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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191120T160000
DTEND;TZID=America/New_York:20191120T170000
DTSTAMP:20260518T092934
CREATED:20191115T211228Z
LAST-MODIFIED:20191115T211228Z
UID:11230-1574265600-1574269200@idss-stage.mit.edu
SUMMARY:A Causal Exposure Response Function with Local Adjustment for Confounding: A study of the health effects of long-term exposure to low levels of fine particulate matter
DESCRIPTION:Abstract:   \nIn the last two decades\, ambient levels of air pollution have declined substantially. Yet\, as mandated by the Clean Air Act\, we must continue to address the following question: is exposure to levels of air pollution that are well below the National Ambient Air Quality Standards (NAAQS) harmful to human health? Furthermore\, the highly contentious nature surrounding environmental regulations necessitates casting this question within a causal inference framework. Several parametric and semi-parametric regression modeling approaches have been used to estimate the exposure-response (ER) curve relating long-term exposure to air pollution and various health outcomes. However\, most of these approaches are not formulated in the context of a potential outcome framework for causal inference\, adjust for the same set of potential confounders across all levels of exposure\, and do not account for model uncertainty regarding covariate selection and the shape of the ER. In this paper\, we introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment). LERCA allows for: a) different confounders and different strength of confounding at the different exposure levels; and b) model uncertainty regarding confounders’ selection and the shape of the ER. Also\, LERCA provides a principled way of assessing the observed covariates’ confounding importance at different exposure levels\, providing environmental researchers with important information regarding the set of variables to measure and adjust for in regression models. Using simulation studies\, we show that state of the art approaches perform poorly in estimating the ER curve in the presence of local confounding. Lastly\, LERCA is used on a large data set which includes health\, weather\, demographic\, and pollution information for 5\,362 zip codes and for the years of 2011-2013. \nBiography: \nDr. Francesca Dominici is Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Co-Director of the Data Science Initiative at Harvard University. She was recruited to the Harvard Chan School as a tenured Professor of Biostatistics in 2009. She was appointed Associate Dean of Information Technology in 2011 and Senior Associate Dean for Research in 2013. \n—- \nFor more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \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. And the meetings are open to any interested researcher.   Talks will be followed by 30 minutes of tea/snacks and informal discussion.** \n 
URL:https://stat.mit.edu/calendar/a-causal-exposure-response-function-with-local-adjustment-for-confounding-a-study-of-the-health-effects-of-long-term-exposure-to-low-levels-of-fine-particulate-matter/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191119T160000
DTEND;TZID=America/New_York:20191119T170000
DTSTAMP:20260518T092934
CREATED:20191030T154157Z
LAST-MODIFIED:20191030T154157Z
UID:11098-1574179200-1574182800@idss-stage.mit.edu
SUMMARY:Stability of a Fluid Model for Fair Bandwidth Sharing with General File Size Distributions
DESCRIPTION:Abstract: \nMassoulie and Roberts introduced a stochastic model for a data communication network where file sizes are generally distributed and the network operates under a fair bandwidth sharing policy.  It has been a standing problem to prove stability of this general model when the average load on the system is less than the network’s capacity. A crucial step in an approach to this problem is to prove stability of an associated measure-valued fluid model. We shall describe prior work on this question done under various strong assumptions and indicate how to prove stability of the fluid model under mild conditions. \nThis talk is based on joint work with Yingjia Fu. \nBiography: \nRuth Williams holds the Charles Lee Powell Chair in Mathematics I at the University of California\, San Diego (UCSD). She is a mathematician who works in probability theory\, especially on stochastic processes and their applications. She is particularly known for her foundational work on reflecting diffusion processes in domains with corners\, for co-development with Maury Bramson of a systematic approach to proving heavy traffic limit theorems for multiclass queueing networks\, and for the development of fluid and diffusion approximations for the analysis and control of more general stochastic networks\, including those described by measure-valued processes. Her current research includes the study of stochastic models of complex networks\, for example\, those arising in Internet congestion control and systems biology. \nWilliams studied mathematics at the University of Melbourne where she earned her Bachelor of Science (Honours) and Master of Science degrees. She then studied at Stanford University where she earned her Ph.D. degree in Mathematics. She had a postdoc at the Courant Institute of Mathematical Sciences in New York before taking up a position as an Assistant Professor at the University of California\, San Diego (UCSD). She has remained at UCSD during her career\, where she is now a Distinguished Professor of Mathematics.
URL:https://stat.mit.edu/calendar/stability-of-a-fluid-model-for-fair-bandwidth-sharing-with-general-file-size-distributions/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191118T160000
DTEND;TZID=America/New_York:20191118T170000
DTSTAMP:20260518T092934
CREATED:20190920T151029Z
LAST-MODIFIED:20190920T151029Z
UID:10849-1574092800-1574096400@idss-stage.mit.edu
SUMMARY:LIDS Seminar - Sujay Sanghavi (University of Texas at Austin)
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-sujay-sanghavi-university-texas-austin
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191115T110000
DTEND;TZID=America/New_York:20191115T120000
DTSTAMP:20260518T092934
CREATED:20191017T134056Z
LAST-MODIFIED:20191108T190943Z
UID:10984-1573815600-1573819200@idss-stage.mit.edu
SUMMARY:Understanding machine learning with statistical physics
DESCRIPTION:Abstract: \nThe affinity between statistical physics and machine learning has long history\, this is reflected even in the machine learning terminology that is in part adopted from physics. Current theoretical challenges and open questions about deep learning and statistical learning call for unified account of the following three ingredients: (a) the dynamics of the learning algorithm\, (b) the architecture of the neural networks\, and (c) the structure of the data. Most existing theories are not taking in account all of those three aspects in a satisfactory manner. In this talk I will describe some of the results stemming from statistical physics applied to machine learning and how it does include the three ingredients\, although in a very simplified manner. Then I will focus on the current results improving our modelling in each of the three aspects covering recent articles [1-4]. \n[1] Aubin\, B.\, Maillard\, A.\, Krzakala\, F.\, Macris\, N.\, & Zdeborová\, L.; The committee machine: Computational to statistical gaps in learning a two-layers neural network. NeurIPS’18.\n[2] Sarao Mannelli\, S.\, Biroli\, G.\, Cammarota\, C.\, Krzakala\, F.\, & Zdeborová\, L.; Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model. NeurIPS’19.\n[3] Aubin\, B.\, Loureiro\, B.\, Maillard\, A.\, Krzakala\, F.\, & Zdeborová\, L.; The spiked matrix model with generative priors. NeurIPS’19.\n[4] Goldt\, S.\, Mézard\, M.\, Krzakala\, F.\, & Zdeborová\, L.; Modelling the influence of data structure on learning in neural networks. Preprint arXiv:1909.11500. \nBiography: \nLenka Zdeborová is a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay\, France. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director’s Postdoctoral Fellow. In 2014\, she was awarded the CNRS bronze medal\, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant\, in 2018 the Irène Joliot-Curie prize. She is editorial board member for Journal of Physics A\, Physical review E and Physical Review X.  Lenka’s expertise is in applications of methods developed in statistical physics\, such as advanced mean field methods\, replica method and related message passing algorithms\, to problems in machine learning\, signal processing\, inference and optimization.
URL:https://stat.mit.edu/calendar/zdeborova/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191113T160000
DTEND;TZID=America/New_York:20191113T170000
DTSTAMP:20260518T092934
CREATED:20191107T164432Z
LAST-MODIFIED:20191107T170724Z
UID:11159-1573660800-1573664400@idss-stage.mit.edu
SUMMARY:Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox
DESCRIPTION:Abstract: \nAdvances in Markov chain Monte Carlo in the past 30 years have made Bayesian analysis a routine practice. However\, there is virtually no practice of performing Monte Carlo integration from the Bayesian perspective; indeed\,this problem has earned the “paradox” label in the context of computing normalizing constants (Wasserman\, 2013). We first use the modeling-what-we-ignore idea of Kong et al. (2003) to explain that the crux of the paradox is not with the likelihood theory\, which is essentially the same as for a standard non-parametric probability/density estimation (Vardi\, 1985); though via using group theory\, it provides a richer framework for modeling the trade-off between statistical efficiency and computational efficiency. But there is a real Bayesian paradox: Bayesian analysis cannot be applied exactly for solving Bayesian computation\, because to perform the exact Bayesian Monte Carlo integration would require more computation than needed to solve the original Monte Carlo problem. We then show that there is a practical resolution to this paradox using the profile likelihood obtained in Kong et al. (2006) and that this approximation is second-order valid asymptotically. We also investigate a more computationally efficient approximation via an artificial likelihood of Geyer (1994). This artificial likelihood approach is only first-order valid\, but there is a computationally trivial adjustment to render its second-order validity. We demonstrate empirically the efficiency of these approximated Bayesian estimators\, compared to the usual frequentist-based Monte Carlo estimators\, such as bridge sampling estimators (Meng and Wong\, 1996). \n[This is a joint work with Masatoshi Uehara.]\nReferences: \nWasserman\, L. (2013) All of Statistics: A Concise Course in Statistical Inference.  Springer Science & Business Media. Also see https://normaldeviate.wordpress.com/2012/10/05/the-normalizing-constant-paradox/ \nKong\, A.\,P. McCullagh\, X.-L. Meng\, D. Nicolae\, and Z. Tan (2003). A theory of statistical models for Monte Carlo integration (with Discussions). J. R. Statist. Soc. B 65\, 585-604. \nhttp://stat.harvard.edu/XLM/JRoyStatSoc/JRoyStatSocB65-3_585-618_2003.pdf \nVardi\, Y. (1985). Empirical distributions in selection bias models. Ann. Statist. 13 (1)\, 178-203.  https://projecteuclid.org/download/pdf_1/euclid.aos/1176346585 \nKong\, A.\, P. McCullagh\, X.-L. Meng\, and D. Nicolae (2006). Further explorations of likelihood theory for Monte Carlo integration. In Advances in Statistical Modeling and Inference: Essays in Honor of Kjell A. Doksum (Ed: V. Nair)\, 563-592. World Scientific Press.  http://www.stat.harvard.edu/XLM/books/kmmn.pdf \nGeyer\, C. J. (1994). Estimating normalizing constants and reweighting mixtures in Markov chain Monte Carlo.Technical Report\, School of Statistics\,University of Minnesota\, Minneapolis 568. \nhttps://scholar.google.com/scholar?cluster=6307665497304333587&hl=en&as_sdt=0\,22 \nMeng\, X.-L. and Wong\, W.H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistics Sinica6\, 831-860. http://stat.harvard.edu/XLM/StatSin/StatSin6-4_831-860_1996.pdf \n\nBiography: \nXiao-Li Meng\, the Whipple V. N. Jones Professor of Statistics\, and the Founding Editor-in-Chief of Harvard Data Science Review\, is well known for his depth and breadth in research\, his innovation and passion in pedagogy\, his vision and effectiveness in administration\, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001\, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas\, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics\, and he is the author of “The XL-Files\,” a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g.\, the interplay among Bayesian\, Fiducial\, and frequentist perspectives; frameworks for multi-source\, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g.\, posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural\, social\, and medical sciences and engineering (e.g.\, complex statistical modeling in astronomy and astrophysics\, assessing disparity in mental health services\, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard\, where he served as the Chair of the Department of Statistics (2004-2012) and the Dean of Graduate School of Arts and Sciences (2012-2017). \n\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/artificial-bayesian-monte-carlo-integration-a-practical-resolution-to-the-bayesian-normalizing-constant-paradox/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191112T160000
DTEND;TZID=America/New_York:20191112T173000
DTSTAMP:20260518T092934
CREATED:20190802T192236Z
LAST-MODIFIED:20190802T192751Z
UID:10468-1573574400-1573579800@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Info Session
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Info session is hosted by a member of the IDSS faculty and an SES student who introduce the program and answer your questions.\nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-info-session-2/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191108T110000
DTEND;TZID=America/New_York:20191108T120000
DTSTAMP:20260518T092934
CREATED:20191017T133140Z
LAST-MODIFIED:20191104T140845Z
UID:10982-1573210800-1573214400@idss-stage.mit.edu
SUMMARY:SDP Relaxation for Learning Discrete Structures: Optimal Rates\, Hidden Integrality\, and Semirandom Robustness
DESCRIPTION:Abstract:\n\nWe consider the problems of learning discrete structures from network data under statistical settings. Popular examples include various block models\, Z2 synchronization and mixture models. Semidefinite programming (SDP) relaxation has emerged as a versatile and robust approach to these problems. We show that despite being a relaxation\, SDP achieves the optimal Bayes error rate in terms of distance to the target solution. Moreover\, SDP relaxation is provably robust under the so-called semirandom model\, which frustrates many existing algorithms. Our proof involves a novel primal-dual analysis that establishes what we call the hidden integrality property: the SDP relaxation tightly approximates the optimal (yet unimplementable) integer programs with oracle information.\n\nJoint work with Yingjie Fei (Cornell Ph.D.)\, who won 2nd place in INFORMS Nicholson Student Paper Competition.\n\nBio: Yudong Chen is an assistant professor at the School of Operations Research and Information Engineering (ORIE)\, Cornell University. Before joining Cornell\, he was a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences at University of California\, Berkeley. He obtained his Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin\, and his M.S. and B.S. from Tsinghua University. His research interests include machine learning\, high-dimensional and robust statistics\, convex and non-convex optimization\, and applications in communication and computer networks.
URL:https://stat.mit.edu/calendar/chen/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191106T160000
DTEND;TZID=America/New_York:20191106T170000
DTSTAMP:20260518T092934
CREATED:20191031T161910Z
LAST-MODIFIED:20191031T161910Z
UID:11125-1573056000-1573059600@idss-stage.mit.edu
SUMMARY:Probabilistic Inference and Learning with Stein’s Method
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: \nLester Mackey (Microsoft Research) \nAbstract: \n\nStein’s method is a powerful tool from probability theory for bounding the distance between probability distributions.  In this talk\, I’ll describe how this tool designed to prove central limit theorems can be adapted to assess and improve the quality of practical inference procedures.  I’ll highlight applications to Markov chain sampler selection\, goodness-of-fit testing\, variational inference\, and nonconvex optimization and close with several opportunities for future work.\n\n\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/probabilistic-inference-and-learning-with-steins-method/
LOCATION:37-212
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191106T160000
DTEND;TZID=America/New_York:20191106T170000
DTSTAMP:20260518T092934
CREATED:20191028T131513Z
LAST-MODIFIED:20191028T132229Z
UID:11074-1573056000-1573059600@idss-stage.mit.edu
SUMMARY:One-shot Information Theory via Poisson Processes
DESCRIPTION:Abstract: \nIn information theory\, coding theorems are usually proved in the asymptotic regime where the blocklength tends to infinity. While there are techniques for finite blocklength analysis\, they are often more complex than their asymptotic counterparts. In this talk\, we study the use of Poisson processes in proving coding theorems\, which not only gives sharp one-shot and finite blocklength results\, but also gives significantly shorter proofs than conventional asymptotic techniques in some settings. Instead of using fixed-size random codebooks\, we construct the codebook as a Poisson process. We present a lemma\, called the Poisson matching lemma\, which can replace both packing and covering lemmas in proofs based on typicality. We then demonstrate its use in settings such as channel coding with channel state information at the encoder\, lossy source coding with side information at the decoder\, joint source-channel coding\, broadcast channels\, and distributed lossy source coding. This shows that the Poisson matching lemma is a viable alternative to typicality for most problems in network information theory. \nThe talk is based on a joint work with Prof. Venkat Anantharam (UC Berkeley). \nBio: \nCheuk Ting Li received the B.Sc. degree in mathematics and B.Eng. degree in information engineering from The Chinese University of Hong Kong in 2012\, and the M.S. and Ph.D. degree in electrical engineering from Stanford University in 2014 and 2018 respectively. He was awarded the 2016 IEEE Jack Keil Wolf ISIT Student Paper Award. He is currently a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences\, University of California\, Berkeley. His research interests include finite blocklength schemes in information theory\, generation of random variables\, and information-theoretic secrecy. \n 
URL:https://idss-stage.mit.edu/calendar/one-shot-information-theory-via-poisson-processes/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191104T160000
DTEND;TZID=America/New_York:20191104T170000
DTSTAMP:20260518T092934
CREATED:20190722T171135Z
LAST-MODIFIED:20191105T195746Z
UID:10365-1572883200-1572886800@idss-stage.mit.edu
SUMMARY:Causal Inference in the Age of Big Data
DESCRIPTION:The rise of massive data sets that provide fine-grained information about human beings and their behavior offers unprecedented opportunities for evaluating the effectiveness of social\, behavioral\, and medical treatments. With the availability of fine-grained data\, researchers and policymakers are increasingly unsatisfied with estimates of average treatment effects based on experimental samples that are unrepresentative of populations of interest. Instead\, they seek to target treatments to particular populations and subgroups. Because of these inferential challenges\, Machine Learning (ML) is now being used for evaluating and predicting the effectiveness of interventions in a wide range of domains from technology firms to clinical medicine and election campaigns. However\, there are a number of issues that arise with the use of ML for causal inference. For example\, although ML and related statistical models are good for prediction\, they are not designed to estimate causal effects. Instead\, they focus on predicting observed outcomes. In this talk\, a number of meta-algorithms are presented that can take advantage of any supervised learning method to estimate the Conditional Average Treatment Effect function. Also\, discussed are new theoretical results on confidence intervals and overlap in high-dimensional covariates and a new algorithm for optimal linear aggregation functions for tree-based estimators. \nAbout the speaker: Jasjeet Sekhon is the Robson Professor of Political Science and Statistics at the University of California\, Berkeley. His current research focuses on creating new machine learning methods for estimating causal effects in observational and experimental studies and evaluating social science\, digital\, public health\, and medical interventions. He is also the Head of Causal Inference at Bridgewater Associates. \nReception to follow.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-with-jasjeet-sekhon-uc-berkeley/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191104T100000
DTEND;TZID=America/New_York:20191104T110000
DTSTAMP:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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:20260518T092934
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
END:VCALENDAR