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Poster
Interpretable Distribution Features with Maximum Testing Power
Wittawat Jitkrittum · Zoltán Szabó · Kacper P Chwialkowski · Arthur Gretton

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #194 #None

Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i.e, features). The features are chosen so as to maximize the distinguishability of the distributions, by optimizing a lower bound on test power for a statistical test using these features. The result is a parsimonious and interpretable indication of how and where two distributions differ locally. An empirical estimate of the test power criterion converges with increasing sample size, ensuring the quality of the returned features. In real-world benchmarks on high-dimensional text and image data, linear-time tests using the proposed semimetrics achieve comparable performance to the state-of-the-art quadratic-time maximum mean discrepancy test, while returning human-interpretable features that explain the test results.

Author Information

Wittawat Jitkrittum (Gatsby Unit)

Wittawat Jitkrittum is a postdoctoral researcher at Max Planck Institute for Intelligent Systems, Germany. He earned his PhD from Gatsby Unit, University College London with a thesis on informative features for comparing distributions. He received a best paper award at NeurIPS 2017 and the ELLIS PhD award 2019 for outstanding dissertation. Wittawat has broad research interests covering kernel methods, deep generative models, and approximate Bayesian inference. He served as a publication chair for AISTATS 2016, a program committee for NeurIPS, ICML, AISTATS, among others, and is a co-organizer of the first Southeast Asia Machine Learning School (SEAMLS 2019) in Indonesia and a co-organizer of the first Machine Learning Research School (MLRS 2019) in Thailand.

Zoltán Szabó (École Polytechnique)

[Homepage](http://www.cmap.polytechnique.fr/~zoltan.szabo/)

Kacper P Chwialkowski (Gatsby Unit)
Arthur Gretton (Gatsby Unit, UCL)

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University. Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was program chair for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of the Machine Learning Summer School 2019 in London (with Marc Deisenroth).

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