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DOMIQUE GAY

Domique Gay, Amerin lawyer, specializg the field of Swiss and Internatnal Fisl Laws, Tax Ligatn, Real Estate Taxatn. Member of Etu Oberson. * dominique gay oberson *

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Create acuntENFully supportedEnglish (Uned Stat)Partially supportedFrançais (Canada)Français (France)Dtsch (Dtschland)हिंदी (भारत)Italiano (Italia)Português (Brasil)Español (España)Español (Méxi)Use appBackBgraphyIMDbProBgraphyEdMi BDomique Gay is known for Amerin Gangster Prents: Big 50 - The Delrhonda Hood Story (2021).

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Automatic Feature Engeerg for Time Seri Classifitn: Evaluatn and DiscsnAug 02, 2023Aurélien Renlt, Alexis Bondu, Vcent Lemaire, Domique GayTime Seri Classifitn (TSC) has received much attentn the past two s and is still a ccial and challengg problem data science and knowledge engeerg. ViaInterpretable Feature Constctn for Time Seri Extrsic RegrsnMar 15, 2021Domique Gay, Alexis Bondu, Vcent Lemaire, Marc BoulléSupervised learng of time seri data has been extensively studied for the se of a tegoril target variable. ViaPredictive K-means wh lol molsDec 16, 2020Vcent Lemaire, Oumaima Alaoui Ismaili, Antoe Cornuéjols, Domique GaySupervised classifitn n be effective for predictn but sometim weak on terpretabily or explaabily (XAI).

DOMIQUE GAY

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(a posn paper)Mar 08, 2019Domique Gay, Vcent LemaireSce the troductn and the public availabily of the \textsc{ucr} time seri benchmark data sets, numero Time Seri Classifitn (TSC) methods has been signed, evaluated and pared to each others.

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Temporal Data (AALTD 2018) * 8 pag ViaCats & Co: Categoril Time Seri CocltergMay 06, 2015Domique Gay, Roma Guigourès, Marc Boullé, Fabrice ClérotWe suggt a novel method of clterg and exploratory analysis of temporal event sequenc data (also known as tegoril time seri) based on three-dimensnal data grid mols.

ViaCountry-sle Exploratory Analysis of Call Detail Rerds through the Lens of Data Grid MolsMar 20, 2015Roma Guigourès, Domique Gay, Marc Boullé, Fabrice Clérot, Fabrice RossiCall Detail Rerds (CDRs) are data rerd by telemunitns pani, nsistg of basic rmatns related to several dimensns of the lls ma through the work: the source, statn, date and time of lls. DGDomique Isaiah GayMiddletown, OHSearch background report →The birth date was listed as 22-11-1993. Search background report DGDomique Trevon GayCharlotte, NCSearch background report →Domique will celebrate 30th birthday on September 14.

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Two persons, cludg Sandra Moss Gay, Najja Gay, listed the phone number (704) 777-5413 as their own, var documents dited. Possible nnectns via ma addrs - Anthony Troy Gay. Possible nnectns via phone numbers - Sandra Moss Gay, Najja Gay.

Search background report DGDomique Trevon GayPickergton, OHSearch background report →Domique was born on 02.

Their name are Eboni Rae Gay, Lamar A Jackson, and six others.

DOMIQUE GAY

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Search background report DGDomique D GayWdsor, VASearch background report →Domique celebrated 34th birthday on April 9. Alternative name that Domique n e are Domic D Gay, Domic Gay, Domique Gay.

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Search background report DGDomique N GayHoton, TXSearch background report →Domique celebrated 38th birthday on April 23. We know that Carla D Gay, Deotis Gay, and two other persons also lived at this addrs, perhaps wh a different time ame. Var documents lk the phone number (713) 991-0452 to different owners — Carla D Gay, Eric D Gay, Deotis Gay, Saida Ochoa.

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DOMIQUE GAY

Create acuntENFully supportedEnglish (Uned Stat)Partially supportedFrançais (Canada)Français (France)Dtsch (Dtschland)हिंदी (भारत)Italiano (Italia)Português (Brasil)Español (España)Español (Méxi)BackBgraphyIMDbProBgraphyEdMi BDomique Gay is known for Amerin Gangster Prents: Big 50 - The Delrhonda Hood Story (2021).

Domonique Foxworth on Homosexualy the NFL and the “Inevabily” that Someone Com Out. Earlier this week, ’s Michael Freeman reported that a gay NFL football player is “strongly nsirg” makg his sexual orientatn public the near future.

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-10.33344622s1.1875777-6.20179466 2.24320232 0c0 0 5.9378885 1.05562462 0 2.11124925 0 0-1.05562463 6.33374774-2.24320233 0-3.5627331-.6597654-3.29882695-1.31953078 0-2.11124925z"></path><path style="animatn:sparkle 1.5s 0.9s fe ease--out" d="M33.5173993 75.97263826s1.03464615-5.40315215 1.95433162 0c0 0 5.17323078.91968547 0 1.83937095 0 0-.91968547 5.51811283-1.95433162 0-3.10393847-.57480342-2.8740171-1.14960684 0-1.83937095z"></path><path style="animatn:sparkle 1.7s 0.4s fe ease--out" d="M69.03038108 1.71240809s.73779281-3.852918 1.39360864 0c0 0 3.68896404.65581583 0 1.31163166 0 0-.65581583 3.93489497-1.39360864 0-2.21337842-.4098849-2.04942447-.81976979 0-1.31163166z"></path></g></svg></div></div><span class="Search_publitn-date__nSb6j">Aug 02, 2023<br/></span><span class="AuthorLks_thors-ntaer__r_iyO"><span><a data-ttid="paper-rult-thor" href="/thor/Aur%C3%A9lien%20Renlt">Aurélien Renlt</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Alexis%20Bondu">Alexis Bondu</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Vcent%20Lemaire">Vcent Lemaire</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Domique%20Gay">Domique Gay</a></span><br/><br/></span><div class="Search_paper-tail-page-imag-ntaer__Wphz5"></div><p class="Search_paper-ntent__eQ9jv">Time Seri Classifitn (TSC) has received much attentn the past two s and is still a ccial and challengg problem data science and knowledge engeerg. Ined, along wh the creasg availabily of time seri data, many TSC algorhms have been suggted by the rearch muny the lerature. Bis state-of-the-art methods based on siary measur, tervals, shapelets, dictnari, ep learng methods or hybrid ensemble methods, several tools for extractg unsupervised rmative summary statistics, aka featur, om time seri have been signed the recent years. Origally signed for scriptive analysis and visualizatn of time seri wh rmative and terpretable featur, very few of the feature engeerg tools have been benchmarked for TSC problems and pared wh state-of-the-art TSC algorhms terms of predictive performance. In this article, we aim at fillg this gap and propose a simple TSC procs to evaluate the potential predictive performance of the feature sets obtaed wh existg feature engeerg tools. Th, we prent an empiril study of 11 feature engeerg tools branched wh 9 supervised classifiers over 112 time seri data sets. The analysis of the rults of more than 10000 learng experiments dite that feature-based methods perform as accurately as current state-of-the-art TSC algorhms, and th should rightfully be nsired further the TSC lerature.<br/></p><span></span><span></span><div class="Search_search-rult-provir__Qy9xz">Via<img alt="arxiv in" srcSet="/_next/image?;w=64&q=75 1x, /_next/image?;w=128&q=75 2x" src="/_next/image?;w=128&q=75" width="56" height="25" dg="async" data-nimg="1" class="Search_arxiv-in__h96mG" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-lk__iw6M7"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns=" style="marg-right:5px"><tle>Ghub In</tle><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 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data-ttid="paper-rult-tle" href="/paper/arxiv:2103.10247"><strong>Interpretable Feature Constctn for Time Seri Extrsic Regrsn</strong></a></h2><div class="Search_buttons-ntaer__q9Y_m"><a href="#" target="_blank" id="requt--2103.10247" data-ttid="view--button" class="Search_view--lk__Yn7"><button type="button" class="btn Search_view-button__hnDAt Search_buttons-spacg__R2x0Z Search_view--button__lScxO"><svg role="img" height="14" width="24" viewBox="0 0 24 24" xmlns=" fill="#fff"><tle>Ghub In</tle><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12"></path></svg>View Co</button></a><a class="Search_buttons-spacg__R2x0Z Search_add--button__xiBsy" target="_blank" href="/add_?tle=Interpretable Feature Constctn for Time Seri Extrsic Regrsn&><img alt="Add " tle="Contribute your for this paper to the muny" srcSet="/_next/image?;w=48&q=75 1x, /_next/image?;w=96&q=75 2x" src="/_next/image?;w=96&q=75" width="36" height="36" dg="async" data-nimg="1" loadg="lazy" style="lor:transparent"/></a><div class="wrapper Search_buttons-spacg__R2x0Z"><a tle="Get alerts when new is available for this paper"><img alt="Alert button" id="alert_btn" srcSet="/_next/image?;w=640&q=75 1x, /_next/image?;w=1080&q=75 2x" src="/_next/image?;w=1080&q=75" width="512" height="512" dg="async" data-nimg="1" class="alert-btn-image" loadg="lazy" style="lor:transparent"/></a><svg xmlns=" viewBox="0 0 106 34" style="marg-left:9px"><g class="sparkl"><path style="animatn:sparkle 2s 0s fe ease--out" d="M15.5740361 -10.33344622s1.1875777-6.20179466 2.24320232 0c0 0 5.9378885 1.05562462 0 2.11124925 0 0-1.05562463 6.33374774-2.24320233 0-3.5627331-.6597654-3.29882695-1.31953078 0-2.11124925z"></path><path style="animatn:sparkle 1.5s 0.9s fe ease--out" d="M33.5173993 75.97263826s1.03464615-5.40315215 1.95433162 0c0 0 5.17323078.91968547 0 1.83937095 0 0-.91968547 5.51811283-1.95433162 0-3.10393847-.57480342-2.8740171-1.14960684 0-1.83937095z"></path><path style="animatn:sparkle 1.7s 0.4s fe ease--out" d="M69.03038108 1.71240809s.73779281-3.852918 1.39360864 0c0 0 3.68896404.65581583 0 1.31163166 0 0-.65581583 3.93489497-1.39360864 0-2.21337842-.4098849-2.04942447-.81976979 0-1.31163166z"></path></g></svg></div></div><span class="Search_publitn-date__nSb6j">Mar 15, 2021<br/></span><span class="AuthorLks_thors-ntaer__r_iyO"><span><a data-ttid="paper-rult-thor" href="/thor/Domique%20Gay">Domique Gay</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Alexis%20Bondu">Alexis Bondu</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Vcent%20Lemaire">Vcent Lemaire</a>, </span><span><a data-ttid="paper-rult-thor" href="/thor/Marc%20Boull%C3%A9">Marc Boullé</a></span><br/><br/></span><div class="Search_paper-tail-page-imag-ntaer__Wphz5"><div class="Search_paper-imag__M3iM_"><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 1 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 2 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 3 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 4 for Interpretable Feature Constctn for Time Seri Extrsic Regrsn" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div></div></div><p class="Search_paper-ntent__eQ9jv">Supervised learng of time seri data has been extensively studied for the se of a tegoril target variable. In some applitn domas, e.g., energy, environment and health monorg, occurs that the target variable is numeril and the problem is known as time seri extrsic regrsn (TSER). In the lerature, some well-known time seri classifiers have been extend for TSER problems. As first benchmarkg studi have foced on predictive performance, very ltle attentn has been given to terpretabily. To fill this gap, this paper, we suggt an extensn of a Bayian method for robt and terpretable feature nstctn and selectn the ntext of TSER. Our approach explos a relatnal way to tackle wh TSER: (i), we build var and simple reprentatns of the time seri which are stored a relatnal data scheme, then, (ii), a proposnalisatn technique (based on classil aggregatn / selectn functns om the relatnal data field) is applied to build terpretable featur om sendary tabl to "flatten" the data; and (iii), the nstcted featur are filtered out through a Bayian Maximum A Posterri approach. The rultg transformed data n be procsed wh var existg regrsors. Experimental validatn on var benchmark data sets monstrat the benefs of the suggted approach.<br/></p><span></span><span></span><div class="Search_search-rult-provir__Qy9xz">Via<img alt="arxiv in" srcSet="/_next/image?;w=64&q=75 1x, /_next/image?;w=128&q=75 2x" src="/_next/image?;w=128&q=75" width="56" height="25" dg="async" data-nimg="1" class="Search_arxiv-in__h96mG" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-lk__iw6M7"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns=" style="marg-right:5px"><tle>Ghub In</tle><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 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class="Search_buttons-spacg__R2x0Z Search_add--button__xiBsy" target="_blank" href="/add_?tle=Predictive K-means wh lol mols&><img alt="Add " tle="Contribute your for this paper to the muny" srcSet="/_next/image?;w=48&q=75 1x, /_next/image?;w=96&q=75 2x" src="/_next/image?;w=96&q=75" width="36" height="36" dg="async" data-nimg="1" loadg="lazy" style="lor:transparent"/></a><div class="wrapper Search_buttons-spacg__R2x0Z"><a tle="Get alerts when new is available for this paper"><img alt="Alert button" id="alert_btn" srcSet="/_next/image?;w=640&q=75 1x, /_next/image?;w=1080&q=75 2x" src="/_next/image?;w=1080&q=75" width="512" height="512" dg="async" data-nimg="1" class="alert-btn-image" loadg="lazy" style="lor:transparent"/></a><svg xmlns=" viewBox="0 0 106 34" style="marg-left:9px"><g class="sparkl"><path style="animatn:sparkle 2s 0s fe ease--out" d="M15.5740361 -10.33344622s1.1875777-6.20179466 2.24320232 0c0 0 5.9378885 1.05562462 0 2.11124925 0 0-1.05562463 6.33374774-2.24320233 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</span><span><a data-ttid="paper-rult-thor" href="/thor/Domique%20Gay">Domique Gay</a></span><br/><br/></span><div class="Search_paper-tail-page-imag-ntaer__Wphz5"><div class="Search_paper-imag__M3iM_"><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 1 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 2 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 3 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-image__el16d" data-ttid="paper-rult-image"><img alt="Figure 4 for Predictive K-means wh lol mols" dg="async" srcSet="/_next/image?;w=384&q=75 1x, /_next/image?;w=640&q=75 2x" src="/_next/image?;w=640&q=75" width="298" height="192" data-nimg="1" loadg="lazy" style="lor:transparent"/></div></div></div><p class="Search_paper-ntent__eQ9jv">Supervised classifitn n be effective for predictn but sometim weak on terpretabily or explaabily (XAI). Clterg, on the other hand, tends to isolate tegori or profil that n be meangful but there is no guarantee that they are eful for labels predictn. Predictive clterg seeks to obta the bt of the two worlds. Startg om labeled data, looks for clters that are as pure as possible wh regards to the class labels. One technique nsists tweakg a clterg algorhm so that data pots sharg the same label tend to aggregate together. Wh distance-based algorhms, such as k-means, a solutn is to modify the distance ed by the algorhm so that rporat rmatn about the labels of the data pots. In this paper, we propose another method which reli on a change of reprentatn guid by class nsi and then rri out clterg this new reprentatn space. We prent two new algorhms g this technique and show on a variety of data sets that they are petive for predictn performance wh pure supervised classifiers while offerg terpretabily of the clters disvered.<br/></p><span></span><span></span><div class="Search_search-rult-provir__Qy9xz">Via<img alt="arxiv in" srcSet="/_next/image?;w=64&q=75 1x, /_next/image?;w=128&q=75 2x" src="/_next/image?;w=128&q=75" width="56" height="25" dg="async" data-nimg="1" class="Search_arxiv-in__h96mG" loadg="lazy" style="lor:transparent"/></div><div class="Search_paper-lk__iw6M7"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns=" style="marg-right:5px"><tle>Ghub In .

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