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A coreset is a tiny weighted subset of an input set, that closely resembles the loss function, with respect to a certain set of queries.
Über den variabilitätsbereich der koeffizienten von potenzreihen, die gegebene werte nicht annehmen
Carathéodory, C · 1907
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Correlation and the coefficient of determination
Ozer, D. J · 1985
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Early stopping-but when?
Prechelt, L · 1998
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Early stopping-but when?
Prechelt, L · 2002
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A guide to NumPy , volume 1
Oliphant, T. E · 2006
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Detection of non-coding rnas on the basis of predicted secondary structure formation free energy change
Uzilov, A. V., Keegan, J. M., and Mathews, D. H · 2006
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K-means++ the advantages of careful seeding
Arthur, D. and Vassilvitskii, S · 2007
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Bert loses patience: Fast and robust inference with early exit
Zhou, W., Xu, C., Ge, T., McAuley, J., Xu, K., and Wei, F · 2007
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Optimal core-sets for balls
Bădoiu, M. and Clarkson, K. L · 2008
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Python 3 Reference Manual
Van Rossum, G. and Drake, F. L · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
Yeh, I.-C. and Lien, C.-h · 2009
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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Clarkson, K. L · 2010
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Universal ε \varepsilon -approximators for integrals
Langberg, M. and Schulman, L. J · 2010
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LIBSVM: A library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
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A unified framework for approximating and clustering data
Feldman, D. and Langberg, M · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Ecomark: evaluating models of vehicular environmental impact
Guo, C., Ma, Y., Yang, B., Jensen, C. S., and Kaul, M · 2012
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Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools
Tsanas, A. and Xifara, A · 2012
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Distributed k k -means and k k -median clustering on general topologies
Balcan, M.-F. F., Ehrlich, S., and Liang, Y · 2013
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Coresets for k-segmentation of streaming data
Feldman, D., Rossman, G., Volkov, M., and Rus, D · 2014
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Geometric median and robust estimation in banach spaces
On coresets for regularized loss minimization
Curtain, R., Im, S., Moseley, B., Pruhs, K., and Samadian, A · 2019
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Introduction to coresets: Accurate coresets
Jubran, I., Maalouf, A., and Feldman, D · 2019
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Discrepancy, coresets, and sketches in machine learning
Karnin, Z. and Liberty, E · 2019
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Fast and accurate least-mean-squares solvers
Maalouf, A., Jubran, I., and Feldman, D · 2019
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Prediction of motor failure time using an artificial neural network
Sampaio, G. S., de Aguiar Vallim Filho, A. R., da Silva, L. S., and da Silva, L. A · 2019
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Core-sets: Updated survey
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Minsker, S · 2015
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Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach
Lyon, R. J., Stappers, B., Cooper, S., Brooke, J. M., and Knowles, J. D · 2016
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Phillips, J. M · 2016
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Coresets for vector summarization with applications to network graphs
Feldman, D., Ozer, S., and Rus, D · 2017
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Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al · 2018
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Coresets-methods and history: A theoreticians design pattern for approximation and streaming algorithms
Munteanu, A. and Schwiegelshohn, C · 2018
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Feldman, D · 2020
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Jubran, I., Tukan, M., Maalouf, A., and Feldman, D · 2020
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Tight sensitivity bounds for smaller coresets
Maalouf, A., Statman, A., and Feldman, D · 2020
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Coresets for near-convex functions
Tukan, M., Maalouf, A., and Feldman, D · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C., Polat, İ., Feng, Y., Moore, E. W., Vand erPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and Contributors, S. . · 2020
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Efficient coreset constructions via sensitivity sampling
Braverman, V., Feldman, D., Lang, H., Statman, A., and Zhou, S · 2021
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On coresets for support vector machines
Tukan, M., Baykal, C., Feldman, D., and Rus, D · 2021
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Obstacle aware sampling for path planning
Tukan, M., Maalouf, A., Feldman, D., and Poranne, R · 2022
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Pruning neural networks via coresets and convex geometry: Towards no assumptions
Tukan, M., Mualem, L., and Maalouf, A · 2022
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