BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Inderjit Dhillon (University of Texas at Austin) DTSTART:20200827T190000Z DTEND:20200827T203000Z DTSTAMP:20260423T021057Z UID:IASML/23 DESCRIPTION:Title: M ulti-Output Prediction: Theory and Practice\nby Inderjit Dhillon (Univ ersity of Texas at Austin) as part of IAS Seminar Series on Theoretical Ma chine Learning\n\n\nAbstract\nMany challenging problems in modern applicat ions amount to finding relevant results from an enormous output space of p otential candidates\, for example\, finding the best matching product from a large catalog or suggesting related search phrases on a search engine. The size of the output space for these problems can be in the millions to billions. Moreover\, observational or training data is often limited for m any of the so-called “long-tail” of items in the output space. Given t he inherent paucity of training data for most of the items in the output s pace\, developing machine learned models that perform well for spaces of t his size is challenging. Fortunately\, items in the output space are often correlated thereby presenting an opportunity to alleviate the data sparsi ty issue. In this talk\, I will first discuss the challenges in modern mul ti-output prediction\, including missing values\, features associated with outputs\, absence of explicit negative examples\, and the need to scale u p to enormous data sets. Bilinear methods\, such as Inductive Matrix Compl etion (IMC)\, enable us to handle missing values and output features in pr actice\, while coming with theoretical guarantees. Nonlinear methods such as nonlinear IMC and DSSM (Deep Semantic Similarity Model) enable more pow erful models that are used in practice in real-life applications. However\ , inference in these models scales linearly with the size of the output sp ace. In order to scale up\, I will present the Prediction for Enormous and Correlated Output Spaces (PECOS) framework\, that performs prediction in three phases: (i) in the first phase\, the output space is organized using a semantic indexing scheme\, (ii) in the second phase\, the indexing is u sed to narrow down the output space by orders of magnitude using a machine learned matching scheme\, and (iii) in the third phase\, the matched item s are ranked by a final ranking scheme. The versatility and modularity of PECOS allows for easy plug-and-play of various choices for the indexing\, matching\, and ranking phases\, and it is possible to ensemble various mod els\, each arising from a particular choice for the three phases.\n LOCATION:https://researchseminars.org/talk/IASML/23/ END:VEVENT END:VCALENDAR