Reference shelf

Related areas and papers

A lightweight map of neighboring literatures, with each area curated as a Semble collection and synced into the site at build time.

19 areas 92 papers Updated Apr 2, 2026

This page is generated from a set of curated Semble collections. Each related area here corresponds to a collection where I add, remove, and reorganize papers as the reading map changes.

When the site builds, Astro pulls the latest public Semble data and turns those collections into the grouped shelves you see on this page. The result is still intentionally non-exhaustive: each area is meant as a hand-curated starting shelf rather than a comprehensive bibliography.

Core areas

collective action
data dividends
influence
scaling laws
selection and coresets
poisoning
semivalues
user-generated content

Related areas

active learning
augmentation and curriculum
causality
distillation
fairness via data interventions
membership inference
meta-learning
model collapse
reinforcement learning for data valuation
training dynamics
unlearning

Shelf grouping

Core areas

Related area

collective action

5 papers

Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm

Rushabh Solanki, Elliot Creager / 2nd Workshop on Regulatable ML at NeurIPS 2024 (2024)
Algorithmic Collective Action in Machine Learning

Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic / International Conference on Machine Learning (2023)
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

Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor, Brent Hecht / Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (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 (2019)

Related area

data dividends

7 papers

Sharing the Algorithm: The Tax Solution to Generative AI

Jeremy Bearer-Friend, Sarah Polcz / Columbia Journal of Tax Law (2025)
Sharing the Winnings of AI with Data Dividends: Challenges with "Meritocratic" Data Valuation

Nicholas Vincent, Brent Hecht / EAAMO '23 (2023)
Berggruen Institute

Yakov Feygin, Brent Hecht, Matthew Prewitt, Hanlin Li, Nicholas Vincent, Chirag Lala, Luisa Scarcella / Berggruen Institute white paper (2021)
Nonrivalry and the Economics of Data

Charles I. Jones, Christopher Tonetti / American Economic Review (2020)
DCI Quick Guide: Key Concepts To Know About Data Flow - Data Catalyst

Tarun Wadhwa / Data Catalyst (2020)
Mapping the Potential and Pitfalls of "Data Dividends" as a Means of Sharing the Profits of Artificial Intelligence

Nicholas Vincent, Yichun Li, Renee Zha, Brent Hecht / arXiv (2019)
Should We Treat Data as Labor? Moving Beyond “Free”

Imanol Arrieta-Ibarra, Leonard Goff, Diego Jiménez-Hernández, Jaron Lanier, E. Glen Weyl / AEA Papers and Proceedings (2018)

Related area

influence

7 papers

Better Training Data Attribution via Better Inverse Hessian-Vector Products

Wang, Andrew, Nguyen, Elisa, Yang, Runshi, Bae, Juhan, McIlraith, Sheila A., Grosse, Roger / arXiv.org (2025)
TRAK: Attributing Model Behavior at Scale

Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry / International Conference on Machine Learning (2023)
Datamodels: Understanding Predictions with Data and Data with Predictions

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry / International Conference on Machine Learning (2022)
Influence Functions in Deep Learning Are Fragile

Samyadeep Basu, Phillip Pope, Soheil Feizi / ICLR (2021)
Estimating Training Data Influence by Tracing Gradient Descent

Pruthi, Garima, Liu, Frederick, Kale, Satyen, Sundararajan, Mukund / Advances in Neural Information Processing Systems (2020)
Representer Point Selection for Explaining Deep Neural Networks

Yeh, Chih-Kuan, Kim, Joon, Yen, Ian En-Hsu, Ravikumar, Pradeep K. / Advances in Neural Information Processing Systems (2018)
Understanding Black-box Predictions via Influence Functions

Pang Wei Koh, Percy Liang / International Conference on Machine Learning (2017)

Related area

scaling laws

5 papers

Beyond neural scaling laws: beating power law scaling via data pruning

Sorscher, Ben, Geirhos, Robert, Shekhar, Shashank, Ganguli, Surya, Morcos, Ari S. / arXiv.org (2023)
Training Compute-Optimal Large Language Models

Hoffmann, Jordan, Borgeaud, Sebastian, Mensch, Arthur, Buchatskaya, Elena, Cai, Trevor, Rutherford, Eliza, Casas, Diego de Las, Hendricks, Lisa Anne, Welbl, Johannes, Clark, Aidan, Hennigan, Tom, Noland, Eric, Millican, Katie, Driessche, George van den, Damoc, Bogdan, Guy, Aurelia, Osindero, Simon, Simonyan, Karen, Elsen, Erich, Rae, Jack W., Vinyals, Oriol, Sifre, Laurent / arXiv.org (2022)
Scaling Laws for Autoregressive Generative Modeling

Henighan, Tom, Kaplan, Jared, Katz, Mor, Chen, Mark, Hesse, Christopher, Jackson, Jacob, Jun, Heewoo, Brown, Tom B., Dhariwal, Prafulla, Gray, Scott, Hallacy, Chris, Mann, Benjamin, Radford, Alec, Ramesh, Aditya, Ryder, Nick, Ziegler, Daniel M., Schulman, John, Amodei, Dario, McCandlish, Sam / arXiv.org (2020)
Scaling Laws for Neural Language Models

Kaplan, Jared, McCandlish, Sam, Henighan, Tom, Brown, Tom B., Chess, Benjamin, Child, Rewon, Gray, Scott, Radford, Alec, Wu, Jeffrey, Amodei, Dario / arXiv.org (2020)
Deep Learning Scaling is Predictable, Empirically

Hestness, Joel, Narang, Sharan, Ardalani, Newsha, Diamos, Gregory, Jun, Heewoo, Kianinejad, Hassan, Patwary, Md. Mostofa Ali, Yang, Yang, Zhou, Yanqi / arXiv.org (2017)

Related area

selection and coresets

3 papers

GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning

Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh Iyer / Proceedings of the AAAI Conference on Artificial Intelligence (2021)
Coresets for Data-efficient Training of Machine Learning Models

Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec / International Conference on Machine Learning (2020)
Active Learning for Convolutional Neural Networks: A Core-Set Approach

Ozan Sener, Silvio Savarese / ICLR (2018)

Related area

poisoning

5 papers

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: Evaluating Backdooring Attacks on Deep Neural Networks

Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg / IEEE Access (2019)
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, Dawn Song / arXiv (2017)
Certified Defenses for Data Poisoning Attacks

Steinhardt, Jacob, Koh, Pang Wei W., Liang, Percy S. / Advances in Neural Information Processing Systems (2017)
Poisoning Attacks against Support Vector Machines

Battista Biggio, Blaine Nelson, Pavel Laskov / Proceedings of the 29th International Conference on Machine Learning (2012)

Related area

semivalues

6 papers

Data Banzhaf: A Robust Data Valuation Framework for Machine Learning

Wang, Jiachen T., Jia, Ruoxi / arXiv.org (2023)
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning

Jiachen T. Wang, Ruoxi Jia / International Conference on Artificial Intelligence and Statistics (2023)
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

Yongchan Kwon, James Zou / International Conference on Artificial Intelligence and Statistics (2022)
Efficient task-specific data valuation for nearest neighbor algorithms

Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas Spanos, Dawn Song / Proceedings of the VLDB Endowment (2019)
Data Shapley: Equitable Valuation of Data for Machine Learning

Amirata Ghorbani, James Zou / International Conference on Machine Learning (2019)
17. A Value for n-Person Games

L. S. Shapley / Contributions to the Theory of Games (AM-28), Volume II (1953)

Related area

user-generated content

7 papers

A large-scale audit of dataset licensing and 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)
LAION-5B: An open large-scale dataset for training next generation image-text models

Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev / NeurIPS 2022 Datasets and Benchmarks (2022)
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, Matt Gardner / Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu / Journal of Machine Learning Research (2020)
Wiki-40B: Multilingual Language Model Dataset

Mandy Guo, Zihang Dai, Denny Vrandečić, Rami Al-Rfou’ / Proceedings of the Twelfth Language Resources and Evaluation Conference (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 Brümmer, Milan Dojchinovski, Sebastian Hellmann / Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (2016)

Shelf grouping

Related areas

Related area

active learning

5 papers

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)
Active Learning with Statistical Models

D. A. Cohn, Z. Ghahramani, M. I. Jordan / Journal of Artificial Intelligence Research (1996)
A Sequential Algorithm for Training Text Classifiers

David D. Lewis, William A. Gale / SIGIR ’94 (1994)
Query by committee

H. S. Seung, M. Opper, H. Sompolinsky / Proceedings of the fifth annual workshop on Computational learning theory (1992)

Related area

augmentation and curriculum

5 papers

Randaugment: Practical automated data augmentation with a reduced search space

Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le / 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features

Yun, Sangdoo, Han, Dongyoon, Oh, Seong Joon, Chun, Sanghyuk, Choe, Junsuk, Yoo, Youngjoon / Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
AutoAugment: Learning Augmentation Strategies From Data

Cubuk, Ekin D., Zoph, Barret, Mane, Dandelion, Vasudevan, Vijay, Le, Quoc V. / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
mixup: Beyond Empirical Risk Minimization

Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz / ICLR 2018 (2018)
Curriculum learning

Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston / Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Related area

causality

4 papers

Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

Rosenbaum, Paul R., Rubin, Donald / Journal of the American Statistical Association (2010)
Causality

Judea Pearl / Cambridge University Press (2009)
The Central Role of the Propensity Score in Observational Studies for Causal Effects

Paul R. Rosenbaum, Donald B. Rubin / Biometrika (1983)
Estimating causal effects of treatments in randomized and nonrandomized studies.

Donald B. Rubin / Journal of Educational Psychology (1974)

Related area

distillation

4 papers

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)
Distilling the Knowledge in a Neural Network

Geoffrey Hinton, Oriol Vinyals, Jeffrey Dean / NeurIPS Deep Learning and Representation Learning Workshop (2015)

Related area

fairness via data interventions

6 papers

Datasheets for Datasets

Gebru, Timnit, Morgenstern, Jamie, Vecchione, Briana, Vaughan, Jennifer Wortman, Wallach, Hanna, Daumé III, Hal, Crawford, Kate / Communications of the ACM (2021)
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Joy Buolamwini, Timnit Gebru / Conference on Fairness, Accountability and Transparency (2018)
Optimized Pre-Processing for Discrimination Prevention

Calmon, Flavio, Wei, Dennis, Vinzamuri, Bhanukiran, Natesan Ramamurthy, Karthikeyan, Varshney, Kush R. / Advances in Neural Information Processing Systems (2017)
Certifying and Removing Disparate Impact

Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, Suresh Venkatasubramanian / Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015)
Learning Fair Representations

Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork / International Conference on Machine Learning (2013)
Data preprocessing techniques for classification without discrimination

Faisal Kamiran, Toon Calders / Knowledge and Information Systems (2011)

Related area

membership inference

6 papers

Detecting Non-Membership in LLM Training Data via Rank Correlations

Pranav Shetty, Mirazul Haque, Zhiqiang Ma, Xiaomo Liu / Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) (2026)
Membership Inference Attacks From First Principles

Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr / 2022 IEEE Symposium on Security and Privacy (SP) (2022)
Extracting Training Data from Large Language Models

Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel / 30th USENIX Security Symposium (USENIX Security 21) (2021)
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, Samuel, Giacomelli, Irene, Fredrikson, Matt, Jha, Somesh / arXiv.org (2018)
Membership Inference Attacks Against Machine Learning Models

Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov / 2017 IEEE Symposium on Security and Privacy (SP) (2017)
Differential Privacy

Cynthia Dwork / Lecture Notes in Computer Science (2006)

Related area

meta-learning

3 papers

DataRater: Meta-Learned Dataset Curation

Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeffrey Dean, Hado van Hasselt, David Silver / NeurIPS 2025 (2025)
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting

Shu, Jun, Xie, Qi, Yi, Lixuan, Zhao, Qian, Zhou, Sanping, Xu, Zongben, Meng, Deyu / Advances in Neural Information Processing Systems (2019)
Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun / International Conference on Machine Learning (2018)

Related area

model collapse

3 papers

The Curse of Recursion: Training on Generated Data Makes Models Forget

Shumailov, Ilia, Shumaylov, Zakhar, Zhao, Yiren, Gal, Yarin, Papernot, Nicolas, Anderson, Ross / arXiv.org (2024)
AI models collapse when trained on recursively generated data

Shumailov, Ilia, Shumaylov, Zakhar, Zhao, Yiren, Papernot, Nicolas, Anderson, Ross, Gal, Yarin / Nature (2024)
Self-Consuming Generative Models Go MAD

Alemohammad, Sina, Casco-Rodriguez, Josue, Luzi, Lorenzo, Humayun, Ahmed Imtiaz, Babaei, Hossein, LeJeune, Daniel, Siahkoohi, Ali, Baraniuk, Richard G. / arXiv.org (2023)

Related area

reinforcement learning for data valuation

1 papers

Data Valuation using Reinforcement Learning

Jinsung Yoon, Sercan Arik, Tomas Pfister / International Conference on Machine Learning (2020)

Related area

training dynamics

3 papers

Deep Learning on a Data Diet: Finding Important Examples Early in Training

Paul, Mansheej, Ganguli, Surya, Dziugaite, Gintare Karolina / Advances in Neural Information Processing Systems (2021)
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi / Proceedings of the 2020 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)

Related area

unlearning

7 papers

Unlearning Traces the Influential Training Data of Language Models

Masaru Isonuma, Ivan Titov / Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2024)
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning

Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi / Algorithmic Learning Theory (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)
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks

Aditya Golatkar, Alessandro Achille, Stefano Soatto / 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Certified Data Removal from Machine Learning Models

Chuan Guo, Tom Goldstein, Awni Hannun, Laurens Van Der Maaten / International Conference on Machine Learning (2020)
Making AI Forget You: Data Deletion in Machine Learning

Ginart, Antonio, Guan, Melody, Valiant, Gregory, Zou, James Y. / Advances in Neural Information Processing Systems (2019)
Towards Making Systems Forget with Machine Unlearning

Yinzhi Cao, Junfeng Yang / 2015 IEEE Symposium on Security and Privacy (2015)