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X-WR-CALDESC:Events for IDSS STAGE
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BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20180311T070000
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TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20181104T060000
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TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
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TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
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TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
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TZOFFSETFROM:-0400
TZOFFSETTO:-0500
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DTSTART:20201101T060000
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191104T100000
DTEND;TZID=America/New_York:20191104T110000
DTSTAMP:20260412T054136
CREATED:20190802T191628Z
LAST-MODIFIED:20191028T203636Z
UID:10464-1572861600-1572865200@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar (updated start time)
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty who introduces the program and answers your questions. \nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar-6/
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20191101
DTEND;VALUE=DATE:20191103
DTSTAMP:20260412T054136
CREATED:20190910T153401Z
LAST-MODIFIED:20190910T154051Z
UID:10658-1572566400-1572739199@idss-stage.mit.edu
SUMMARY:LIDS@80: A Celebration
DESCRIPTION:We are pleased to announce that registration is now open for the LIDS 80th-anniversary celebration. \nThis free event will take place on November 1-2\, 2019 at MIT. Advance registration is required. \nRegistration closes on October 3\, 2019.
URL:https://idss-stage.mit.edu/calendar/lids80-a-celebration/
LOCATION:Tang Building (E51)\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191030T160000
DTEND;TZID=America/New_York:20191030T170000
DTSTAMP:20260412T054136
CREATED:20190923T182903Z
LAST-MODIFIED:20191028T170752Z
UID:10862-1572451200-1572454800@idss-stage.mit.edu
SUMMARY:FinTech in China and the extension of new organizational firm boundary
DESCRIPTION:Speaker:\nZixia Sheng\, CEO\, New Hope Financial Services \nAbstract:\nRecent new technologies (Fintech and 5G) have had a profound impact on extending the boundaries of firms into more complicated financial ecology system. Nowadays in China\, a typical traditional loan underwriting procedure within a bank has been fulfilled by different external parties (e.g. online portals\, marketing agencies\, data vendors\, risk modelers\, trusts\, funds\, invest bankers\, debt collectors). How new technology could improve information sharing\, reduce transaction costs/contractual costs and therefore change the entire landscape of firm organization and supply chain will be a critical extension to my introduction of Fintech in China. \nAbout the Speaker:\nZixia Sheng received his M.Sc. in Decision Science from Carnegie Mellon University in 2006. The following year he started a position at Discover Financial Services. From 2012 to 2018 Sheng was the Director or Ant Financial’s AI Division and also the CRO of the organisation’s Banking Divison. Currently\, Zixia Sheng is the CEO of New Hope Financial Services. His focuses on inclusive financial services and big data solutions for industrial and agricultural supply chains.
URL:https://idss-stage.mit.edu/calendar/fintech-in-china-and-the-extension-of-new-organizational-firm-boundary/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191030T160000
DTEND;TZID=America/New_York:20191030T160000
DTSTAMP:20260412T054136
CREATED:20191023T184829Z
LAST-MODIFIED:20191023T185032Z
UID:11043-1572451200-1572451200@idss-stage.mit.edu
SUMMARY:Using Bagged Posteriors for Robust Inference
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \n**PLEASE NOTE ROOM CHANGE TO BUILDING 37-212 FOR THE WEEKS OF 10/30 AND 11/6** \nSpeaker:   \nJonathan Huggins (Boston University) \nAbstract: \nStandard Bayesian inference is known to be sensitive to misspecification between the model and the data-generating mechanism\, leading to unreliable uncertainty quantification and poor predictive performance. However\, finding generally applicable and computationally feasible methods for robust Bayesian inference under misspecification has proven to be a difficult challenge. An intriguing approach is to use bagging on the Bayesian posterior (“BayesBag”); that is\, to use the average of posterior distributions conditioned on bootstrapped datasets. In this talk\, I describe the statistical behavior of BayesBag\, propose a model–data mismatch index for diagnosing model misspecification using BayesBag\, and empirically validate our BayesBag methodology on synthetic and real-world data. We find that in the presence of significant misspecification\, BayesBag yields more reproducible inferences\, has better predictive accuracy\, and selects correct models more often than the standard Bayesian posterior; meanwhile\, when the model is correctly specified\, BayesBag produces superior or equally good results for parameter inference and prediction\, while being slightly more conservative for model selection. Overall\, our results demonstrate that BayesBag combines the attractive modeling features of standard Bayesian inference with the distributional robustness properties of frequentist methods. \nBio: \nJonathan Huggins will formally join the Mathematics & Statistics faculty of Boston University in January 2020 as an Assistant Professor\, coming from Harvard University\, where he has been a postdoctoral fellow in biostatistics. \n\n\n\n\n–\n\n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes.  For more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/\n \n**Meetings are open to any interested researcher.
URL:https://stat.mit.edu/calendar/using-bagged-posteriors-for-robust-inference/
LOCATION:37-212
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191029T110000
DTEND;TZID=America/New_York:20191029T120000
DTSTAMP:20260412T054136
CREATED:20191022T130053Z
LAST-MODIFIED:20191028T165950Z
UID:11020-1572346800-1572350400@idss-stage.mit.edu
SUMMARY:Communicating uncertainty about facts\, numbers and science
DESCRIPTION:The claim of a ‘post-truth’ society\, in which emotional responses trump balanced consideration of evidence\, presents a strong challenge to those who value quantitative and scientific evidence: how can we communicate risks and unavoidable scientific uncertainty in a transparent and trustworthy way? \nCommunication of quantifiable risks has been well-studied\, leading to recommendations for using an expected frequency format. But deeper uncertainty about facts\, numbers\, or scientific hypotheses needs to be communicated without losing trust and credibility. This is an empirically researchable issue\, and I shall describe some current randomised experiments concerning the impact on audiences of alternative verbal\, numerical and graphical means of communicating uncertainty. \nAvailable evidence may often not permit a quantitative assessment of uncertainty\, and I will also examine scales being used to summarise degrees of ‘confidence’ in conclusions\, in terms of the quality of the research underlying the whole assessment. \nAbout the speaker: Professor Sir David Spiegelhalter is Chair of the Winton Centre for Risk and Evidence Communication in the University of Cambridge\, which aims to improve the way that statistical evidence is used by health professionals\, patients\, lawyers and judges\, media and policy-makers. He advises organisations and government agencies on risk communication and is a regular media commentator on statistical issues\, with a particular focus on communicating uncertainty. His background is in medical statistics\, and he has over 200 refereed publications and is co-author of 6 textbooks\, as well as The Norm Chronicles (with Michael Blastland)\, and Sex by Numbers. He works extensively with the media\, and presented the BBC4 documentaries “Tails you Win: the Science of Chance”\, the award-winning “Climate Change by Numbers”\, and in 2011 came 7 th in an episode of BBC1’s Winter Wipeout. He was elected Fellow of the Royal Society in 2005\, and knighted in 2014 for services to medical statistics. He was President of the Royal Statistical Society for 2017-2018. His bestselling book\, The Art of Statistics\, was published in March 2019. He is @d_spiegel on Twitter\, and his homepage is http://www.statslab.cam.ac.uk/~david/.
URL:https://idss-stage.mit.edu/calendar/communicating-uncertainty-about-facts-numbers-and-science/
LOCATION:32-D643
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191028T160000
DTEND;TZID=America/New_York:20191028T170000
DTSTAMP:20260412T054136
CREATED:20190920T150920Z
LAST-MODIFIED:20190920T150920Z
UID:10846-1572278400-1572282000@idss-stage.mit.edu
SUMMARY:The Age of Information in Networks: Moments\, Distributions\, and Sampling
DESCRIPTION:We examine a source providing status updates to monitors through a network with state defined by a continuous-time finite Markov chain. Using an age of information (AoI) metric\, we characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach\, we derive first-order linear differential equations for the temporal evolution of both the age moments and a moment generating function (MGF) of the age vector components. We show that the existence of a non-negative fixed point for the first moment is sufficient to guarantee convergence of all higher-order moments as well as a region of convergence for the stationary MGF vector of the age. The stationary MGF vector is then found for the age on a line network of preemptive memoryless servers. It is found that the age at a node is identical in distribution to the sum of independent exponential service times. This observation is then generalized to linear status sampling networks in which each node receives samples of the update process at each preceding node according to a renewal point process. For each node in the line\, the age is shown to be identical in distribution to a sum of independent renewal process age random variables. \nBio: Roy Yates is a Distinguished Professor with the Wireless Information Networks Laboratory (WINLAB) and the Electrical and Computer Engineering (ECE) department at Rutgers University. He received the B.S.E. degree in 1983 from Princeton University\, and the S.M. and Ph.D. degrees in 1986 and 1990 from M.I.T.\, all in Electrical Engineering. He is an author of three editions of the John Wiley textbook “Probability and Stochastic Processes: A Friendly Introduction for Electrical Engineers.” An IEEE Fellow in 2011\, Dr. Yates is a past associate editor of the IEEE Journal on Selected Areas of Communication Series in Wireless Communication and also a past Associate Editor for Communication Networks of the IEEE Transactions on Information Theory. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/age-information-networks-moments-distributions-and-sampling%C2%A0
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191025T110000
DTEND;TZID=America/New_York:20191025T120000
DTSTAMP:20260412T054136
CREATED:20191017T132846Z
LAST-MODIFIED:20191021T134432Z
UID:10980-1572001200-1572004800@idss-stage.mit.edu
SUMMARY:Accurate Simulation-Based Parametric Inference in High Dimensional Settings
DESCRIPTION:Abstract: \nAccurate estimation and inference in finite sample is important for decision making in many experimental and social fields\, especially when the available data are complex\, like when they include mixed types of measurements\, they are dependent in several ways\, there are missing data\, outliers\, etc. Indeed\, the more complex the data (hence the models)\, the less accurate are asymptotic theory results in finite samples.  This is in particular the case\, for example\, with logistic regression\, with possibly also random effects to account for the dependence structure between the outcomes\, or more generally\, when the likelihood function or the estimating equations have non closed-form expression. Moreover\, resampling techniques such as the Bootstrap can also be quite inaccurate in these settings\, unless (complex) corrections are provided. We propose instead a simulation based method\, the Iterative Bootstrap (IB)\, that can be used\, very generally\, to obtain a) unbiased estimators in high dimensional settings\, b) finite sample distributions for inference\, with\, under suitable conditions\, the exact probability coverage property. The method is based on an initial estimator\, that does not need to be consistent and can hence be chosen for numerical convenience\, and/or can have some desirable properties such as robustness. We present the main theoretical results and the relationships with well-established methods\, as well as simulation studies involving complex models and different estimators. \nAbout the Speaker: \nMaria-Pia Victoria-Feser is currently professor of statistics at the Geneva School of Economics and Management\, University of Geneva\, Switzerland. She received her Ph.D. in econometrics and statistics from the University of Geneva\, and started her carrier as a lecturer at the London School of Economics and Management. She was awarded the Latzis International Prize for her Ph.D. thesis\, as well as doctoral and professorial fellowships from the Swiss National Science Foundation. \nMaria-Pia Victoria-Feser’s research interests are in statistical methodology (robust statistics\, model selection and simulation based inference in high dimensions for complex models) with applications in economics (welfare economics\, extremes)\, psychology and social sciences (generalized linear latent variable models)\, and engineering (time series for geo-localization). She has published in leading journals in statistics as well as in related fields.
URL:https://stat.mit.edu/calendar/victoria-feser/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191023T160000
DTEND;TZID=America/New_York:20191023T170000
DTSTAMP:20260412T054136
CREATED:20191021T140515Z
LAST-MODIFIED:20191021T140515Z
UID:11003-1571846400-1571850000@idss-stage.mit.edu
SUMMARY:Esther Williams in the Harold Holt Memorial Swimming Pool: Some Thoughts on Complexity
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \nSpeaker: \nDaniel Simpson (University of Toronto) \nAbstract: \n\nAbstract: As data becomes more complex and computational modelling\nbecomes more powerful\, we rapidly find ourselves beyond the scope of\ntraditional statistical theory. As we venture beyond the traditional\nthunderdome\, we need to think about how to cope with this additional\ncomplexity in our model building.  In this talk\, I will talk about a\nfew techniques that are useful when specifying prior distributions and\nbuilding Bayesian models for complex data.\n\n\n\n\nBio:\nDaniel Simpson is an Assistant Professor at the University of Toronto’s Department of Statistical Sciences.\n\n\n\n–\n\n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes.  For more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/\n \n**Meetings are open to any interested researcher.
URL:https://stat.mit.edu/calendar/esther-williams-in-the-harold-holt-memorial-swimming-pool-some-thoughts-on-complexity/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191021T160000
DTEND;TZID=America/New_York:20191021T170000
DTSTAMP:20260412T054136
CREATED:20190920T150813Z
LAST-MODIFIED:20191010T121729Z
UID:10844-1571673600-1571677200@idss-stage.mit.edu
SUMMARY:LIDS Seminar - George Pappas (University of Pennsylvania)
DESCRIPTION:TBD \nBio: \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-george-pappas-university-pennsylvania
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191018T110000
DTEND;TZID=America/New_York:20191018T120000
DTSTAMP:20260412T054136
CREATED:20191015T180210Z
LAST-MODIFIED:20191015T180614Z
UID:10971-1571396400-1571400000@idss-stage.mit.edu
SUMMARY:Towards Robust Statistical Learning Theory
DESCRIPTION:Abstract: \nReal-world data typically do not fit statistical models or satisfy assumptions underlying the theory exactly\, hence reducing the number and strictness of these assumptions helps to lessen the gap between the “mathematical” world and the “real” world. The concept of robustness\, in particular\, robustness to outliers\, plays the central role in understanding this gap. The goal of the talk is to introduce the principles and robust algorithms based on these principles that can be applied in the general framework of statistical learning theory. These algorithms avoid explicit (and often bias-producing) outlier detection and removal\, instead taking advantage of induced symmetries in the distribution of the data. \nI will discuss uniform deviation bounds for the mean estimators of heavy-tailed distributions and applications of these bounds to robust empirical risk minimization. \nImplications of proposed techniques for logistic regression and regression with quadratic loss will be highlighted. \nThis talk is partially based on a joint work with Timothée Mathieu. \nBiography: \nStanislav Minsker is currently an Assistant Professor in the Department of Mathematics at the University of Southern California. He received B.Sc. in Mathematics from the Novosibirsk State University in 2007 and Ph.D. in Mathematics from the Georgia Institute of Technology in 2012. Prior to joining USC\, he was a Visiting Assistant Professor at Duke University and worked in Quantitative Analytics at Wells Fargo Securities. His main research interests are in the areas of statistical learning theory\, robust statistics\, and concentration of measure inequalities.
URL:https://stat.mit.edu/calendar/minsker/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191016T160000
DTEND;TZID=America/New_York:20191016T170000
DTSTAMP:20260412T054136
CREATED:20191010T172901Z
LAST-MODIFIED:20191010T173013Z
UID:10964-1571241600-1571245200@idss-stage.mit.edu
SUMMARY:Markov Chain Monte Carlo Methods and Some Attempts at Parallelizing Them
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \nAbstract: \nMCMC methods yield approximations that converge to quantities of interest in the limit of the number of iterations. This iterative asymptotic justification is not ideal: it stands at odds with current trends in computing hardware. Namely\, it would often be computationally preferable to run many short chains in parallel\, but such an approach is flawed because of the so-called “burn-in” bias.  This talk will first describe that issue and some known resolutions\, including regeneration techniques and sequential Monte Carlo samplers.  Then I will describe a recent proposal\, joint work with John O’Leary\, Yves Atchadé and others\, that allows to completely remove the burn-in bias. In a nutshell\, the proposed unbiased estimators are constructed from pairs of chains\, that are generated over a random\, finite number of iterations. Furthermore\, their variances and costs can be made arbitrarily close to those of standard MCMC estimators\, if desired.  The proposed method is described in https://arxiv.org/abs/1708.03625 and code in R is available to reproduce the experiments at https://github.com/pierrejacob/unbiasedmcmc. \nBiography: \nPierre E. Jacob is an Associate Professor of Statistics at Harvard University.  He develops methods for statistical inference\, e.g. to run Monte Carlo methods on parallel computers\, to compare models\, to estimate latent variables\, and to deal with intractable likelihood functions. \n– \nFor more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes. And the meetings are open to any interested researcher.   Talks will be followed by 30 minutes of tea/snacks and informal discussion.**
URL:https://stat.mit.edu/calendar/markov-chain-monte-carlo-methods-and-some-attempts-at-parallelizing-them/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191016T090000
DTEND;TZID=America/New_York:20191016T100000
DTSTAMP:20260412T054136
CREATED:20190802T190945Z
LAST-MODIFIED:20190802T192632Z
UID:10462-1571216400-1571220000@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty who introduces the program and answers your questions. \nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar-5/
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191011T110000
DTEND;TZID=America/New_York:20191011T120000
DTSTAMP:20260412T054136
CREATED:20190923T173105Z
LAST-MODIFIED:20190926T135551Z
UID:10860-1570791600-1570795200@idss-stage.mit.edu
SUMMARY:The Planted Matching Problem
DESCRIPTION:Abstract:\n\nWhat happens when an optimization problem has a good solution built into it\, but which is partly obscured by randomness? Here we revisit a classic polynomial-time problem\, the minimum perfect matching problem on bipartite graphs. If the edges have random weights in [0\,1]\, Mézard and Parisi — and then Aldous\, rigorously — showed that the minimum matching has expected weight zeta(2) = pi^2/6. We consider a “planted” version where a particular matching has weights drawn from an exponential distribution with mean mu/n. When mu < 1/4\, the minimum matching is almost identical to the planted one. When mu > 1/4\, the overlap between the two is given by a system of differential equations that result from a message-passing algorithm. This is joint work with Mehrdad Moharrami (Michigan) and Jiaming Xu (Duke).\n\nBiography:\n\nCristopher Moore received his B.A. in Physics\, Mathematics\, and Integrated Science from Northwestern University\, and his Ph.D. in Physics from Cornell. From 2000 to 2012 he was a professor at the University of New Mexico\, with joint appointments in Computer Science and Physics. Since 2012\, Moore has been a resident professor at the Santa Fe Institute; he has also held visiting positions at École Normale Superieure\, École Polytechnique\, Université Paris 7\, the Niels Bohr Institute\, Northeastern University\, and the University of Michigan. He has published over 150 papers at the boundary between physics and computer science\, ranging from quantum computing\, to phase transitions in NP-complete problems\, to the theory of social networks and efficient algorithms for analyzing their structure. He is an elected Fellow of the American Physical Society\, the American Mathematical Society\, and the American Association for the Advancement of Science. With Stephan Mertens\, he is the author of The Nature of Computation from Oxford University Press.\n\n\n\n–\n\n\n\nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/moore/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191009T160000
DTEND;TZID=America/New_York:20191009T170000
DTSTAMP:20260412T054136
CREATED:20191007T141618Z
LAST-MODIFIED:20191007T141618Z
UID:10922-1570636800-1570640400@idss-stage.mit.edu
SUMMARY:Probabilistic Programming and Artificial Intelligence
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \nAbstract: \nProbabilistic programming is an emerging field at the intersection of programming languages\, probability theory\, and artificial intelligence. This talk will show how to use recently developed probabilistic programming languages to build systems for robust 3D computer vision\, without requiring any labeled training data; for automatic modeling of complex real-world time series; and for machine-assisted analysis of experimental data that is too small and/or messy for standard approaches from machine learning and statistics. \nThis talk will use these applications to illustrate recent technical innovations in probabilistic programming that formalize and unify modeling approaches from multiple eras of AI\, including generative models\, neural networks\, symbolic programs\, causal Bayesian networks\, and hierarchical Bayesian modeling. Specifically\, it will present languages in which models are represented using executable code\, and in which inference is programmable using novel constructs for Monte Carlo\, optimization-based\, and neural inference. It will also present techniques for Bayesian learning of probabilistic program structure and parameters from real-world data. Finally\, this talk will review challenges and research opportunities in the development and use of general-purpose probabilistic programming languages that performant enough and flexible enough for real-world AI engineering. \nBiography: \nVikash Mansinghka is a Principal Research Scientist at MIT\, where he leads the MIT Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT\, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science\, and his research on the Picture probabilistic programming language won an award at CVPR. He co-founded two VC-backed startups — Prior Knowledge (acquired by Salesforce in 2012) and Empirical Systems (acquired by Tableau in 2018) — and has consulted on probabilistic programming for leading companies in the semiconductor\, biopharma\, IT services\, and banking sectors. He served on DARPA’s Information Science and Technology advisory board from 2010-2012\, currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation\, and co-founded the International Conference on Probabilistic Programming. \n=========== \nFor more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes. And the meetings are open to any interested researcher.   Talks will be followed by 30 minutes of tea/snacks and informal discussion.**
URL:https://stat.mit.edu/calendar/probabilistic-programming-and-artificial-intelligence/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191007T160000
DTEND;TZID=America/New_York:20191007T170000
DTSTAMP:20260412T054136
CREATED:20190722T170917Z
LAST-MODIFIED:20191009T193356Z
UID:10361-1570464000-1570467600@idss-stage.mit.edu
SUMMARY:Theoretical Foundations of Active Machine Learning
DESCRIPTION:Title:\nTheoretical Foundations of Active Machine Learning\nAbstract:\nThe field of Machine Learning (ML) has advanced considerably in recent years\, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate text\, but they must be trained with more images and text than a person can see in nearly a lifetime.  The computational complexity of training has been offset by recent technological advances\, but the cost of training data is measured in terms of the human effort in labeling data. People are not getting faster nor cheaper\, so generating labeled training datasets has become a major bottleneck in ML pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative examples for labeling so that human time is not wasted labeling irrelevant\, redundant\, or trivial examples. This talk explores the development of active ML theory and methods over the past decade\, including recently proposed approaches to active ML with nonparametric or overparameterized models such as neural networks. \nSpeaker: Rob Nowak\, University of Wisconsin\, Madison\nReception to follow.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-with-rob-nowak-university-of-wisconsin-madison/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191002T160000
DTEND;TZID=America/New_York:20191002T170000
DTSTAMP:20260412T054136
CREATED:20191001T173138Z
LAST-MODIFIED:20191001T173138Z
UID:10895-1570032000-1570035600@idss-stage.mit.edu
SUMMARY:Behavior of the Gibbs Sampler in the Imbalanced Case/Bias Correction from Daily Min and Max Temperature Measurements
DESCRIPTION:IDS.190 Topics in Bayesian Modeling and Computation \n*Note:  The speaker this week will give two shorter talks within the usual session \nTitle: \nBehavior of the Gibbs sampler in the imbalanced case \nAbstract:   \nMany modern applications collect highly imbalanced categorical data\, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information\, while also quantifying uncertainty. However\, posterior computation presents a fundamental barrier to routine use; a single class of algorithms does not work well in all settings and practitioners waste time trying different types of MCMC approaches. This talk is motivated by an application to quantitative advertising in which we encountered extremely poor computational performance for common data augmentation MCMC algorithms but obtained excellent performance for adaptive Metropolis. To obtain a deeper understanding of this behavior\, we give strong theory results on computational complexity in an infinitely imbalanced asymptotic regime. Our results show why the data augmentations methods work poorly. \nTitle:   \nBias correction from the daily min and the max temperature measurements. \nAbstract:   \nThis will be a talk on an applied project\, which involves a mix of modeling and obtaining MCMC samplers for a data set from the climate sciences. \n=========== \nFor more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes. And the meetings are open to any interested researcher.   Talks will be followed by 30 minutes of tea/snacks and informal discussion.**
URL:https://stat.mit.edu/calendar/pillai/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191001T160000
DTEND;TZID=America/New_York:20191001T170000
DTSTAMP:20260412T054136
CREATED:20190920T150647Z
LAST-MODIFIED:20190920T150647Z
UID:10842-1569945600-1569949200@idss-stage.mit.edu
SUMMARY:Data-driven Coordination of Distributed Energy Resources
DESCRIPTION:The integration of distributed energy resources (DERs)\, e.g.\, rooftop photovoltaics installations\, electric energy storage devices\, and flexible loads\, is becoming prevalent. This integration poses numerous operational challenges on the lower-voltage systems to which the DERs are connected\, but also creates new opportunities for the provision of grid services. In the first part of the talk\, we discuss one such operational challenge—ensuring proper voltage regulation in the distribution network to which DERs are connected. To address this problem\, we propose a Volt/VAR control architecture that relies on the proper coordination of conventional voltage regulation devices\, e.g.\, tap changing under load (TCUL) transformers and switched capacitors and DERs with reactive power provision capability. In the second part of the talk\, we discuss one such opportunity—utilizing DERs to provide regulation services to the bulk power grid. To leverage this opportunity\, we propose a scheme for coordinating the response of the DERs so that the power injected into the distribution network (to which the DERs are connected) follows some regulation signal provided by the bulk power system operator. Throughout the talk\, we assume limited knowledge of the particular power system models and develop data-driven methods to learn them. We then utilize these models to design appropriate controls for determining the set-points of DERs (and other assets\, e.g.\, TCULs) in an optimal or nearly-optimal fashion. \nBio: \nAlejandro Dominguez-Garcıa is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign\, where he is affiliated with the Power and Energy Systems area. Also within ECE Illinois\, he is a Research Professor in the Coordinated Science Laboratory and in the Information Trust Institute and has been a Grainger Associate since 2011\, and a William L. Everitt Scholar since 2017. His research program aims at the development of technologies for providing a reliable and efficient supply of electricity. Specific activities within his program include work on: (i) control of distributed energy resources\, (ii) power system health monitoring and reliability analysis\, and (iii) quantifying and mitigating the impact of renewable-based generation.\n\nProfessor Dom´ınguez-Garc´ıa received the degree of “Ingeniero Industrial” from the University of Oviedo in 2001\, and the Ph.D. degree in electrical engineering and computer science from MIT in 2007. He also spent time as a post-doctoral research associate at MIT before joining the Illinois faculty in 2008. He received the NSF CAREER Award in 2010\, and the Young Engineer Award from the IEEE Power and Energy Society in 2012. In 2014\, he was invited by the National Academy of Engineering to attend the US Frontiers of Engineering Symposium and was selected by the University of Illinois at Urbana-Champaign Provost to receive a Distinguished Promotion Award. In 2015\, he received the U of I College of Engineering Dean’s Award for Excellence in Research. He is currently an associate editor of the IEEE Transactions on Control of Network Systems. He also served as an editor of the IEEE Transactions on Power Systems and IEEE Power Engineering Letters from 2011 to 2017.\n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/data-driven-coordination-distributed-energy-resources
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190930T160000
DTEND;TZID=America/New_York:20190930T170000
DTSTAMP:20260412T054136
CREATED:20190619T144446Z
LAST-MODIFIED:20191001T175118Z
UID:9778-1569859200-1569862800@idss-stage.mit.edu
SUMMARY:Selection and Endogenous Bias in Studies of Health Behaviors
DESCRIPTION:Abstract:\nStudies of health behaviors using observational data are prone to bias from selection in behavior choices. How important are these biases? Are they dynamic – that is\, are they influenced by the recommendations we make? Are there formal assumptions under which we can use information we have about selection on observed variables to learn about the possible bias from unobserved selection? \nAbout the Speaker:\nEmily Oster is a professor of economics. Prior to coming to Brown she was an associate professor at the University of Chicago Booth School of Business. She is affiliated with the National Bureau of Economic Research. She earned her BA and her PhD from Harvard\, in 2002 and 2006\, respectively. \n  \nReception to follow.
URL:https://idss-stage.mit.edu/calendar/selection-and-endogenous-bias-in-studies-of-health-behaviors/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190930
DTEND;VALUE=DATE:20191001
DTSTAMP:20260412T054136
CREATED:20190716T135001Z
LAST-MODIFIED:20191218T184639Z
UID:10286-1569801600-1569887999@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data-Driven Decisions
DESCRIPTION:Developed by 11 MIT faculty members at IDSS\, this seven-week course is specially designed for data scientists\, business analysts\, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins September 30\, 2019. \n 
URL:https://xpro.mit.edu/courses/course-v1:xPRO+DSx/?utm_medium=website&#038;utm_source=idss&#038;utm_campaign=dsx-3t-2019&#038;utm_content=event-calendar
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190927T110000
DTEND;TZID=America/New_York:20190927T120000
DTSTAMP:20260412T054136
CREATED:20190923T172454Z
LAST-MODIFIED:20191016T163112Z
UID:10858-1569582000-1569585600@idss-stage.mit.edu
SUMMARY:Frontiers of Efficient Neural-Network Learnability
DESCRIPTION:Abstract:  \nWhat are the most expressive classes of neural networks that can be learned\, provably\, in polynomial-time in a distribution-free setting? In this talk we give the first efficient algorithm for learning neural networks with two nonlinear layers using tools for solving isotonic regression\, a nonconvex (but tractable) optimization problem. If we further assume the distribution is symmetric\, we obtain the first efficient algorithm for recovering the parameters of a one-layer convolutional network. These results implicitly make use of a convex surrogate loss for generalized linear models and go beyond the kernel-method/overparameterization regime used in recent works.\n\nBiography:  \nAdam Klivans is a professor of computer science at the University of Texas at Austin who works in theoretical computer science and machine learning. He completed his doctorate in mathematics from MIT\, where he was awarded the Charles W. and Jennifer C. Johnson Prize. \nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/frontiers/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190923T140000
DTEND;TZID=America/New_York:20190923T150000
DTSTAMP:20260412T054136
CREATED:20190920T150517Z
LAST-MODIFIED:20190920T150517Z
UID:10840-1569247200-1569250800@idss-stage.mit.edu
SUMMARY:Power of Experimental Design and Active Learning
DESCRIPTION:Classical supervised machine learning algorithms focus on the setting where the algorithm has access to a fixed labeled dataset obtained prior to any analysis. In most applications\, however\, we have control over the data collection process such as which image labels to obtain\, which drug-gene interactions to record\, which network routes to probe\, which movies to rate\, etc. Furthermore\, most applications face budget limitations on the amount of labels that can be collected. Experimental design and active learning are two paradigms that involve careful selection of data points to label from a large unlabeled pool. This talk will discuss and contrast the power of experimental design and active learning\, starting with some recent advances in these paradigms and then posing open questions involving their integration and application to deep models. \nBio: Aarti Singh is an Associate Professor in the Machine Learning Department at Carnegie Mellon University. Her research lies at the intersection of machine learning\, statistics and signal processing\, and focuses on designing statistically and computationally efficient algorithms for learning from direct\, compressive and interactive queries. Her work is recognized by an NSF Career Award\, the United States Air Force Young Investigator Award\, A. Nico Habermann Junior Faculty Chair Award\, Harold A. Peterson Best Dissertation Award\, and three best student paper awards. Her service honors include serving as Program Chair for the International Conference on Machine Learning (ICML) 2020\, Program Chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference\, member of the National Academy of Sciences (NAS) Committee on Applied and Theoretical Statistics\, guest editor for Electronic Journal of Statistics\, and Associate Editor of the IEEE Transactions on Information Theory and IEEE Transactions on Signal and Information Processing over Networks. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/power-experimental-design-and-active-learning
LOCATION:E18-304\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190920T110000
DTEND;TZID=America/New_York:20190920T120000
DTSTAMP:20260412T054136
CREATED:20190910T191447Z
LAST-MODIFIED:20191016T163208Z
UID:10670-1568977200-1568980800@idss-stage.mit.edu
SUMMARY:Some New Insights On Transfer Learning
DESCRIPTION:Abstract:  \nThe problem of transfer and domain adaptation is ubiquitous in machine learning and concerns situations where predictive technologies\, trained on a given source dataset\, have to be transferred to a new target domain that is somewhat related. For example\, transferring voice recognition trained on American English accents to apply to Scottish accents\, with minimal retraining. A first challenge is to understand how to properly model the ‘distance’ between source and target domains\, viewed as probability distributions over a feature space.\n\nIn this talk we will argue that various existing notions of distance between distributions turn out to be pessimistic\, i.e.\, these distances might appear high in many situations where transfer is possible\, even at fast rates. Instead we show that some new notions of distance tightly capture a continuum from easy to hard transfer\, and furthermore can be adapted to\, i.e.\, do not need to be estimated in order to perform near-optimal transfer. Finally we will discuss near-optimal approaches to minimizing sampling of target data (e.g. sampling Scottish speech)\, when one already has access to a given amount of source data (e.g. American speech).\n\nThis talk is based on some joint work with G. Martinet\, and ongoing work with S. Hanneke.\n\nBiography:  \nSamory Kpotufe is an Associate Professor in Statistics at Columbia University. He works in machine learning\, with an emphasis on nonparametric methods and high dimensional statistics. Generally\, his interests are in understanding basic learning scenarios under practical constraints from modern application domains. He has previously held positions at the Max Planck Institute in Germany\, the Toyota Technological Institute at Chicago\, and Princeton University. \nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/some-new-insights-on-transfer-learning/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190918T160000
DTEND;TZID=America/New_York:20190918T170000
DTSTAMP:20260412T054136
CREATED:20190916T194901Z
LAST-MODIFIED:20190916T194901Z
UID:10702-1568822400-1568826000@idss-stage.mit.edu
SUMMARY:Probabilistic Modeling meets Deep Learning using TensorFlow Probability
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \nSpeaker: \nBrian Patton (Google AI) \nAbstract: \nTensorFlow Probability provides a toolkit to enable\nresearchers and practitioners to integrate uncertainty with\ngradient-based deep learning on modern accelerators. In this talk\nwe’ll walk through some practical problems addressed using TFP;\ndiscuss the high-level interfaces\, goals\, and principles of the\nlibrary; and touch on some recent innovations in describing\nprobabilistic graphical models. Time-permitting\, we may touch on a\ncouple areas of research interest for the team.\n\n–\n\n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes.  For more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/\n \n**Meetings are open to any interested researcher.
URL:https://stat.mit.edu/calendar/probabilistic-modeling-meets-deep-learning-using-tensorflow-probability/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190916T160000
DTEND;TZID=America/New_York:20190916T170000
DTSTAMP:20260412T054136
CREATED:20190920T150317Z
LAST-MODIFIED:20190920T150317Z
UID:10838-1568649600-1568653200@idss-stage.mit.edu
SUMMARY:Dynamic Monitoring and Decision Systems (DyMonDS) Framework for Data-Enabled Integration in Complex Electric Energy Systems
DESCRIPTION:In this talk\, we introduce a unifying Dynamic Monitoring and Decision Systems (DyMonDS) framework that is based on multi-layered modeling for aggregation and minimal coordination of interactions between the layers of complex electric energy systems. Using this approach\, distributed control and optimization problems are formulated so that: (1) the low-level decision-makers optimize cost of local interactions while accounting for their heterogeneous technologies\, as well as for their social and risk preferences; and\, (2) the higher layer aggregators and coordinators optimize the cost of all interactions at their levels to enable cooperative control. The interactions of each layer are abstracted by using unifying energy state space and the Lagrange coefficients associated with the general physical laws. This sets the bases for both nonlinear control of power electronically-switched automation and for market design formulation. Potential benefits (such as enhanced reliability\, resiliency\, and efficiency) from integrating flexible technologies\, storage\, and control\, in particular\, are illustrated on simple IEEE test systems. \nBio: Marija Ilić has retired as a Professor Emerita at Carnegie Mellon University. She is currently a Senior Staff in the Energy Systems Group 73 at the MIT Lincoln Laboratory. She is also a Senior Research Scientist at MIT in LIDS and IDSS. She is an IEEE Life Fellow. She was the first recipient of the NSF Presidential Young Investigator Award for Power Systems. In addition to her academic work\, she has gained considerable industry experience as the founder of New Electricity Transmission Software Solutions\, Inc. (NETSS\, Inc.). She has co-authored several books on the subject of large-scale electric power systems and has co-organized an annual multidisciplinary Electricity Industry conference series at Carnegie Mellon with participants from academia\, government\, and industry. She was the founder and co-director of the Electric Energy Systems Group (EESG) at Carnegie Mellon University. Currently\, she is building EESG@MIT\, in the same spirit as EESG@CMU. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/dynamic-monitoring-and-decision-systems-dymonds-framework-data-enabled
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190911T160000
DTEND;TZID=America/New_York:20190911T170000
DTSTAMP:20260412T054136
CREATED:20190910T184518Z
LAST-MODIFIED:20190910T190807Z
UID:10666-1568217600-1568221200@idss-stage.mit.edu
SUMMARY:Automated Data Summarization for Scalability in Bayesian Inference
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \nAbstract: \nMany algorithms take prohibitively long to run on modern\, large datasets. But even in complex data sets\, many data points may be at least partially redundant for some task of interest. So one might instead construct and use a weighted subset of the data (called a “coreset”) that is much smaller than the original dataset. Typically running algorithms on a much smaller data set will take much less computing time\, but it remains to understand whether the output can be widely useful. (1) In particular\, can running an analysis on a smaller coreset yield answers close to those from running on the full data set? (2) And can useful coresets be constructed automatically for new analyses\, with minimal extra work from the user? We answer in the affirmative for a wide variety of problems in Bayesian inference. We demonstrate how to construct “Bayesian coresets” as an automatic\, practical pre-processing step. We prove that our method provides geometric decay in relevant approximation error as a function of coreset size. Empirical analysis shows that our method reduces approximation error by orders of magnitude relative to uniform random subsampling of data. Though we focus on Bayesian methods here\, we also show that our construction can be applied in other domains. \nBiography: \nTamara Broderick is an Associate Professor in EECS at MIT. \n**Meetings are open to any interested researcher.  \n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes.  For more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \n 
URL:https://stat.mit.edu/calendar/automated-data-summarization-for-scalability-in-bayesian-inference/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190906T110000
DTEND;TZID=America/New_York:20190906T120000
DTSTAMP:20260412T054136
CREATED:20190903T150512Z
LAST-MODIFIED:20190903T152812Z
UID:10580-1567767600-1567771200@idss-stage.mit.edu
SUMMARY:GANs\, Optimal Transport\, and Implicit Density Estimation
DESCRIPTION:Abstract:  \nWe first study the rate of convergence for learning distributions with the adversarial framework and Generative Adversarial Networks (GANs)\, which subsumes Wasserstein\, Sobolev\, and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions\, under a collection of objective evaluation metrics. On the nonparametric end\, we investigate the minimax optimal rates and fundamental difficulty of the implicit density estimation under the adversarial framework. On the parametric end\, we establish a theory for general neural network classes\, that characterizes the interplay on the choice of generator and discriminator. We investigate how to obtain a good statistical guarantee for GANs through the lens of regularization. We discover and isolate a new notion of regularization\, called the generator/discriminator pair regularization\, that sheds light on the advantage of GANs compared to classical approaches for density estimation. We develop novel oracle inequalities as the main tools for analyzing GANs\, which is of independent theoretical interest. \nLater\, we proceed to discuss optimal transport\, estimating under the Wasserstein metric\, and how to use them for implicit density estimation. We will point out an interesting connection between pair regularization and optimal transport.\n\n\nBiography: \nDr. Liang is an assistant professor at Chicago Booth. He is also the George C. Tiao faculty fellow in data science research. His current research interests include computational and algorithmic aspects of statistical inference\, machine learning and statistical learning theory\, stochastic methods in non-convex optimization. \nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/liang/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190530
DTEND;VALUE=DATE:20190601
DTSTAMP:20260412T054136
CREATED:20190502T161055Z
LAST-MODIFIED:20190502T161155Z
UID:9592-1559174400-1559347199@idss-stage.mit.edu
SUMMARY:Learning for Dynamics and Control (L4DC)
DESCRIPTION:Over the next decade\, the biggest generator of data is expected to be devices which sense and control the physical world. This explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning\, control theory\, and optimization. While control theory has been firmly rooted in tradition of model-based design\, the availability and scale of data (both temporal and spatial) will require rethinking of the foundations of our discipline. From a machine learning perspective\, one of the main challenges going forward is to go beyond pattern recognition and address problems in data driven control and optimization of dynamical processes. Our overall goal is to create a new community of people that think rigorously across the disciplines\, asks new questions\, and develops the foundations of this new scientific area.
URL:https://l4dc.mit.edu/
LOCATION:32-123\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190520
DTEND;VALUE=DATE:20190522
DTSTAMP:20260412T054136
CREATED:20190417T144745Z
LAST-MODIFIED:20190417T145145Z
UID:9389-1558310400-1558483199@idss-stage.mit.edu
SUMMARY:Conference on Synthetic Controls and Related Methods
DESCRIPTION:Organizers are Alberto Abadie (MIT)\, Victor Chernozhukov (MIT)\, and Guido Imbens (Stanford University). The program is posted here. \nParticipation by invitation only.
URL:https://idss-stage.mit.edu/calendar/conference-on-synthetic-controls-and-related-methods/
LOCATION:E18-304\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190514T160000
DTEND;TZID=America/New_York:20190514T170000
DTSTAMP:20260412T054136
CREATED:20190301T172026Z
LAST-MODIFIED:20190501T142034Z
UID:8991-1557849600-1557853200@idss-stage.mit.edu
SUMMARY:Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery
DESCRIPTION:The overarching goal of my research is to develop cutting-edge machine learning\, AI and operations research theory\, methods\, algorithms\, and systems to understand the basis of health and disease; develop methodology to catalyze clinical research; support clinical decisions through individualized medicine; inform clinical pathways\, better utilize resources & reduce costs; and inform public health. \nTo do this\, Prof. van der Schaar is creating what she calls Learning Engines for Healthcare (LEH’s). An LEH is an integrated ecosystem that uses machine learning\, AI and operations research to provide clinical insights and healthcare intelligence to all the stakeholders (patients\, clinicians\, hospitals\, administrators). In contrast to an Electronic Health Record\, which provides a static\, passive\, isolated display of information\, an LEH provides a dynamic\, active\, holistic & individualized display of information including alerts. \nIn this talk Prof. van der Schaar will focus on 3 steps in the development of LEH’s: \n\nBuilding a comprehensive model that accommodates irregularly sampled\, temporally correlated\, informatively censored and non-stationary processes in order to understand and predict the longitudinal trajectories of diseases.\nEstablishing the theoretical limits of causal inference and using what has been established to create a new approach that makes it possible to better estimate individualized treatment effects.\nUsing Machine Learning itself to automate the design and construction of entire pipelines of Machine Learning algorithms for risk prediction\, screening\, diagnosis\, and prognosis.\n\nBio: Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning\, Artificial Intelligence\, and Medicine at the University of Cambridge\, a Turing Faculty Fellow at The Alan Turing Institute in London\, where she leads the effort on data science and machine learning for personalized medicine. Prior to this\, she was a Chancellor’s Professor at UCLA and MAN Professor of Quantitative Finance at the University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018).  She has also been the recipient of an NSF Career Award\, 3 IBM Faculty Awards\, the IBM Exploratory Stream Analytics Innovation Award\, the Philips Make a Difference Award and several best paper awards\, including the IEEE Darlington Award. She holds 35 granted USA patents. Her current research focus is on data science\, machine learning\, AI and operations research for medicine. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-series-mihaela-van-der-schaar
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190513
DTEND;VALUE=DATE:20190514
DTSTAMP:20260412T054136
CREATED:20190328T163321Z
LAST-MODIFIED:20190328T163321Z
UID:9178-1557705600-1557791999@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data-Driven Decisions
DESCRIPTION:Developed by 11 MIT faculty members at IDSS\, this seven-week course is specially designed for data scientists\, business analysts\, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins May\, 13\, 2019.
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+2T2019/about?utm_medium=website&#038;utm_source=idss&#038;utm_campaign=ds-su19&#038;utm_content=event-calendar
CATEGORIES:Online events
END:VEVENT
END:VCALENDAR