[25] Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In World Wide Web (WWW), 2016.
[26] Danny Hernandez, Tom Brown, Tom Conerly, Nova DasSarma, Dawn Drain, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Tom Henighan, Tristan Hume, Scott Johnston, Ben Mann, Chris Olah, Catherine Olsson, Dario Amodei, Nicholas Joseph, Jared Kaplan, and Sam McCandlish. Scaling laws and interpretability of learning from repeated data. arXiv, 2022.
[27] 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, and Laurent Sifre. An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[28] Peter Izsak, Moshe Berchansky, and Omer Levy. How to train BERT with an academic budget. In Empirical Methods in Natural Language Processing (EMNLP), 2021.
[29] Jing Jiang and ChengXiang Zhai. Instance weighting for domain adaptation in NLP. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 264-271, Prague, Czech Republic, June 2007. Association for Computational Linguistics. URL https://aclanthology.org/P07-1034.
[30] Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. Bag of tricks for efficient text classification. European Chapter of the Association for Computational Linguistics (EACL), 2, 2017.
[31] David Jurgens, Srijan Kumar, Raine Hoover, Daniel A. McFarland, and Dan Jurafsky. Measuring the evolution of a scientific field through citation frames. Transactions of the Association for Computational Linguistics (TACL), 6, 2018.
[32] Angelos Katharopoulos and François Fleuret. Not all samples are created equal: Deep learning with importance sampling. In International Conference on Machine Learning (ICML), 2018.
[33] Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan. Learning from less data: A unified data subset selection and active learning framework for computer vision. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2019.
[34] Johannes Kiesel, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. SemEval2019 task 4: Hyperpartisan news detection. SemEval, 2019.
[35] Krishnateja Killamsetty, Durga S, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer. GRAD-MATCH: Gradient matching based data subset selection for efficient deep model training. In International Conference on Machine Learning (ICML), 2021.
[36] Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, and Rishabh Iyer. Glister: Generalization based data subset selection for efficient and robust learning. In Association for the Advancement of Artificial Intelligence (AAAI), 2021.
[37] Krishnateja Killamsetty, Xujiang Zhao, Feng Chen, and Rishabh Iyer. Retrieve: Coreset selection for efficient and robust semi-supervised learning. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
[38] Carolyn Kim, Ashish Sabharwal, and Stefano Ermon. Exact sampling with integer linear programs and random perturbations. In Association for the Advancement of Artificial Intelligence (AAAI), 2016.
[39] Wouter Kool, Herke van Hoof, and Max Welling. Stochastic beams and where to find them: The Gumbel-top-k trick for sampling sequences without replacement. In International Conference on Machine Learning (ICML), 2019.