BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Yian Ma (UC San Diego) DTSTART:20200428T230000Z DTEND:20200429T001500Z DTSTAMP:20260422T212707Z UID:MADDD/1 DESCRIPTION:Title: Br iding MCMC and Optimization\nby Yian Ma (UC San Diego) as part of Math ematics of Data and Decisions @ Davis\n\nLecture held in ZOOM.\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/MADDD/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Roummel Marcia (UC Merced) DTSTART:20200505T230000Z DTEND:20200506T001500Z DTSTAMP:20260422T212707Z UID:MADDD/2 DESCRIPTION:Title: Op timization methods for machine learning\nby Roummel Marcia (UC Merced) as part of Mathematics of Data and Decisions @ Davis\n\nLecture held in Z OOM.\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/MADDD/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Venkat Chandrasekaran (Caltech) DTSTART:20200512T230000Z DTEND:20200513T001500Z DTSTAMP:20260422T212707Z UID:MADDD/3 DESCRIPTION:Title: Fi tting convex sets to data\nby Venkat Chandrasekaran (Caltech) as part of Mathematics of Data and Decisions @ Davis\n\n\nAbstract\nA number of pr oblems in signal processing may be viewed conceptually as fitting a convex set to data. In vision and learning\, the task of identifying a collecti on of features or atoms that provide a concise description of a dataset ha s been widely studied under the title of dictionary learning or sparse cod ing. In convex-geometric terms\, this problem entails learning a polytope with a desired facial structure from data. In computed tomography\, reco nstructing a shape from support measurements arises commonly in MRI\, robo tics\, and target reconstruction from radar data. This problem is usually reformulated as one of estimating a polytope from a collection of noisy h alfspaces.\n\nIn this talk we describe new approaches to these problems th at leverage contemporary ideas from the optimization literature on lift-an d-project descriptions of convex sets. This perspective leads to natural semidefinite programming generalizations of previous techniques for fittin g polyhedral convex sets to data. We provide several stylized illustratio ns in which these generalizations provide improved reconstructions. On th e algorithmic front our methods rely prominently on operator scaling\, whi le on the statistical side our analysis builds on links between learning t heory and semialgebraic geometry.\n LOCATION:https://researchseminars.org/talk/MADDD/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Paul Atzberger (UC Davis) DTSTART:20200519T230000Z DTEND:20200520T001500Z DTSTAMP:20260422T212707Z UID:MADDD/4 DESCRIPTION:Title: Ge ometric approaches for machine learning in the sciences and engineering\nby Paul Atzberger (UC Davis) as part of Mathematics of Data and Decisio ns @ Davis\n\n\nAbstract\nThere has been a lot of interest recently in lev eraging machine learning approaches for modeling and analysis in the scien ces and engineering. This poses significant challenges and requirements r elated to data efficiency\, interpretability\, and robustness. For scient ific problems there is often a lot of prior knowledge about general underl ying physical principles\, existence of low dimensional latent structures\ , or groups of invariances or equivariances. We discuss approaches for re presenting some of this knowledge to enhance learning methods by using res ults on manifold embeddings\, stochastic processes within manifolds\, and harmonic analysis. We show how the approaches can be used for high-dimens ional stochastic dynamical systems with slow-fast time-scale separations t o learn from observations\, slow variable representations and reduced mode ls for the dynamics. We also discuss a few other examples where utilizing geometric structure has the potential to improve outcomes in scientific m achine learning.\n LOCATION:https://researchseminars.org/talk/MADDD/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Prem Devanbu (UC Santa Barbara) DTSTART:20200526T230000Z DTEND:20200527T001500Z DTSTAMP:20260422T212707Z UID:MADDD/5 DESCRIPTION:Title: Ba sic concepts and live tutorial on docker containers for novice users\n by Prem Devanbu (UC Santa Barbara) as part of Mathematics of Data and Deci sions @ Davis\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/MADDD/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Henry Adams (Colorado State University) DTSTART:20200602T230000Z DTEND:20200603T001500Z DTSTAMP:20260422T212707Z UID:MADDD/6 DESCRIPTION:Title: Ap plied topology: From global to local.\nby Henry Adams (Colorado State University) as part of Mathematics of Data and Decisions @ Davis\n\n\nAbst ract\nThrough the use of examples\, I will explain one way in which applie d topology has evolved since the birth of persistent homology in the early 2000s. The first applications of topology to data emphasized the global s hape of a dataset\, such as the three-circle model for 3 x 3 pixel patches from natural images\, or the configuration space of the cyclo-octane mole cule\, which is a sphere with a Klein bottle attached via two circles of s ingularity. More recently\, persistent homology is being used to measure t he local geometry of data. How do you vectorize geometry for use in machin e learning problems? Persistent homology\, and its vectorization technique s including persistence landscapes and persistence images\, provide popula r techniques for incorporating geometry in machine learning. I will survey applications arising from machine learning tasks in agent-based modeling\ , shape recognition\, archaeology\, materials science\, and biology.\n LOCATION:https://researchseminars.org/talk/MADDD/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Bartolomeo Stellato (MIT) DTSTART:20200609T230000Z DTEND:20200610T001500Z DTSTAMP:20260422T212707Z UID:MADDD/7 DESCRIPTION:Title: A machine learning approach to optimization\nby Bartolomeo Stellato (MIT ) as part of Mathematics of Data and Decisions @ Davis\n\n\nAbstract\nMost applications in engineering\, operations research and finance rely on sol ving the same optimization problem several times with varying parameters. This method generates a large amount of data that is usually discarded. In this talk\, we describe how to use historical data to understand and solv e optimization problems. We present a machine learning approach to predict the strategy behind the optimal solution of continuous and mixed-integer convex optimization problems. Using interpretable algorithms such as optim al classification trees we gain insights on the relationship between the p roblem data and the optimal solution. In this way\, optimization is no lon ger a black-box and practitioners can understand it. Moreover\, our method is able to compute the optimal solutions at very high speed. This applies also to non-interpretable machine learning predictors such as neural netw orks since they can be evaluated very efficiently. We benchmark our approa ch on several examples obtaining accuracy above 90% and computation times multiple orders of magnitude faster than state-of-the-art solvers. Therefo re\, our method provides on the one hand a novel insightful understanding of the optimal strategies to solve a broad class of continuous and mixed-i nteger optimization problems and on the other hand a powerful computationa l tool to solve online optimization at very high speed.\n LOCATION:https://researchseminars.org/talk/MADDD/7/ END:VEVENT END:VCALENDAR