Addressing Unfairness in Data Pruning for Deep Learning

Table of Links Abstract and 1 Introduction 2 Data Pruning & Fairness 3 Method & Results 4 Theoretical Analysis 5 Discussion, Acknowledgments and Disclosure of Funding, and References A Implementation Details B Theoretical Analysis for a Mixture of Gaussians 5 Discussion Data pruning—removal of uninformative samples from the training dataset—offers much needed efficiency in deep learning. However, all existing pruning algorithms are currently evaluated exclusively on their average performance, ignoring the potential impacts on fairness of the model predictions. Through a systematic study of the classification bias, we reveal that current methods often exacerbate the performance disparity across classes, which can deceptively co-occur with high average performance. At the same time, data pruning arguably operates in a similar manner to techniques that directly optimize for worst-class accuracy, suggesting that there is in fact much potential to improve model fairness by removing the right data. By examining elements of these algorithms, we find evidence that appropriately selecting the class-wise ratios but subsampling randomly within classes should reduce the implicit bias and produce more robust models. This leads us to formulate error-based class-wise pruning quotas coined MetriQ. At the same time, we find value in pruning randomly within classes, as opposed to cherry-picking, which is inherent to the existing data pruning techniques. We confirm the effectiveness of our method on a series of standard computer vision benchmarks; our simple pruning protocol traces the best trade-off between average and worst-class performance among all existing data pruning algorithms and related baselines. Additionally, we find theoretical justification for the phenomenal success of this simple strategy in a toy classification model. Limitations & Future Work. In this study, we focused our empirical evaluation exclusively on classification bias, so we only scratched the surface of fairness in deep learning. Further research is warranted to understand the effect of MetriQ and data pruning in general on worst-group accuracy and spurious correlations. Acknowledgments and Disclosure of Funding AV and JK were supported by the NSF Award 1922658. This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. References Sharat Agarwal, Himanshu Arora, Saket Anand, and Chetan Arora. Contextual diversity for active learning. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVI 16, pages 137–153. Springer, 2020. Fadhel Ayed and Soufiane Hayou. Data pruning and neural scaling laws: fundamental limitations of score-based algorithms. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. Sukarna Barua, Md Monirul Islam, Xin Yao, and Kazuyuki Murase. Mwmote–majority weighted minority oversampling technique for imbalanced data set learning. IEEE Transactions on knowledge and data engineering, 26(2):405–425, 2012. William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler. The power of ensembles for active learning in image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9368–9377, 2018. Philipp Benz, Chaoning Zhang, Adil Karjauv, and In So Kweon. Robustness may be at odds with fairness: An empirical study on class-wise accuracy. In NeurIPS 2020 Workshop on Pre-registration in Machine Learning, pages 325–342. PMLR, 2021. Simon Caton and Christian Haas. Fairness in machine learning: A survey. ACM Comput. Surv., aug 2023. Luigi Cavalli. Alcuni problemi della analisi biometrica di popolazioni naturali, 1945. George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Dataset distillation by matching training trajectories. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4750–4759, 2022. Kamalika Chaudhuri, Kartik Ahuja, Martin Arjovsky, and David Lopez-Paz. Why does throwing away data improve worst-group error? In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 4144–4188. PMLR, 23–29 Jul 2023. Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357, 2002. Robert S Chen, Brendan Lucier, Yaron Singer, and Vasilis Syrgkanis. Robust optimization for non-convex objectives. Advances in Neural Information Processing Systems, 30, 2017. Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. Class-balanced loss based on effective number of samples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9268–9277, 2019. Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, and Yongkai Wu. Coupling fairness and pruning in a single run: a bi-level optimization perspective. arXiv preprint arXiv:2312.10181, 2023. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214–226, 2012. Charles Elkan. The foundations of cost-sensitive learning. In International joint conference on artificial intelligence, volume 17, pages 973–978. Lawrence Erlbaum Associates Ltd, 2001. Dan Feldman. Core-sets: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(1): e1335, 2020. Yunzhen Feng, Shanmukha Ramakrishna Vedantam, and Julia Kempe. Embarrassingly simple dataset distillation. In The Twelfth International Conference on Learning Representations (ICLR), 2024. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin. Pruning neural networks at initialization: Why are we missing the mark? In International Conference on Learning Representations, 2021. Aidan Good, Jiaqi Lin, Xin Yu, Hannah Sieg, Mikey Fergurson, Shandian Zhe, Jerzy Wieczorek, and Thiago Serra. Recall distortion in neural network pruning and the undecayed pruning algorithm. Advances in Neural Information Processing Systems, 35:32762–32776, 2022. Mitchell A Gordon, Kevin Duh, and Jared Kaplan. Data and parameter scaling laws for neural machine translation. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5915–5922, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics. Chengcheng Guo, Bo Zhao, and Yanbing Bai. Deepcore: A comprehensive library for coreset selection in deep learning. In International Conference on Database and Expert Systems Applications, pages 181–195. Springer, 2022. Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016. Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran, Golnoosh Farnadi, Simon Lacoste-Julien, and Jose Gallego-Posada. Balancing act: Constraining disparate impact in sparse models, 2023. Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning, pages 1929–1938. PMLR, 2018. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, pages 1026–1034, 2015. Muyang He, Shuo Yang, Tiejun Huang, and Bo Zhao. Large-scale dataset pruning with dynamic uncertainty. arXiv preprint arXiv:2306.05175, 2023. Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409, 2017. Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz. Simple data balancing achieves competitive worst-group-accuracy. In Bernhard Schölkopf, Caroline Uhler, and Kun Zhang, editors, Proceedings of the First Conference on Causal Learning and Reasoning, volume 177 of Proceedings of Machine Learning Research, pages 336–351. PMLR, 11–13 Apr 2022. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, page 448–456. JMLR.org, 2015. Vinu Joseph, Shoaib Ahmed Siddiqui, Aditya Bhaskara, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland, Sheraz Ahmed, and Andreas Dengel. Going beyond classification accuracy metrics in model compression. arXiv preprint arXiv:2012.01604, 2020. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020. Kenji Kawaguchi and Haihao Lu. Ordered sgd: A new stochastic optimization framework for empirical risk minimization. In International Conference on Artificial Intelligence and Statistics, pages 669–679. PMLR, 2020. Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson. Last layer re-training is sufficient for robustness to spurious correlations. In The Eleventh International Conference on Learning Representations, 2023. Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information processing systems, 32, 2019. Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, and David Rolnick. Dataset difficulty and the role of inductive bias. arXiv preprint arXiv:2401.01867, 2024. Boqi Li and Weiwei Liu. Wat: improve the worst-class robustness in adversarial training. AAAI’23/IAAI’23/EAAI’23. AAAI Press, 2023. ISBN 978-1-57735-880-0. Xiaofeng Lin, Seungbae Kim, and Jungseock Joo. Fairgrape: Fairness-aware gradient pruning method for face attribute classification. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIII, page 414–432, Berlin, Heidelberg, 2022. Springer-Verlag. Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn. Just train twice: Improving group robustness without training group information. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 6781–6792. PMLR, 18–24 Jul 2021. Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, and Sanjiv Kumar. Teacher’s pet: understanding and mitigating biases in distillation. Transactions on Machine Learning Research, 2022. ISSN 2835-8856. Xinsong Ma, Zekai Wang, and Weiwei Liu. On the tradeoff between robustness and fairness. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N Gomez, Adrien Morisot, Sebastian Farquhar, et al. Prioritized training on points that are learnable, worth learning, and not yet learnt. In International Conference on Machine Learning, pages 15630–15649. PMLR, 2022. Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec. Coresets for data-efficient training of machine learning models. In International Conference on Machine Learning, pages 6950–6960. PMLR, 2020. Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, and John P. Dickerson. Fairness through robustness: Investigating robustness disparity in deep learning. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 466–477, New York, NY, USA, 2021. Association for Computing Machinery. Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee. Dataset distillation with infinitely wide convolutional networks. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. Michela Paganini. Prune responsibly, 2020. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite. Deep learning on a data diet: Finding important examples early in training. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. Tejas Pote, Mohammed Adnan, Yigit Yargic, and Yani Ioannou. Classification bias on a data diet. In Conference on Parsimony and Learning (Recent Spotlight Track), 2023. Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B Gupta, Xiaojiang Chen, and Xin Wang. A survey of deep active learning. ACM computing surveys (CSUR), 54(9):1–40, 2021. Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit. A constructive prediction of the generalization error across scales. In International Conference on Learning Representations (ICLR) 2020, 2020. Tim GJ Rudner, Ya Shi Zhang, Andrew Gordon Wilson, and Julia Kempe. Mind the gap: Improving robustness to subpopulation shifts with group-aware priors. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2024. Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. Distributionally robust neural networks. In International Conference on Learning Representations, 2020. Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations, 2018. Saptarshi Sinha, Hiroki Ohashi, and Katsuyuki Nakamura. Class-difficulty based methods for long-tailed visual recognition. International Journal of Computer Vision, 130(10):2517–2531, 2022. Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S. Morcos. Beyond neural scaling laws: beating power law scaling via data pruning. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune. Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data. In International Conference on Machine Learning, pages 9206–9216. PMLR, 2020. Ilia Sucholutsky and Matthias Schonlau. Soft-label dataset distillation and text dataset distillation. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2021. Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan. Equalization loss for long-tailed object recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11662–11671, 2020. Alaa Tharwat and Wolfram Schenck. A survey on active learning: State-of-the-art, practical challenges and research directions. Mathematics, 11(4), 2023. R. Thomas McCoy, Ellie Pavlick, and Tal Linzen. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, pages 3428–3448. Association for Computational Linguistics (ACL), 2020. Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. An empirical study of example forgetting during deep neural network learning. In International Conference on Learning Representations, 2019. Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu. Pruning has a disparate impact on model accuracy. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. Chaoqi Wang, Guodong Zhang, and Roger Grosse. Picking winning tickets before training by preserving gradient flow. In International Conference on Learning Representations, 2020. Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, and Aditya Krishna Menon. Robust distillation for worst-class performance: on the interplay between teacher and student objectives. In Robin J. Evans and Ilya Shpitser, editors, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, volume 216 of Proceedings of Machine Learning Research, pages 2237–2247. PMLR, 31 Jul–04 Aug 2023. Max Welling. Herding dynamical weights to learn. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, page 1121–1128, New York, NY, USA, 2009. Association for Computing Machinery. Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang. To be robust or to be fair: Towards fairness in adversarial training. In International conference on machine learning, pages 11492–11501. PMLR, 2021. Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, and Ping Li. Dataset pruning: Reducing training data by examining generalization influence. In The Eleventh International Conference on Learning Representations, 2023. Abdelrahman Zayed, Prasanna Parthasarathi, Gonçalo Mordido, Hamid Palangi, Samira Shabanian, and Sarath Chandar. Deep learning on a healthy data diet: finding important examples for fairness. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence, AAAI’23/IAAI’23/EAAI’23. AAAI Press, 2023. Bo Zhao and Hakan Bilen. Dataset condensation with distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 6514–6523, 2023. Haizhong Zheng, Rui Liu, Fan Lai, and Atul Prakash. Coverage-centric coreset selection for high pruning rates. In The Eleventh International Conference on Learning Representations, 2023. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Artem Vysogorets, Center for Data Science, New York University (amv458@nyu.edu); (2) Kartik Ahuja, Meta FAIR; (3) Julia Kempe, New York University, Meta FAIR. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Data Pruning & Fairness 2 Data Pruning & Fairness 3 Method & Results 3 Method & Results 4 Theoretical Analysis 4 Theoretical Analysis 5 Discussion, Acknowledgments and Disclosure of Funding, and References 5 Discussion, Acknowledgments and Disclosure of Funding, and References A Implementation Details A Implementation Details B Theoretical Analysis for a Mixture of Gaussians B Theoretical Analysis for a Mixture of Gaussians 5 Discussion Data pruning—removal of uninformative samples from the training dataset—offers much needed efficiency in deep learning. However, all existing pruning algorithms are currently evaluated exclusively on their average performance, ignoring the potential impacts on fairness of the model predictions. Through a systematic study of the classification bias, we reveal that current methods often exacerbate the performance disparity across classes, which can deceptively co-occur with high average performance. At the same time, data pruning arguably operates in a similar manner to techniques that directly optimize for worst-class accuracy, suggesting that there is in fact much potential to improve model fairness by removing the right data. By examining elements of these algorithms, we find evidence that appropriately selecting the class-wise ratios but subsampling randomly within classes should reduce the implicit bias and produce more robust models. This leads us to formulate error-based class-wise pruning quotas coined MetriQ. At the same time, we find value in pruning randomly within classes, as opposed to cherry-picking, which is inherent to the existing data pruning techniques. We confirm the effectiveness of our method on a series of standard computer vision benchmarks; our simple pruning protocol traces the best trade-off between average and worst-class performance among all existing data pruning algorithms and related baselines. Additionally, we find theoretical justification for the phenomenal success of this simple strategy in a toy classification model. Limitations & Future Work. In this study, we focused our empirical evaluation exclusively on classification bias, so we only scratched the surface of fairness in deep learning. Further research is warranted to understand the effect of MetriQ and data pruning in general on worst-group accuracy and spurious correlations. Limitations & Future Work. Acknowledgments and Disclosure of Funding AV and JK were supported by the NSF Award 1922658. This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. References Sharat Agarwal, Himanshu Arora, Saket Anand, and Chetan Arora. Contextual diversity for active learning. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVI 16, pages 137–153. Springer, 2020. Fadhel Ayed and Soufiane Hayou. Data pruning and neural scaling laws: fundamental limitations of score-based algorithms. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. Sukarna Barua, Md Monirul Islam, Xin Yao, and Kazuyuki Murase. Mwmote–majority weighted minority oversampling technique for imbalanced data set learning. IEEE Transactions on knowledge and data engineering, 26(2):405–425, 2012. William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler. The power of ensembles for active learning in image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9368–9377, 2018. Philipp Benz, Chaoning Zhang, Adil Karjauv, and In So Kweon. Robustness may be at odds with fairness: An empirical study on class-wise accuracy. In NeurIPS 2020 Workshop on Pre-registration in Machine Learning, pages 325–342. PMLR, 2021. Simon Caton and Christian Haas. Fairness in machine learning: A survey. ACM Comput. Surv., aug 2023. Luigi Cavalli. Alcuni problemi della analisi biometrica di popolazioni naturali, 1945. George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. Dataset distillation by matching training trajectories. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4750–4759, 2022. Kamalika Chaudhuri, Kartik Ahuja, Martin Arjovsky, and David Lopez-Paz. Why does throwing away data improve worst-group error? In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 4144–4188. PMLR, 23–29 Jul 2023. Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357, 2002. Robert S Chen, Brendan Lucier, Yaron Singer, and Vasilis Syrgkanis. Robust optimization for non-convex objectives. Advances in Neural Information Processing Systems, 30, 2017. Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. Class-balanced loss based on effective number of samples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9268–9277, 2019. Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, and Yongkai Wu. Coupling fairness and pruning in a single run: a bi-level optimization perspective. arXiv preprint arXiv:2312.10181, 2023. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214–226, 2012. Charles Elkan. The foundations of cost-sensitive learning. In International joint conference on artificial intelligence, volume 17, pages 973–978. Lawrence Erlbaum Associates Ltd, 2001. Dan Feldman. Core-sets: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(1): e1335, 2020. Yunzhen Feng, Shanmukha Ramakrishna Vedantam, and Julia Kempe. Embarrassingly simple dataset distillation. In The Twelfth International Conference on Learning Representations (ICLR), 2024. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin. Pruning neural networks at initialization: Why are we missing the mark? In International Conference on Learning Representations, 2021. Aidan Good, Jiaqi Lin, Xin Yu, Hannah Sieg, Mikey Fergurson, Shandian Zhe, Jerzy Wieczorek, and Thiago Serra. Recall distortion in neural network pruning and the undecayed pruning algorithm. Advances in Neural Information Processing Systems, 35:32762–32776, 2022. Mitchell A Gordon, Kevin Duh, and Jared Kaplan. Data and parameter scaling laws for neural machine translation. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5915–5922, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics. Chengcheng Guo, Bo Zhao, and Yanbing Bai. Deepcore: A comprehensive library for coreset selection in deep learning. In International Conference on Database and Expert Systems Applications, pages 181–195. Springer, 2022. Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016. Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran, Golnoosh Farnadi, Simon Lacoste-Julien, and Jose Gallego-Posada. Balancing act: Constraining disparate impact in sparse models, 2023. Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning, pages 1929–1938. PMLR, 2018. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, pages 1026–1034, 2015. Muyang He, Shuo Yang, Tiejun Huang, and Bo Zhao. Large-scale dataset pruning with dynamic uncertainty. arXiv preprint arXiv:2306.05175, 2023. Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409, 2017. Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz. Simple data balancing achieves competitive worst-group-accuracy. In Bernhard Schölkopf, Caroline Uhler, and Kun Zhang, editors, Proceedings of the First Conference on Causal Learning and Reasoning, volume 177 of Proceedings of Machine Learning Research, pages 336–351. PMLR, 11–13 Apr 2022. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, page 448–456. JMLR.org, 2015. Vinu Joseph, Shoaib Ahmed Siddiqui, Aditya Bhaskara, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland, Sheraz Ahmed, and Andreas Dengel. Going beyond classification accuracy metrics in model compression. arXiv preprint arXiv:2012.01604, 2020. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020. Kenji Kawaguchi and Haihao Lu. Ordered sgd: A new stochastic optimization framework for empirical risk minimization. In International Conference on Artificial Intelligence and Statistics, pages 669–679. PMLR, 2020. Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson. Last layer re-training is sufficient for robustness to spurious correlations. In The Eleventh International Conference on Learning Representations, 2023. Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information processing systems, 32, 2019. Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, and David Rolnick. Dataset difficulty and the role of inductive bias. arXiv preprint arXiv:2401.01867, 2024. Boqi Li and Weiwei Liu. Wat: improve the worst-class robustness in adversarial training. AAAI’23/IAAI’23/EAAI’23. AAAI Press, 2023. ISBN 978-1-57735-880-0. Xiaofeng Lin, Seungbae Kim, and Jungseock Joo. Fairgrape: Fairness-aware gradient pruning method for face attribute classification. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIII, page 414–432, Berlin, Heidelberg, 2022. Springer-Verlag. Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn. Just train twice: Improving group robustness without training group information. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 6781–6792. PMLR, 18–24 Jul 2021. Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, and Sanjiv Kumar. Teacher’s pet: understanding and mitigating biases in distillation. Transactions on Machine Learning Research, 2022. ISSN 2835-8856. Xinsong Ma, Zekai Wang, and Weiwei Liu. On the tradeoff between robustness and fairness. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N Gomez, Adrien Morisot, Sebastian Farquhar, et al. Prioritized training on points that are learnable, worth learning, and not yet learnt. In International Conference on Machine Learning, pages 15630–15649. PMLR, 2022. Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec. Coresets for data-efficient training of machine learning models. In International Conference on Machine Learning, pages 6950–6960. PMLR, 2020. Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, and John P. Dickerson. Fairness through robustness: Investigating robustness disparity in deep learning. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 466–477, New York, NY, USA, 2021. Association for Computing Machinery. Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee. Dataset distillation with infinitely wide convolutional networks. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. Michela Paganini. Prune responsibly, 2020. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite. Deep learning on a data diet: Finding important examples early in training. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. Tejas Pote, Mohammed Adnan, Yigit Yargic, and Yani Ioannou. Classification bias on a data diet. In Conference on Parsimony and Learning (Recent Spotlight Track), 2023. Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B Gupta, Xiaojiang Chen, and Xin Wang. A survey of deep active learning. ACM computing surveys (CSUR), 54(9):1–40, 2021. Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit. A constructive prediction of the generalization error across scales. In International Conference on Learning Representations (ICLR) 2020, 2020. Tim GJ Rudner, Ya Shi Zhang, Andrew Gordon Wilson, and Julia Kempe. Mind the gap: Improving robustness to subpopulation shifts with group-aware priors. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2024. Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. Distributionally robust neural networks. In International Conference on Learning Representations, 2020. Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations, 2018. Saptarshi Sinha, Hiroki Ohashi, and Katsuyuki Nakamura. Class-difficulty based methods for long-tailed visual recognition. International Journal of Computer Vision, 130(10):2517–2531, 2022. Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S. Morcos. Beyond neural scaling laws: beating power law scaling via data pruning. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune. Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data. In International Conference on Machine Learning, pages 9206–9216. PMLR, 2020. Ilia Sucholutsky and Matthias Schonlau. Soft-label dataset distillation and text dataset distillation. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2021. Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan. Equalization loss for long-tailed object recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11662–11671, 2020. Alaa Tharwat and Wolfram Schenck. A survey on active learning: State-of-the-art, practical challenges and research directions. Mathematics, 11(4), 2023. R. Thomas McCoy, Ellie Pavlick, and Tal Linzen. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, pages 3428–3448. Association for Computational Linguistics (ACL), 2020. Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. An empirical study of example forgetting during deep neural network learning. In International Conference on Learning Representations, 2019. Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu. Pruning has a disparate impact on model accuracy. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. Chaoqi Wang, Guodong Zhang, and Roger Grosse. Picking winning tickets before training by preserving gradient flow. In International Conference on Learning Representations, 2020. Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, and Aditya Krishna Menon. Robust distillation for worst-class performance: on the interplay between teacher and student objectives. In Robin J. Evans and Ilya Shpitser, editors, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, volume 216 of Proceedings of Machine Learning Research, pages 2237–2247. PMLR, 31 Jul–04 Aug 2023. Max Welling. Herding dynamical weights to learn. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, page 1121–1128, New York, NY, USA, 2009. Association for Computing Machinery. Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang. To be robust or to be fair: Towards fairness in adversarial training. In International conference on machine learning, pages 11492–11501. PMLR, 2021. Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, and Ping Li. Dataset pruning: Reducing training data by examining generalization influence. In The Eleventh International Conference on Learning Representations, 2023. Abdelrahman Zayed, Prasanna Parthasarathi, Gonçalo Mordido, Hamid Palangi, Samira Shabanian, and Sarath Chandar. Deep learning on a healthy data diet: finding important examples for fairness. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence, AAAI’23/IAAI’23/EAAI’23. AAAI Press, 2023. Bo Zhao and Hakan Bilen. Dataset condensation with distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 6514–6523, 2023. Haizhong Zheng, Rui Liu, Fan Lai, and Atul Prakash. Coverage-centric coreset selection for high pruning rates. In The Eleventh International Conference on Learning Representations, 2023. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Artem Vysogorets, Center for Data Science, New York University (amv458@nyu.edu); (2) Kartik Ahuja, Meta FAIR; (3) Julia Kempe, New York University, Meta FAIR. Authors: Authors: (1) Artem Vysogorets, Center for Data Science, New York University (amv458@nyu.edu); (2) Kartik Ahuja, Meta FAIR; (3) Julia Kempe, New York University, Meta FAIR.