BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:João Xavier (Instituto Superior Técnico and ISR) DTSTART:20200618T163000Z DTEND:20200618T173000Z DTSTAMP:20260423T003243Z UID:MPML/11 DESCRIPTION:Title: Le arning from distributed datasets: an introduction with two examples\nb y João Xavier (Instituto Superior Técnico and ISR) as part of Mathematic s\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nData are in creasingly measured\, in ever tinier minutiae\, by networks of spatially d istributed agents. Illustrative examples include a team of robots searchin g a large region\, a collection of sensors overseeing a critical infra-str ucture\, or a swarm of drones policing a wide area.\n\nHow to learn from t hese large\, spatially distributed datasets? In the centralized approach e ach agent forwards its dataset to a fusion center\, which then carries out the learning from the pile of amassed datasets. This approach\, however\, prevents the number of agents to scale up: as more and more agents ship d ata to the center\, not only the communication channels near the center qu ickly swell to congestion\, but also the computational power of the center is rapidly outpaced.\n\nIn this seminar\, I describe the alternative appr oach of distributed learning. Here\, no fusion center exists\, and the age nts themselves recreate the centralized computation by exchanging short me ssages (not data) between network neighbors. To illustrate\, I describe tw o learning algorithms: one solves convex learning problems via a token tha t randomly roams through the network\, and the other solves a classificati on problem via random meetings between agents (e.g.\, gossip)\, each agent measuring only its own stream of features.\n\nThis seminar is aimed at no n-specialists. Rather than trying to impart the latest developments of the field\, I hope to open a welcoming door to those wishing to have a peek a t this bubbling field of research\, where optimization\, control\, probabi lity\, and machine learning mingle happily.\n LOCATION:https://researchseminars.org/talk/MPML/11/ END:VEVENT END:VCALENDAR