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We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model.
Exploring the limits of transfer learning with a unified text-to-text transformer
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On the number of real roots of a random algebraic equation. ii
Littlewood, J. E. and Offord, A. C · 1939
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Active learning with statistical models
Cohn, D. A., Ghahramani, Z., and Jordan, M. I · 1996
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Analysis of the greedy approach in problems of maximum k-coverage
Hochbaum, D. S. and Pathria, A · 1998
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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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Toward optimal active learning through monte carlo estimation of error reduction
Roy, N. and McCallum, A · 2001
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Support vector machine active learning with applications to text classification
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Incorporating diversity in active learning with support vector machines
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The online median problem
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Analysis of a greedy active learning strategy
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On coresets for k-means and k-median clustering
Har-Peled, S. and Mazumdar, S · 2004
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Batch mode active learning and its application to medical image classification
Hoi, S. C., Jin, R., Zhu, J., and Lyu, M. R · 2006
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k-means++: the advantages of careful seeding
Arthur, D. and Vassilvitskii, S · 2007
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Discriminative batch mode active learning
Guo, Y. and Schuurmans, D · 2007
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A bound on the label complexity of agnostic active learning
Hanneke, S · 2007
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Active learning with gaussian processes for object categorization
Kapoor, A., Grauman, K., Urtasun, R., and Darrell, T · 2007
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Multi-class active learning for image classification
Joshi, A. J., Porikli, F., and Papanikolopoulos, N · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Active learning literature survey
Settles, B · 2009
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Interactive submodular set cover
Guillory, A. and Bilmes, J · 2010
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Multi-class batch-mode active learning for image classification
Joshi, A. J., Porikli, F., and Papanikolopoulos, N · 2010
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A unified framework for approximating and clustering data
Feldman, D. and Langberg, M · 2011
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Adaptive submodularity: Theory and applications in active learning and stochastic optimization
Golovin, D. and Krause, A · 2011
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Gas Sensor Array Drift Dataset
Vergara, A · 2012
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Chemical gas sensor drift compensation using classifier ensembles
Vergara, A., Vembu, S., Ayhan, T., Ryan, M. A., Homer, M. L., and Huerta, R · 2012
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A convex optimization framework for active learning
Elhamifar, E., Sapiro, G., Yang, A., and Sasrty, S. S · 2013
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Understanding the effects of batching in online active learning
Amin, K., Cortes, C., DeSalvo, G., and Rostamizadeh, A · 2020
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Fast and accurate $k$-means++ via rejection sampling
Cohen-Addad, V., Lattanzi, S., Norouzi-Fard, A., Sohler, C., and Svensson, O · 2020
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Data-independent neural pruning via coresets
Mussay, B., Osadchy, M., Braverman, V., Zhou, S., and Feldman, D · 2020
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On coresets for support vector machines
Tukan, M., Baykal, C., Feldman, D., and Rus, D · 2020
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Coresets for near-convex functions
Tukan, M., Maalouf, A., and Feldman, D · 2020
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Query complexity of least absolute deviation regression via robust uniform convergence
Chen, X. and Derezinski, M · 2021
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Efficient active learning of halfspaces: an aggressive approach
Gonen, A., Sabato, S., and Shalev-Shwartz, S · 2013
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Findings of the 2014 workshop on statistical machine translation
Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint-Amand, H., Soricut, R., Specia, L., and Tamchyna, A. s · 2014
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Adaptive batch mode active learning
Chakraborty, S., Balasubramanian, V., and Panchanathan, S · 2014
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On the calibration of sensor arrays for pattern recognition using the minimal number of experiments
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Querying discriminative and representative samples for batch mode active learning
Wang, Z. and Ye, J · 2015
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Batch active learning at scale
Citovsky, G., DeSalvo, G., Gentile, C., Karydas, L., Rajagopalan, A., Rostamizadeh, A., and Kumar, S · 2021
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Parallel and efficient hierarchical k-median clustering
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Adaptivity in adaptive submodularity
Esfandiari, H., Karbasi, A., and Mirrokni, V. S · 2021
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Fast and accurate least-mean-squares solvers for high dimensional data
Maalouf, A., Jubran, I., and Feldman, D · 2021
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Ni, J., Ábrego, G. H., Constant, N., Ma, J., Hall, K. B., Cer, D., and Yang, Y · 2021
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L1 regression with lewis weights subsampling
Parulekar, A., Parulekar, A., and Price, E · 2021
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A survey of deep active learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., and Wang, X · 2021
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Active learning via transductive experimental design
Yu, K., Bi, J., and Tresp, V · 2021
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Improved coresets for euclidean k-means
Cohen-Addad, V., Larsen, K. G., Saulpic, D., Schwiegelshohn, C., and Sheikh-Omar, O. A · 2022
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Active linear regression for ℓ p \ell_{p} norms and beyond
Musco, C., Musco, C., Woodruff, D. P., and Yasuda, T · 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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On optimal coreset construction for euclidean ( k , z ) (k,z) -clustering, 2023
Huang, L., Li, J., and Wu, X · 2023
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Provable data subset selection for efficient neural networks training
Tukan, M., Zhou, S., Maalouf, A., Rus, D., Braverman, V., and Feldman, D · 2023
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New subset selection algorithms for low rank approximation: Offline and online
Woodruff, D. P. and Yasuda, T · 2023
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