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Coreset selection, which aims to select a subset of the most informative training samples, is a long-standing learning problem that can benefit many downstream tasks such as data-efficient learning, continual learning, neural architecture search, active learning, etc.
1905
Earlier work this paper cites.
Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions—i. Mathematical programming 14
1978
Earlier work this paper cites.
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural computation 1
1989
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Farahani, R.Z., Hekmatfar, M.: Facility location: concepts, models, algorithms and case studies (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Settles, B.: Active learning literature survey (2009)
2009
Earlier work this paper cites.
Welling, M.: Herding dynamical weights to learn. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp. 1121–1128 (2009)
2009
Earlier work this paper cites.
Chen, Y., Welling, M., Smola, A.: Super-samples from kernel herding. The Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (2010)
2010
Earlier work this paper cites.
Feldman, D., Faulkner, M., Krause, A.: Scalable training of mixture models via coresets. In: NIPS. pp. 2142–2150. Citeseer (2011)
2011
Earlier work this paper cites.
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)
2011
Earlier work this paper cites.
Settles, B.: From theories to queries: Active learning in practice. In: Active learning and experimental design workshop in conjunction with AISTATS 2010. pp. 1–18. JMLR Workshop and Conference Proceedings (2011)
2011
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems. vol. 25. Curran Associates, Inc. (2012)
2012
Earlier work this paper cites.
Iyer, R.K., Bilmes, J.A.: Submodular optimization with submodular cover and submodular knapsack constraints. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
Bateni, M., Bhaskara, A., Lattanzi, S., Mirrokni, V.S.: Distributed balanced clustering via mapping coresets. In: NIPS. pp. 2591–2599 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Bachem, O., Lucic, M., Krause, A.: Coresets for nonparametric estimation-the case of dp-means. In: ICML. pp. 209–217. PMLR (2015)
2015
Earlier work this paper cites.
Le, Y., Yang, X.: Tiny imagenet visual recognition challenge. CS 231N 7
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. IJCV (2015)
2015
Earlier work this paper cites.
Wei, K., Iyer, R., Bilmes, J.: Submodularity in data subset selection and active learning. In: International Conference on Machine Learning. PMLR (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Cited alongside, same era.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2818–2826 (2016)
2016
Cited alongside, same era.
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
2016
Cited alongside, same era.
2017
Cited alongside, same era.
Mirzasoleiman, B., Bilmes, J., Leskovec, J.: Coresets for data-efficient training of machine learning models. In: ICML. PMLR (2020)
2020
Later among the works it cites.
Mirzasoleiman, B., Cao, K., Leskovec, J.: Coresets for robust training of deep neural networks against noisy labels (2020)
2020
Later among the works it cites.
Sinha, S., Zhang, H., Goyal, A., Bengio, Y., Larochelle, H., Odena, A.: Small-gan: Speeding up gan training using core-sets. In: ICML. PMLR (2020)
2020
Later among the works it cites.
Borsos, Z., Tagliasacchi, M., Krause, A.: Semi-supervised batch active learning via bilevel optimization. In: ICASSP 2021. pp. 3495–3499. IEEE (2021)
2021
Later among the works it cites.
Iyer, R., Khargoankar, N., Bilmes, J., Asanani, H.: Submodular combinatorial information measures with applications in machine learning. In: Algorithmic Learning Theory. pp. 722–754. PMLR (2021)
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2018
Cited alongside, same era.
Munteanu, A., Schwiegelshohn, C., Sohler, C., Woodruff, D.P.: On coresets for logistic regression. In: NeurIPS (2018)
2018
Cited alongside, same era.
Sener, O., Savarese, S.: Active learning for convolutional neural networks: A core-set approach. In: ICLR (2018)
2018
Cited alongside, same era.
Sohler, C., Woodruff, D.P.: Strong coresets for k-median and subspace approximation: Goodbye dimension. In: 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS). pp. 802–813. IEEE (2018)
2018
Cited alongside, same era.
Toneva, M., Sordoni, A., des Combes, R.T., Trischler, A., Bengio, Y., Gordon, G.J.: An empirical study of example forgetting during deep neural network learning. In: ICLR (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Gradient based sample selection for online continual learning. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
Coleman, C., Yeh, C., Mussmann, S., Mirzasoleiman, B., Bailis, P., Liang, P., Leskovec, J., Zaharia, M.: Selection via proxy: Efficient data selection for deep learning. In: ICLR (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Killamsetty, K., Durga, S., Ramakrishnan, G., De, A., Iyer, R.: Grad-match: Gradient matching based data subset selection for efficient deep model training. In: ICML. pp. 5464–5474 (2021)
2021
Later among the works it cites.
Killamsetty, K., Sivasubramanian, D., Ramakrishnan, G., Iyer, R.: Glister: Generalization based data subset selection for efficient and robust learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Liu, E.Z., Haghgoo, B., Chen, A.S., Raghunathan, A., Koh, P.W., Sagawa, S., Liang, P., Finn, C.: Just train twice: Improving group robustness without training group information. In: ICML. pp. 6781–6792 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Zhao, B., Bilen, H.: Dataset condensation with differentiable siamese augmentation. In: International Conference on Machine Learning (2021)
2021
Later among the works it cites.
Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=mSAKhLYLSsl
2021
Later among the works it cites.