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Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing
Stephane Canu · Olivier Cappé · Arthur Gretton · Zaid Harchaoui · Alain Rakotomamonjy · Jean-Philippe Vert

Sat Dec 12 07:30 AM -- 06:45 PM (PST) @ Westin: Glacier
Event URL: http://www.harchaoui.eu/zaid/workshops/nips09/index.html »

Data with temporal (or sequential) structure arise in several applications, such as speaker diarization, human action segmentation, network intrusion detection, DNA copy number analysis, and neuron activity modelling, to name a few. A particularly recurrent temporal structure in real applications is the so-called change-point model, where the data may be temporally partitioned into a sequence of segments delimited by change-points, such that a single model holds within each segment whereas different models hold accross segments. Change-point problems may be tackled from two points of view, corresponding to the practical problem at hand: retrospective (or "a posteriori"), aka multiple change-point estimation, where the whole signal is taken at once and the goal is to estimate the change-point locations, and online (or sequential), aka quickest detection, where data are observed sequentially and the goal is to quickly detect change-points. The purpose of this workshop is to bring together experts from the statistics, machine learning, signal processing communities, to address a broad range of applications from robotics to neuroscience, to discuss and cross-fertilize ideas, and to define the current challenges in temporal segmentation.

Author Information

Stephane Canu (INSA Rouen, LITIS)
Olivier Cappé (CNRS)
Arthur Gretton (Google Deepmind / 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).

Zaid Harchaoui (University of Washington)
Alain Rakotomamonjy (Université de Rouen Normandie Criteo AI Lab)
Jean-Philippe Vert (Owkin / PSL University)

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