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<div class="menu-category">Nicolas Goix</div>
<div class="menu-item"><a href="index.html">Home</a></div>
<div class="menu-item"><a href="biography.html">Biography</a></div>
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<div class="menu-item"><a href="papers.html" class="current">Papers</a></div>
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<div class="menu-item"><a href="nyu.html">Black hole Cyg-X-1</a></div>
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<h1>Nicolas Goix – Papers</h1>
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<h2>Papers</h2>
<ul>
<li><p> One Class Splitting Criteria for Random Forests <a href="https://arxiv.org/abs/1611.01971" target="_blank">[arXiv]</a> <br> N. Goix, R. Brault, N. Drougard, M. Chiapino (ACML 2017) </p></li>
<li><p> How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms? <a href="https://arxiv.org/abs/1607.01152" target="_blank">[arXiv]</a> <br> N. Goix (ICML 2016, Workshop on Anomaly Detection, co-winner of Google Best Paper Award) </p></li>
<li><p> Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking. <a href="http://jmlr.org/proceedings/papers/v51/goix16.pdf" target="_blank">[JMLR]</a> <br> N. Goix, A. Sabourin, S. Clémençon (AISTAT 2016) </p></li>
<li><p> Sparse Representation of Multivariate Extremes. <a href="http://www.cs.cmu.edu/~andrewgw/accepted.html" target="_blank">[PDF]</a> <br> N. Goix, A. Sabourin, S. Clémençon (NIPS Workshop on Nonparametric Methods for Large Scale Representation Learning, 2015) </p></li>
<li><p> Sparsity in Multivariate Extremes with Application to Anomaly Detection. <a href="https://hal.archives-ouvertes.fr/hal-01179142" target="_blank">[ArXiv]</a> <br> N. Goix, A. Sabourin, S. Clémençon (Journal of Multivariate Analysis 2017)</p></li>
<li><p> Learning the Dependence Structure of Rare Events: a Non-Asymptotic Study. <a href="http://jmlr.csail.mit.edu/proceedings/papers/v40/Goix15" target="_blank">[JMLR]</a> <br> N. Goix, A. Sabourin, S. Clémençon (COLT 2015, selected for a long talk) </p></li>
<li><p> On Anomaly Ranking and Excess-Mass Curves. <a href="http://jmlr.org/proceedings/papers/v38/goix15" target="_blank">[JMLR]</a> <br> N. Goix, A. Sabourin, S. Clémençon (AISTAT 2015) </p></li>
</ul>
<h2>PhD Thesis</h2>
<ul>
<li><p> Machine Learning and Extremes for Anomaly Detection. <a href="thesis.pdf" target="_blank">[online]</a> <a href="thesis_book.pdf" target="_blank">[book]</a> <br> N. Goix (Dec. 2016) </p></li>
</ul>
<h2>Talks and Resources</h2>
<ul>
<li><p> UMPC LSTA GT Extremes, Machine Learning and Extremes for Anomaly Detection <a href="slides_jussieu.pdf" target="_blank"> [Slides] </a> </p></li>
<li><p> ICML 2016, <a href="https://sites.google.com/site/icmlworkshoponanomalydetection/" target="_blank"> Workshop on Anomaly Detection </a>, New York City June 2016, how to evaluate anomaly detection algorithms? <a href="slides_icml2016.pdf" target="_blank">[Slides]</a> </p></li>
<li><p> AISTATS 2016, <a href="http://www.aistats.org/aistats2016/poster_sessions.html" target="_blank"> Cadiz May 2016 </a>, Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking </p></li>
<li><p> French Ministry of Industry, <a href="http://bourseauxtechnos-bercy.strikingly.com/" target="_blank">Bourse aux Technologies--Industrie du Futur--Smart Manufacturing</a> , Paris Mars 2016, Damex: Detecting Anomalies in High Dimension.</p></li>
<li><p> Telecom Paristech, TSI department seminar, Paris Jan. 2016, Anomaly Detection in Scikit-Learn and new tools from Multivariate Extreme Value Theory. <a href="talk_telecom.pdf" target="_blank">[Slides]</a> </p></li>
<li><p> NIPS 2015, Workshop on Nonparametric Methods for Large Scale Representation Learning, Montréal Dec. 2015, Sparse Representation of Multivariate Extremes <a href="poster_nips.pdf" target="_blank">[Poster]</a> </p></li>
<li><p> Séminaire LJK-Statistique, Grenoble Nov. 2015, Learning a Sparse Representation of Rare Events with Application to Anomaly Ranking <a href="grenoble.pdf" target="_blank">[Slides]</a> </p></li>
<li><p> Paris-Saclay Center for Data Science, Orsay Oct. 2015, Anomaly Detection algorithms in Scikit-Learn <a href="https://webcast.in2p3.fr/videos-anomaly_detection_algorithms_in_scikitlearn" target="_blank">[Video talk with slides]</a> <a href="nicolas_goix_osi_presentation.pdf" target="_blank">[Slides]</a> </p></li>
<li><p> ML for Big Data Chair - GT predictive maintenance, Paris Oct. 2015, Anomaly Detection with Multivariate Extremes <a href="GT2015.pdf" target="_blank">[Slides]</a> </p></li>
<li><p> COLT 2015, Paris July 2015, Learning the Dependence Structure of Rare Events <a href="http://videolectures.net/colt2015_goix_rare_events" target="_blank">[Video talk with slides]</a> <a href="slide_colt2015.pdf" target="_blank">[Slides]</a><a href="poster_colt2015.pdf" target="_blank">[Poster]</a> </p></li>
<li><p> AISTAT 2015, San Diego May 2015, Anomaly Ranking and Excess-Mass Curves <a href="poster_aistat2015.pdf" target="_blank">[Poster]</a></p></li>
<li><p> Séminaire de Statistique AgroParisTech, Paris May 2015, Scoring Anomalies among Multivariate Extreme Observations <a href="AgroParisTech2015.pdf" target="_blank">[Slides]</a></p></li>
<li><p> <a href="https://sites.google.com/site/smileinparis/contacts" target="_blank">SMILE</a>, 'NIPS defriefing', Paris January 15, Approximating Hierarchical MV-sets for Hierarchical Clustering (Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch)
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