and Siva Reddy · 2020
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“Hidden stratification causes clinically meaningful failures in machine learning for medical imaging”
Lauren Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro and Christopher Ré · 2020
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“Estimating Training Data Influence by Tracing Gradient Descent”
Garima Pruthi, Frederick Liu, Mukund Sundararajan and Satyen Kale · 2020
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“Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization”
Shiori Sagawa, Pang Koh, Tatsunori. Hashimoto and Percy Liang · 2020
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“An investigation of why overparameterization exacerbates spurious correlations”
Shiori Sagawa, Aditi Raghunathan, Pang Koh and Percy Liang · 2020
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“The pitfalls of simplicity bias in neural networks”
Harshay Shah et al · 2020
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“Large image datasets: A pyrrhic win for computer vision?”
Abeba Birhane and Vinay Prabhu · 2021
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“Multimodal datasets: misogyny, pornography, and malignant stereotypes”
Abeba Birhane, Vinay Prabhu and Emmanuel Kahembwe · 2021
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“Excavating AI: the politics of images in machine learning training sets”
Kate Crawford and Trevor Paglen · 2021
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“Just Train Twice: Improving Group Robustness without Training Group Information”
Evan Liu et al · 2021
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“A Survey on Bias and Fairness in Machine Learning”
Ninareh Mehrabi et al · 2021
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“Editing a classifier by rewriting its prediction rules”
Shibani Santurkar et al · 2021
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“Machine Bias *”
Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner · 2022
“Scaling up influence functions”
Andrea Schioppa, Polina Zablotskaia, David Vilar and Artem Sokolov · 2022
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“Quantifying and mitigating the impact of label errors on model disparity metrics”
Julius Adebayo, Melissa Hall, Bowen Yu and Bobbie Chern · 2023
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Rhys Compton, Lily Zhang, Aahlad Puli and Rajesh Ranganath · 2023
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“Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies”
Emilio Ferrara · 2023
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“Spurious features everywhere-large-scale detection of harmful spurious features in imagenet”
Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko and Matthias Hein · 2023
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Cited alongside, same era.
“If Influence Functions are the Answer, Then What is the Question?”
Juhan Bae et al · 2022
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“Domino: Discovering systematic errors with cross-modal embeddings”
Sabri Eyuboglu et al · 2022
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“Training Data Influence Analysis and Estimation: A Survey”
Zayd Hammoudeh and Daniel Lowd · 2022
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“Simple data balancing achieves competitive worst-group-accuracy”
Badr Idrissi, Martin Arjovsky, Mohammad Pezeshki and David Lopez-Paz · 2022
Cited alongside, same era.
“Datamodels: Predicting Predictions from Training Data”
Andrew Ilyas et al · 2022
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“Distilling Model Failures as Directions in Latent Space”
Saachi Jain, Hannah Lawrence, Ankur Moitra and Aleksander Madry · 2022
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“In-context example selection with influences”
Tai Nguyen and Eric Wong · 2023
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“Discovering environments with XRM”
Mohammad Pezeshki et al · 2023
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“TRAK: Attributing Model Behavior at Scale”
Sung Park et al · 2023
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“Don’t blame dataset shift! Shortcut learning due to gradients and cross entropy”
Aahlad Puli, Lily Zhang, Yoav Wald and Rajesh Ranganath · 2023
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“Simple and Fast Group Robustness by Automatic Feature Reweighting”
Shikai Qiu, Andres Potapczynski, Pavel Izmailov and Andrew Wilson · 2023
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“Identifying and eliminating CSAM in generative ML training data and models”
David Thiel · 2023
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“Unifying corroborative and contributive attributions in large language models”
Theodora Worledge et al · 2023
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“Data selection for language models via importance resampling”
Sang Xie, Shibani Santurkar, Tengyu Ma and Percy Liang · 2023
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“DsDm: Model-Aware Dataset Selection with Datamodels”, 2024
Logan Engstrom, Axel Feldmann and Aleksander Madry · 2024
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“Decomposing and Editing Model Predictions”
Harshay Shah, Andrew Ilyas and Aleksander Madry · 2024
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“FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes”
Haonan Wang, Ziwei Wu and Jingrui He · 2024
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“Less: Selecting influential data for targeted instruction tuning”
Mengzhou Xia et al · 2024
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