BEGIN:VCALENDAR VERSION:2.0 PRODID:-//IFDS - ECPv6.0.1.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:IFDS X-ORIGINAL-URL:https://ifds.info X-WR-CALDESC:Events for IFDS REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20240310T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20241103T070000 END:STANDARD TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20240310T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20241103T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Chicago:20240308T133000 DTEND;TZID=America/Chicago:20240308T143000 DTSTAMP:20260409T133929 CREATED:20240318T213102Z LAST-MODIFIED:20240318T213102Z UID:2890-1709904600-1709908200@ifds.info SUMMARY:Low-Rank Structures in Optimal Transport DESCRIPTION:Bio: Meyer Scetbon is currently a Research Scientist at Microsoft Research. He completed his PhD at Institut Polytechnique de Paris\, advised by M. Cuturi. He did\, as a visiting student\, his MS theses UW and Technion\, on kernel-based viewpoints on deep neural networks advised by Z. Harchaoui\, and end-to-end signal and image denoising advised by M. Elad\, respectively. \n\n\n\n\n\nAbstract: Optimal transport (OT) plays an increasingly important role in machine learning (ML) to compare probability distributions. Yet\, it poses\, in its original form\, several challenges when used for applied problems: (i) computing OT between discrete distributions amounts to solving a large and expensive network flow problem which requires a supercubic complexity in the number of points; (ii) estimating OT using sampled measures is doomed by the curse of dimensionality. These issues can be mitigated using an entropic regularization\, solved with the Sinkhorn algorithm\, which improves on both statistical and computational aspects. While much faster\, entropic OT still requires a quadratic complexity with respect to the number of points and therefore remains prohibitive for large-scale problems. In this talk\, I will present new regularization approaches for the OT problem\, as well as its quadratic extension\, the Gromov-Wasserstein (GW) problem\, which impose low-rank structures on the admissible couplings. This results in the development of new algorithms that enjoy a linear complexity both in time and memory with respect to the number of points\, enabling their applications in the large-scale setting where millions of points need to be compared. Additionally\, I will show that these new regularization schemes have better statistical performances compared to the entropic approach\, that they naturally interpolate between the Maximum Mean Discrepancy (MMD) and OT\, and that they offer general clustering methods for arbitrary geometry.Website:  URL:https://ifds.info/event/low-rank-structures-in-optimal-transport/ LOCATION:CSE (Allen) 403 CATEGORIES:MLOpt@UWash END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20240315T133000 DTEND;TZID=America/Los_Angeles:20240315T143000 DTSTAMP:20260409T133929 CREATED:20240318T212943Z LAST-MODIFIED:20240318T212943Z UID:2888-1710509400-1710513000@ifds.info SUMMARY:Optimized Decision Making via Active Learning of Stochastic Hamiltonians DESCRIPTION:Speaker: Prof. Chandrajit Bajaj \, UT Austin \nAbstract: A Hamiltonian represents the energy of a dynamical system in phase space with coordinates of position and momentum. The Hamilton’s equations of motion are obtainable as coupled symplectic differential equations.  In this talk I shall show how optimized decision making (action sequences) can be obtained via a reinforcement learning problem wherein the agent interacts with the unknown environment to simultaneously learn a Hamiltonian surrogate and the optimal action sequences using Hamilton dynamics\, by invoking the Pontryagin Maximum Principle. We use optimal control theory to define an optimal control gradient flow\, which guides the reinforcement learning process of the agent to progressively optimize the Hamiltonian while simultaneously converging to the optimal action sequence. Extensions to stochastic Hamiltonians leading to stochastic action sequences and the free-energy principle shall also be discussed. This is joint work with  Taemin Heo\, Minh Nguyen. URL:https://ifds.info/event/optimized-decision-making-via-active-learning-of-stochastic-hamiltonians/ LOCATION:CSE (Allen) 403 CATEGORIES:MLOpt@UWash END:VEVENT END:VCALENDAR