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Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning.
Toward understanding catastrophic forgetting in continual learning
Nguyen, C. V.; Achille, A.; Lam, M.; Hassner, T.; Mahadevan, V.; and Soatto, S. 2019 · 1908
Earlier work this paper cites.
Polar Factorization and Monotone Rearrangement of Vector-Valued Functions
Brenier, Y. 1991 · 1991
Earlier work this paper cites.
A database for handwritten text recognition research
Hull, J. 1994 · 1994
Earlier work this paper cites.
Parametric Classification
Devroye, L.; Györfi, L.; and Lugosi, G. 1996 · 1996
Earlier work this paper cites.
Testing that distributions are close
Batu, T.; Fortnow, L.; Rubinfeld, R.; Smith, W. D.; and White, P. 2000 · 2000
Earlier work this paper cites.
The earth mover’s distance as a metric for image retrieval
Rubner, Y.; Tomasi, C.; and Guibas, L. J. 2000 · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, J. B.; Silva, V. d.; and Langford, J. C. 2000 · 2000
Earlier work this paper cites.
Detecting change in data streams
Kifer, D.; Ben-David, S.; and Gehrke, J. 2004 · 2004
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Y.; and Cortes, C. 2005 · 2005
Earlier work this paper cites.
Predicting relative performance of classifiers from samples
Leite, R.; and Brazdil, P. 2005 · 2005
Earlier work this paper cites.
Analysis of representations for domain adaptation
Ben-David, S.; Blitzer, J.; Crammer, K.; and Pereira, F. 2006 · 2006
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, A.; Borgwardt, K.; Rasch, M.; Schölkopf, B.; and Smola, A. 2006 · 2006
Earlier work this paper cites.
Wasserstein embedding for graph learning
Kolouri, S.; Naderializadeh, N.; Rohde, G. K.; and Hoffmann, H. 2020 · 2006
Earlier work this paper cites.
Task-similarity aware meta-learning through nonparametric kernel regression
Venkitaraman, A.; Hansson, A.; and Wahlberg, B. 2020 · 2006
Earlier work this paper cites.
Multidimensional scaling
Cox, M. A.; and Cox, T. F. 2008 · 2008
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. 2009 · 2009
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Mansour, Y.; Mohri, M.; and Rostamizadeh, A. 2009 · 2009
Earlier work this paper cites.
Fast and robust earth mover’s distances
Pele, O.; and Werman, M. 2009 · 2009
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Villani, C. 2009 · 2009
Earlier work this paper cites.
Domain adaptation via transfer component analysis
Pan, S. J.; Tsang, I. W.; Kwok, J. T.; and Yang, Q. 2010 · 2010
Earlier work this paper cites.
An Optimal Transportation Approach for Nuclear Structure-Based Pathology
Wang, W.; Ozolek, J. A.; Slepčev, D.; Lee, A. B.; Chen, C.; and Rohde, G. K. 2011 · 2011
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M. 2013 · 2013
Cited alongside, same era.
A linear optimal transportation framework for quantifying and visualizing variations in sets of images
Wang, W.; Slepčev, D.; Basu, S.; Ozolek, J. A.; and Rohde, G. K. 2013 · 2013
Cited alongside, same era.
Domain adaptation with regularized optimal transport
Courty, N.; Flamary, R.; and Tuia, D. 2014 · 2014
Cited alongside, same era.
Earth mover’s distances on discrete surfaces
Solomon, J.; Rustamov, R.; Guibas, L.; and Butscher, A. 2014 · 2014
Cited alongside, same era.
Tiny ImageNet Visual Recognition Challenge
Le, Y.; and Yang, X. S. 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Task2vec: Task embedding for meta-learning
Achille, A.; Lam, M.; Tewari, R.; Ravichandran, A.; Maji, S.; Fowlkes, C. C.; Soatto, S.; and Perona, P. 2019 · 2019
Later among the works it cites.
An information-theoretic approach to transferability in task transfer learning
Bao, Y.; Li, Y.; Huang, S.-L.; Zhang, L.; Zheng, L.; Zamir, A.; and Guibas, L. 2019 · 2019
Later among the works it cites.
On the Bures–Wasserstein distance between positive definite matrices
Bhatia, R.; Jain, T.; and Lim, Y. 2019 · 2019
Later among the works it cites.
Representation similarity analysis for efficient task taxonomy & transfer learning
Dwivedi, K.; and Roig, G. 2019 · 2019
Later among the works it cites.
Adaptive gradient-based meta-learning methods
Khodak, M.; Balcan, M.-F. F.; and Talwalkar, A. S. 2019 · 2019
Later among the works it cites.
Wasserstein gan with quadratic transport cost
Liu, H.; Gu, X.; and Samaras, D. 2019 · 2019
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A continuous linear optimal transport approach for pattern analysis in image datasets
Kolouri, S.; Tosun, A. B.; Ozolek, J. A.; and Rohde, G. K. 2016 · 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 · 2017
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Wasserstein generative adversarial networks
Arjovsky, M.; Chintala, S.; and Bottou, L. 2017 · 2017
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EMNIST: Extending MNIST to handwritten letters
Cohen, G.; Afshar, S.; Tapson, J.; and Van Schaik, A. 2017 · 2017
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Learning wasserstein embeddings
Courty, N.; Flamary, R.; and Ducoffe, M. 2017 · 2017
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Joint distribution optimal transportation for domain adaptation
Courty, N.; Flamary, R.; Habrard, A.; and Rakotomamonjy, A. 2017 · 2017
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Transferability and hardness of supervised classification tasks
Tran, A. T.; Nguyen, C. V.; and Hassner, T. 2019 · 2019
Later among the works it cites.
Gromov-wasserstein learning for graph matching and node embedding
Xu, H.; Luo, D.; Zha, H.; and Duke, L. C. 2019 · 2019
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Hierarchical optimal transport for document representation
Yurochkin, M.; Claici, S.; Chien, E.; Mirzazadeh, F.; and Solomon, J. M. 2019 · 2019
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Geometric dataset distances via optimal transport
Alvarez-Melis, D.; and Fusi, N. 2020 · 2020
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P2L: Predicting transfer learning for images and semantic relations
Bhattacharjee, B.; Kender, J. R.; Hill, M.; Dube, P.; Huo, S.; Glass, M. R.; Belgodere, B.; Pankanti, S.; Codella, N.; and Watson, P. 2020 · 2020
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Leep: A new measure to evaluate transferability of learned representations
Nguyen, C.; Hassner, T.; Seeger, M.; and Archambeau, C. 2020 · 2020
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Domain2vec: Domain embedding for unsupervised domain adaptation
Peng, X.; Li, Y.; and Saenko, K. 2020 · 2020
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Efficiently identifying task groupings for multi-task learning
Fifty, C.; Amid, E.; Zhao, Z.; Yu, T.; Anil, R.; and Finn, C. 2021 · 2021
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POT: Python Optimal Transport
Flamary, R.; Courty, N.; Gramfort, A.; Alaya, M. Z.; Boisbunon, A.; Chambon, S.; Chapel, L.; Corenflos, A.; Fatras, K.; Fournier, N.; Gautheron, L.; Gayraud, N. T.; Janati, H.; Rakotomamonjy, A.; Redko, I.; Rolet, A.; Schutz, A.; Seguy, V.; Sutherland, D. J.; Tavenard, R.; Tong, A.; and Vayer, T. 2021 · 2021
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An information-geometric distance on the space of tasks
Gao, Y.; and Chaudhari, P. 2021 · 2021
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Wasserstein Embedding for Graph Learning
Kolouri, S.; Naderializadeh, N.; Rohde, G. K.; and Hoffmann, H. 2021 · 2021
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Topics in optimal transportation , volume 58
Villani, C. 2021 · 2021
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Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning
Hamm, K.; Henscheid, N.; and Kang, S. 2022 · 2022
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Selecting task with optimal transport self-supervised learning for few-shot classification
Xu, R.; Yang, X.; Liu, B.; Zhang, K.; and Liu, W. 2022 · 2022
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