Related areas and papers

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• Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
  Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal / ICLR (2020)
• Active Learning Literature Survey
  Burr Settles / University of Wisconsin-Madison, Computer Sciences Technical Report 1648 (2009)
• Query by Committee
  H. Sebastian Seung, Manfred Opper, Haim Sompolinsky / COLT (1992)

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• CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
  Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo / ICCV (2019)
• AutoAugment: Learning Augmentation Strategies From Data
  Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le / CVPR (2019)
• mixup: Beyond Empirical Risk Minimization
  Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz / ICLR 2018 (2018)
• Curriculum Learning
  Yoshua Bengio, Jerome Louradour, Ronan Collobert, Jason Weston / ICML 2009 (2009)

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• Causality: Models, Reasoning, and Inference
  Judea Pearl / Cambridge University Press (2009)
• Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
  Paul R. Rosenbaum, Donald B. Rubin / Journal of the American Statistical Association (1984)
• The Central Role of the Propensity Score in Observational Studies for Causal Effects
  Paul R. Rosenbaum, Donald B. Rubin / Biometrika (1983)

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• Can "Conscious Data Contribution" Help Users to Exert "Data Leverage" Against Technology Companies?
  Nicholas Vincent, Brent Hecht / Proceedings of the ACM on Human-Computer Interaction (2021)
• Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies
  Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor, Brent Hecht / FAccT (2021)
• "Data Strikes": Evaluating the Effectiveness of a New Form of Collective Action Against Technology Companies
  Nicholas Vincent, Brent Hecht, Shilad Sen / The World Wide Web Conference (WWW) (2019)

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• Dataset Condensation with Distribution Matching
  Bo Zhao, Hakan Bilen / WACV (2023)
• Dataset Condensation with Gradient Matching
  Bo Zhao, Konda Reddy Mopuri, Hakan Bilen / ICLR (2021)
• Dataset Distillation
  Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros / International Conference on Learning Representations (2018)

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• Datasheets for Datasets
  Gebru, Timnit, Morgenstern, Jamie, Vecchione, Briana, Vaughan, Jennifer Wortman, Wallach, Hanna, Daumé III, Hal, Crawford, Kate / arXiv:1803.09010 (2018)
• Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
  Buolamwini, Joy, Gebru, Timnit / Proceedings of the Conference on Fairness, Accountability and Transparency (FAT*) (2018)
• Learning Fair Representations
  Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork / ICML (2013)
• Data Preprocessing Techniques for Classification Without Discrimination
  Faisal Kamiran, Toon Calders / Knowledge and Information Systems (2012)

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• TRAK: Attributing Model Behavior at Scale
  Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry / ICML (2023)
• Estimating Training Data Influence by Tracing Gradient Descent
  Garima Pruthi, Frederick Liu, Mukund Sundararajan, Satyen Kale / Advances in Neural Information Processing Systems (NeurIPS) (2020)
• Representer Point Selection for Explaining Deep Neural Networks
  Chih-Kuan Yeh, Joon Sik Kim, Ian E. H. Yen, Pradeep Ravikumar / NeurIPS (2018)
• Understanding Black-box Predictions via Influence Functions
  Pang Wei Koh, Percy Liang / Proceedings of the 34th International Conference on Machine Learning (ICML) (2017)

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• Membership Inference Attacks From First Principles
  Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramer / IEEE Symposium on Security and Privacy (2022)
• Extracting Training Data from Large Language Models
  Carlini, Nicholas, Tramer, Florian, Wallace, Eric, Jagielski, Matthew, Herbert-Voss, Ariel, Lee, Katherine, Roberts, Adam, Brown, Tom B., Song, Dawn, Erlingsson, {\'U}lfar, Oprea, Alina, Papernot, Nicolas / Proceedings of USENIX Security Symposium (2021)
• Membership Inference Attacks Against Machine Learning Models
  Reza Shokri

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• Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
  Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng / NeurIPS (2019)
• Learning to Reweight Examples for Robust Deep Learning
  Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun / ICML (2018)
• DataRater: Meta-Learned Dataset Curation

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• AI models collapse when trained on recursively generated data
  Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, Yarin Gal / Nature (2024)
• Self-Consuming Generative Models Go MAD
  Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, Richard G. Baraniuk / arXiv (2023)

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• Backdoor or Feature? A New Perspective on Data Poisoning
  Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Andrew Ilyas, Hadi Salman, Aleksander Madry / ICLR (2023)
• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
  Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg / IEEE Access (2019)
• Certified Defenses for Data Poisoning Attacks
  Jacob Steinhardt, Pang Wei Koh, Percy Liang / NeurIPS (2017)
• Poisoning Attacks against Support Vector Machines
  Battista Biggio, Blaine Nelson, Pavel Laskov / Proceedings of the 29th International Conference on Machine Learning (ICML) (2012)

Part of the datacounterfactuals.org reading lists.

• Training Compute-Optimal Large Language Models
  Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, Laurent Sifre / NeurIPS 2022 (2022)
• Scaling Laws for Autoregressive Generative Modeling
  Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, Sam McCandlish / arXiv (2020)
• Scaling Laws for Neural Language Models
  Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei / arXiv preprint (2020)
• Deep Learning Scaling is Predictable, Empirically
  Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory F. Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, Yanqi Zhou / arXiv (2017)

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• GLISTER: Generalization Based Data Subset Selection for Efficient and Robust Learning
  Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh Iyer / AAAI (2021)
• Coresets for Data-efficient Training of Machine Learning Models
  Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec / International Conference on Machine Learning (ICML) (2020)
• Active Learning for Convolutional Neural Networks: A Core-Set Approach
  Ozan Sener, Silvio Savarese / ICLR (2018)

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• Data Banzhaf: A Robust Data Valuation Framework for Machine Learning
  Jiachen T. Wang, Ruoxi Jia / AISTATS (2023)
• Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
  Yongchan Kwon, James Zou / AISTATS (2022)
• Data Shapley: Equitable Valuation of Data for Machine Learning
  Amirata Ghorbani, James Zou / International Conference on Machine Learning (2019)

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• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
  Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Yejin Choi, Noah A. Smith / Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
• An Empirical Study of Example Forgetting during Deep Neural Network Learning
  Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, Geoff Gordon / ICLR (2019)

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• Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
  Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi / ALT (2021)
• Machine Unlearning
  Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot / IEEE Symposium on Security and Privacy (S&P) (2021)
• Certified Data Removal from Machine Learning Models
  Chuan Guo, Tom Goldstein, Awni Hannun, Laurens Van Der Maaten / ICML (2020)

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• The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
  Shayne Longpre, Robert Mahari, Anthony Chen, Naana Obeng-Marnu, Damien Sileo, William Brannon, Niklas Muennighoff, Nathan Khazam, Jad Kabbara, Kartik Perisetla, Xinyi Wu, Enrico Shippole, Kurt Bollacker, Tongshuang Wu, Luis Villa, Sandy Pentland, Sara Hooker / Nature Machine Intelligence (2024)
• Wiki-40B: Multilingual Language Model Dataset
  Mandy Guo, Zihang Dai, Denny Vrandecic, Rami Al-Rfou / LREC (2020)
• Measuring the Importance of User-Generated Content to Search Engines
  Nicholas Vincent, Isaac Johnson, Patrick Sheehan, Brent Hecht / Proceedings of the International AAAI Conference on Web and Social Media (2019)
• DBpedia Abstracts: A Large-Scale, Open, Multilingual NLP Training Corpus
  Martin Brummer, Milan Dojchinovski, Sebastian Hellmann / LREC (2016)