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Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms.
The influence curve and its role in robust estimation
Hampel, F. R · 1974
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Axioms for lexicographic preferences
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Minimization by random search techniques
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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On the local convergence of pattern search
Dolan, E. D., Lewis, R. M., and Torczon, V · 2003
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Maximum margin coresets for active and noise tolerant learning
Har-Peled, S., Roth, D., and Zimak, D · 2007
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Visualizing the pareto frontier
Lotov, A. V. and Miettinen, K · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
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An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm
Deb, K. and Sinha, A · 2010
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Multiobjective bilevel optimization
Eichfelder, G · 2010
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A unified framework for approximating and clustering data
Feldman, D. and Langberg, M · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Practical bilevel optimization: algorithms and applications , volume 30
Bard, J. F · 2013
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Towards understanding bilevel multi-objective optimization with deterministic lower level decisions
Sinha, A., Malo, P., and Deb, K · 2015
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Coresets for scalable bayesian logistic regression
Huggins, J., Campbell, T., and Broderick, T · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data
Bommert, A., Rahnenführer, J., and Lang, M · 2017
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Training gaussian mixture models at scale via coresets
Lucic, M., Faulkner, M., Krause, A., and Feldman, D · 2017
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A review on bilevel optimization: From classical to evolutionary approaches and applications
Sinha, A., Malo, P., and Deb, K · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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A review of multi-objective optimization: Methods and its applications
Gunantara, N · 2018
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Epsilon-coresets for clustering (with outliers) in doubling metrics
Huang, L., Jiang, S. H.-C., Li, J., and Wu, X · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A · 2018
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Taking human out of learning applications: A survey on automated machine learning
Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.-F., Tu, W.-W., Yang, Q., and Yu, Y · 2018
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Multi-objective bayesian optimisation with preferences over objectives
Abdolshah, M., Shilton, A., Rana, S., Gupta, S., and Venkatesh, S · 2019
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Gradient based sample selection for online continual learning
Aljundi, R., Lin, M., Goujaud, B., and Bengio, Y · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
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Memory efficient experience replay for streaming learning
Hayes, T. L., Cahill, N. D., and Kanan, C · 2019
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Selective-supervised contrastive learning with noisy labels
Li, S., Xia, X., Ge, S., and Liu, T · 2022
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Dataset distillation via factorization
Liu, S., Wang, K., Yang, X., Ye, J., and Wang, X · 2022
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Efficient dataset distillation using random feature approximation
Loo, N., Hasani, R., Amini, A., and Rus, D · 2022
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A survey on multi-objective hyperparameter optimization algorithms for machine learning
Morales-Hernández, A., Van Nieuwenhuyse, I., and Rojas Gonzalez, S · 2022
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Active learning is a strong baseline for data subset selection
Park, D., Papailiopoulos, D., and Lee, K · 2022
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Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
Peng, B. and Risteski, A · 2022
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Toneva, M., Sordoni, A., Combes, R. T. d., Trischler, A., Bengio, Y., and Gordon, G. J · 2019
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Coresets via bilevel optimization for continual learning and streaming
Borsos, Z., Mutny, M., and Krause, A · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Online continual learning from imbalanced data
Chrysakis, A. and Moens, M.-F · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Feldman, V. and Zhang, C · 2020
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Normalized loss functions for deep learning with noisy labels
Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S., and Bailey, J · 2020
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Coresets for robust training of deep neural networks against noisy labels
Mirzasoleiman, B., Cao, K., and Leskovec, J · 2020
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Adaptive second order coresets for data-efficient machine learning
Pooladzandi, O., Davini, D., and Mirzasoleiman, B · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Sorscher, B., Geirhos, R., Shekhar, S., Ganguli, S., and Morcos, A · 2022
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Cafe: Learning to condense dataset by aligning features
Wang, K., Zhao, B., Peng, X., Zhu, Z., Yang, S., Wang, S., Huang, G., Bilen, H., Wang, X., and You, Y · 2022
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Probabilistic bilevel coreset selection
Zhou, X., Pi, R., Zhang, W., Lin, Y., Chen, Z., and Zhang, T · 2022
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Executing your commands via motion diffusion in latent space
Chen, X., Jiang, B., Liu, W., Huang, Z., Fu, B., Chen, T., Yu, J., and Yu, G · 2023
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Min-max multi-objective bilevel optimization with applications in robust machine learning
Gu, A., Lu, S., Ram, P., and Weng, T.-W · 2023
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Large-scale dataset pruning with dynamic uncertainty
He, M., Yang, S., Huang, T., and Zhao, B · 2023
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Near-optimal coresets for robust clustering
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Optimal sample selection through uncertainty estimation and its application in deep learning
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Robust data pruning under label noise via maximizing re-labeling accuracy
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Infobatch: Lossless training speed up by unbiased dynamic data pruning
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Consistency models
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Llama: Open and efficient foundation language models
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A comprehensive survey of continual learning: Theory, method and application
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Moderate coreset: A universal method of data selection for real-world data-efficient deep learning
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Data selection for language models via importance resampling
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Dataset pruning: Reducing training data by examining generalization influence
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Dataset condensation with distribution matching
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Coverage-centric coreset selection for high pruning rates
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