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The notion of task similarity is at the core of various machine learning paradigms, such as domain adaptation and meta-learning.
Computational optimal transport
Peyré, G. and Cuturi, M · 1935
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A database for handwritten text recognition research
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Differential-Geometrical Methods in Statistics , volume 28 of Lecture Notes in Statistics
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On a formula for the L2 wasserstein metric between measures on euclidean and hilbert spaces
Gelbrich, M · 1990
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Natural gradient works efficiently in learning
Amari, S.-I · 1998
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Methods of Information Geometry
Amari, S.-I. and Nagaoka, H · 2000
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The earth mover’s distance as a metric for image retrieval
Rubner, Y., Tomasi, C., and Guibas, L. J · 2000
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Simultaneous matrix diagonalization: the overcomplete case
De Lathauwer, L · 2003
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Topics in Optimal Transportation
Villani, C · 2003
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Predicting relative performance of classifiers from samples
Leite, R. and Brazdil, P · 2005
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Data Complexity in Machine Learning and Novel Classification Algorithms
Li, L · 2006
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Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F · 2007
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Functions of Matrices: Theory and Computation
Higham, N. J · 2008
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Optimal transport, Old and New , volume 338
Villani, C · 2008
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Domain adaptation: Learning bounds and algorithms
Mansour, Y., Mohri, M., and Rostamizadeh, A · 2009
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MNIST handwritten digit database
LeCun, Y., Cortes, C., and Burges, C. J · 2010
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Domain adaptation in regression
Cortes, C. and Mohri, M · 2011
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Gromov–Wasserstein distances and the metric approach to object matching
Mémoli, F · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Learning generative models with sinkhorn divergences
Genevay, A., Peyre, G., and Cuturi, M · 2018
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Generalizing point embeddings using the wasserstein space of elliptical distributions
Muzellec, B. and Cuturi, M · 2018
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Deep contextualized word representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L · 2018
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Task2Vec: Task embedding for Meta-Learning
Achille, A., Lam, M., Tewari, R., Ravichandran, A., Maji, S., Fowlkes, C., Soatto, S., and Perona, P · 2019
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A wasserstein-type distance in the space of gaussian mixture models
Delon, J. and Desolneux, A · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Zhang, X., Zhao, J., and LeCun, Y · 2015
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Optimal transport for domain adaptation
Courty, N., Flamary, R., Tuia, D., and Rakotomamonjy, A · 2016
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Near-linear time approximation algorithms for optimal transport via sinkhorn iteration
Altschuler, J., Niles-Weed, J., and Rigollet, P · 2017
Cited alongside, same era.
EMNIST: Extending MNIST to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
Cited alongside, same era.
Learning from uncertain curves: The 2-wasserstein metric for gaussian processes
Mallasto, A. and Feragen, A · 2017
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Distances between datasets
Mémoli, F · 2017
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Wasserstein of Wasserstein loss for learning generative models
Dukler, Y., Li, W., Lin, A., and Montufar, G · 2019
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Interpolating between optimal transport and MMD using sinkhorn divergences
Feydy, J., Séjourné, T., Vialard, F.-X., Amari, S.-I., Trouve, A., and Peyré, G · 2019
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Learning embeddings into entropic wasserstein spaces
Frogner, C., Mirzazadeh, F., and Solomon, J · 2019
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Sample complexity of sinkhorn divergences
Genevay, A., Chizat, L., Bach, F., Cuturi, M., and Peyré, G · 2019
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Adaptive Gradient-Based Meta-Learning methods
Khodak, M., Balcan, M.-F. F., and Talwalkar, A. S · 2019
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Wasserstein distributionally robust optimization: Theory and applications in machine learning
Kuhn, D., Esfahani, P. M., Nguyen, V. A., and Shafieezadeh-Abadeh, S · 2019
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Tree-Sliced variants of wasserstein distances
Le, T., Yamada, M., Fukumizu, K., and Cuturi, M · 2019
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Fisher-Rao metric, geometry, and complexity of neural networks
Liang, T., Poggio, T., Rakhlin, A., and Stokes, J · 2019
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Better language models and their implications
Radford, A., Wu, J., Amodei, D., Amodei, D., Clark, J., Brundage, M., and Sutskever, I · 2019
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Transferability and hardness of supervised classification tasks
Tran, A. T., Nguyen, C. V., and Hassner, T · 2019
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Hierarchical optimal transport for document representation
Yurochkin, M., Claici, S., Chien, E., Mirzazadeh, F., and Solomon, J. M · 2019
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