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Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level.
Nonparametric independence testing for small sample sizes
Aaditya Ramdas and Leila Wehbe · 1922
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Radial basis functions, multi-variable functional interpolation and adaptive networks
David S Broomhead and David Lowe · 1988
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Spline models for observational data , volume 59
Grace Wahba · 1990
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Solving the multiple instance problem with axis-parallel rectangles
Thomas G. Dietterich, Richard H. Lathrop, and Tomás Lozano-Pérez · 1997
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A Solution to the Ecological Inference Problem
Gary King · 1997
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Radial basis functions: a bayesian treatment
David Barber and Bernhard Schottky · 1998
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Ridge regression learning algorithm in dual variables
Craig Saunders, Alexander Gammerman, and Volodya Vovk · 1998
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Robust full bayesian learning for radial basis networks
Christophe Andrieu, Nando De Freitas, and Arnaud Doucet · 2001
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Models, assumptions and model checking in ecological regressions
Andrew Gelman, David K Park, Stephen Ansolabehere, Phillip N. Price, and Lorraine C. Minnite · 2001
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Stochastic processes with sample paths in reproducing kernel hilbert spaces
Milan Lukić and Jay Beder · 2001
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A generalized representer theorem
Bernhard Schölkopf, Ralf Herbrich, and Alex J. Smola · 2001
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A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications
Pedro J Moreno, Purdy P Ho, and Nuno Vasconcelos · 2003
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Approximate methods for propagation of uncertainty with Gaussian process models
Agathe Girard · 2004
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Probability product kernels
Tony Jebara, Risi Imre Kondor, and Andrew Howard · 2004
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Learning about individuals from group statistics
Hendrik Kück and Nando de Freitas · 2005
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Pattern recognition and machine learning
Christopher M. Bishop · 2006
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Improving ecological inference using individual-level data
Christopher Jackson, Nicky Best, and Sylvia Richardson · 2006
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Characterizing the function space for bayesian kernel models
Natesh S Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, and Robert L Wolpert · 2007
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Kernels for Structured Data , volume 72
Thomas Gärtner · 2008
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Estimating labels from label proportions
Novi Quadrianto, Alex J Smola, Tiberio S Caetano, and Quoc V Le · 2009
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Multi-instance learning by treating instances as non-iid samples
Zhi-Hua Zhou, Yu-Yin Sun, and Yu-Feng Li · 2009
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Hilbert space embeddings and metrics on probability measures
Bharath K Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, and Gert RG Lanckriet · 2010
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Nonparametric divergence estimation with applications to machine learning on distributions
Barnabás Póczos, Liang Xiong, and Jeff Schneider · 2011
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From word embeddings to document distances
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger · 2015
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Towards a learning theory of cause-effect inference
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, and Ilya Tolstikhin · 2015
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Active pointillistic pattern search
Yifei Ma, Danica J. Sutherland, Roman Garnett, and Jeff Schneider · 2015
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A machine learning approach for dynamical mass measurements of galaxy clusters
Michelle Ntampaka, Hy Trac, Danica J. Sutherland, Nicholas Battaglia, Barnabás Póczos, and Jeff Schneider · 2015
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Bayesian nonparametric kernel-learning
Junier B Oliva, Avinava Dubey, Barnabás Póczos, Jeff Schneider, and Eric P Xing · 2015
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Learning from distributions via support measure machines
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, and Bernhard Schölkopf · 2012
Cited alongside, same era.
Ellipsoidal multiple instance learning
Gabriel Krummenacher, Cheng Soon Ong, and Joachim M Buhmann · 2013
Cited alongside, same era.
Distribution to distribution regression
Junier B Oliva, Barnabás Póczos, and Jeff Schneider · 2013
Cited alongside, same era.
Distribution-free distribution regression
Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, and Larry Wasserman · 2013
Cited alongside, same era.
The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman and Andrew Gelman · 2014
Cited alongside, same era.
Kernel mean estimation and stein effect
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, and Bernhard Schoelkopf · 2014
Cited alongside, same era.
Variational inference for latent variables and uncertain inputs in Gaussian processes
Andreas C. Damianou, Michalis K. Titsias, and Neil D. Lawrence · 2016
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Bayesian learning of kernel embeddings
Seth Flaxman, Dino Sejdinovic, John P. Cunningham, and Sarah Filippi · 2016
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Seth Flaxman, Danica J. Sutherland, Yu-Xiang Wang, and Yee-Whye Teh · 2016
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DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrovic, Dino Sejdinovic, and Yee-Whye Teh · 2016
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Dynamical mass measurements of contaminated galaxy clusters using machine learning
Michelle Ntampaka, Hy Trac, Danica J. Sutherland, Sebastian Fromenteau, Barnabás Póczos, and Jeff Schneider · 2016
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Back to the future: Radial basis function networks revisited
Qichao Que and Mikhail Belkin · 2016
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Deep expectation of real and apparent age from a single image without facial landmarks
Rasmus Rothe, Radu Timofte, and Luc Van Gool · 2016
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Scalable, Flexible, and Active Learning on Distributions
Danica J. Sutherland · 2016
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Testing and learning on distributions with symmetric noise invariance
Ho Chung Leon Law, Christopher Yau, and Dino Sejdinovic · 2017
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Ingo Steinwart · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander Smola · 2017
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Leraning theory for distribution regression
Zoltán Szábo, Bharath K. Sriperumbudur, Barnabás Póczos, and Arthur Gretton · 2066
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