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DTSTART;TZID=America/New_York:20190412T110000
DTEND;TZID=America/New_York:20190412T120000
DTSTAMP:20260408T094529
CREATED:20190204T202500Z
LAST-MODIFIED:20190206T173126Z
UID:8820-1555066800-1555070400@idss-stage.mit.edu
SUMMARY:Exponential line-crossing inequalities
DESCRIPTION:Abstract: \nThis talk will present a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful to formulate exponential concentration inequalities in this way. We will illustrate this point by presenting a single assumption and a single theorem that together strengthen many tail bounds for martingales\, including classical inequalities (1960-80) by Bernstein\, Bennett\, Hoeffding\, and Freedman; contemporary inequalities (1980-2000) by Shorack and Wellner\, Pinelis\, Blackwell\, van de Geer\, and de la Pena; and several modern inequalities (post-2000) by Khan\, Tropp\, Bercu and Touati\, Delyon\, and others. In each of these cases\, we give the strongest and most general statements to date\, quantifying the time-uniform concentration of scalar\, matrix\, and Banach-space-valued martingales\, under a variety of nonparametric assumptions in discrete and continuous time. In doing so\, we bridge the gap between existing line-crossing inequalities\, the sequential probability ratio test\, the Cramer-Chernoff method\, self-normalized processes\, and other parts of the literature. Time permitting\, I will briefly discuss applications to sequential covariance matrix estimation\, adaptive clinical trials and multi-armed bandits via the notion of “confidence sequences”. \n(joint work with Steve Howard\, Jas Sekhon and Jon McAuliffe\, preprint https://arxiv.org/abs/1808.03204) \n Biography: \nAaditya Ramdas is an assistant professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University. Previously\, he was a postdoctoral researcher in Statistics and EECS at UC Berkeley from 2015-18\, mentored by Michael Jordan and Martin Wainwright. He finished his PhD at CMU in Statistics and Machine Learning\, advised by Larry Wasserman and Aarti Singh\, winning the Best Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay. A lot of his research focuses on modern aspects of reproducibility in science and technology — involving statistical testing and false discovery rate control in static and dynamic settings. He also works on some problems in sequential decision-making and online uncertainty quantification \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/tbd-aadityaramdas/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190409T160000
DTEND;TZID=America/New_York:20190409T170000
DTSTAMP:20260408T094529
CREATED:20190301T171340Z
LAST-MODIFIED:20190501T142316Z
UID:8985-1554825600-1554829200@idss-stage.mit.edu
SUMMARY:Personalized Dynamic Pricing with Machine Learning: High Dimensional Covariates and Heterogeneous Elasticity
DESCRIPTION:We consider a seller who can dynamically adjust the price of a product at the individual customer level\, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand model\, parameters of which depend on $s$ out of the $d$ features. The seller initially does not know the relationship between the customer features and the product demand\, but learns this through sales observations over a selling horizon of $T$ periods. We prove that the seller’s expected regret\, i.e.\, the revenue loss against a clairvoyant who knows the underlying demand relationship\, is at least of order $s\sqrt{T}$ under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order $s\sqrt{T}log(T)$. We extend this policy to a more realistic setting where the seller does not know the true demand predictors\, and show this policy has an expected regret of order $s\sqrt{T}(log(d)＋log(T))$\, which is also near-optimal. Finally\, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets\, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then-optimize policies. Furthermore\, our policy significantly improves upon the loan company’s historical pricing decisions in terms of annual expected revenue. \nBio: Gah-Yi Ban is an Assistant Professor of Management Science and Operations at London Business School. Gah-Yi’s research is in Big Data analytics\, specifically decision-making with complex\, high-dimensional and/or highly uncertain data with applications to operations management and finance. Gah-Yi’s research has appeared on most-downloaded lists of Management Science and Operations Research\, and awarded Honorable Mention in 2018 INFORMS JFIG Paper Competition. Gah-Yi graduated from UC Berkeley with MSc/MA/PhD in Industrial Engineering/ Statistics/Operations Research. \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://idss-stage.mit.edu/calendar/lids-seminar-gah-yi-ban-london-business-school/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190405T180000
DTEND;TZID=America/New_York:20190407T170000
DTSTAMP:20260408T094529
CREATED:20190313T133551Z
LAST-MODIFIED:20190313T133551Z
UID:9021-1554487200-1554656400@idss-stage.mit.edu
SUMMARY:MIT Policy Hackathon 2019
DESCRIPTION:
URL:https://www.mitpolicyhackathon.org/
LOCATION:MIT Stata Center\, Cambridge\, MA\, 02139\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190405T090000
DTEND;TZID=America/New_York:20190405T170000
DTSTAMP:20260408T094529
CREATED:20180717T190056Z
LAST-MODIFIED:20181204T161855Z
UID:8034-1554454800-1554483600@idss-stage.mit.edu
SUMMARY:SDSCon2019
DESCRIPTION:SDSCon 2019 is the third annual celebration of the statistics and data science community at MIT and beyond\, organized by MIT’s Statistics and Data Science Center (SDSC).
URL:http://sdsc2019.mit.edu
LOCATION:MIT Media Lab (E14-674)\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190402T160000
DTEND;TZID=America/New_York:20190402T170000
DTSTAMP:20260408T094529
CREATED:20190129T145834Z
LAST-MODIFIED:20190404T145710Z
UID:8793-1554220800-1554224400@idss-stage.mit.edu
SUMMARY:A Particulate Solution: Data Science in the Fight to Stop Air Pollution and Climate Change | IDSS Distinguished Speaker Seminar
DESCRIPTION:Abstract:\nWhat if I told you I had evidence of a serious threat to American national security – a terrorist attack in which a jumbo jet will be hijacked and crashed every 12 days. Thousands will continue to die unless we act now. This is the question before us today – but the threat doesn’t come from terrorists. The threat comes from climate change and air pollution. \nWe have developed an artificial neural network model that uses on-the-ground air-monitoring data and satellite-based measurements to estimate daily pollution levels across the continental U.S.\, breaking the country up into 1-square-kilometer zones. We have paired that information with health data contained in Medicare claims records from the last 12 years\, and for 97% of the population ages 65 or older. We have developed statistical methods and computational efficient algorithms for the analysis over 460 million health records. \nOur research shows that short and long term exposure to air pollution is killing thousands of senior citizens each year. This data science platform is telling us that federal limits on the nation’s most widespread air pollutants are not stringent enough. \nThis type of data is the sign of a new era for the role of data science in public health\, and also for the associated methodological challenges. For example\, with enormous amounts of data\, the threat of unmeasured confounding bias is amplified\, and causality is even harder to assess with observational studies. These and other challenges will be discussed. \nReferences:\nDi Q\, Wang Y\, Zanobetti A\, Wang Y\, Koutrakis P\, Dominici F\, Schwartz J. (2017). Air Pollution and Mortality in the Medicare Population. New England Journal of Medicine\, 376:2513-2522\, June 29\, 2017\, DOI: 10.1056/NEJMoa1702747\nDi Q\, Dai L\, Wang Y\, Zanobetti A\, Dominici F\, Schwartz J. (2017) A Nationwide Case-crossover Study on Air Pollution and Mortality in the United States\, 2000-2012\, Journal of American Medical Association\, AMA. 2017;318(24):2446-2456. doi:10.1001/jama.2017.17923 \nAbout the Speaker:\nFrancesca Dominici is Professor of Biostatistics at the Harvard T.H.Chan School of Public Health and co-Director of the Harvard Data Science Initiative.  \nHer research focuses on the development of statistical methods for the analysis of large and complex data; she leads several interdisciplinary groups of  scientists with the ultimate goal of addressing important questions in environmental health science\, climate change\, comparative effectiveness research  in cancer\, and health policy. Currently\, Dominici’s team uses satellite data and multiple data sources to estimate exposure to air pollution in rural areas in the US\, in India\, and in other developing countries. Her studies have directly and routinely impacted air quality policy and led to more stringent ambient air quality standards in the United States. \n \nDominici was recognized on the Thomson Reuters 2015 Highly Cited Researchers list\, ranking in the top 1 percent of scientists cited in her field. In 2017\, she was named one of the top 10 Italian women scientists with the largest impact in biomedical sciences across the world. In addition to her research interests and administrative leadership roles\, Dominici has demonstrated a career-long commitment to promoting diversity in academia. For her contributions\, she has earned the Jane L. Norwood Award for Outstanding Achievement by a Woman in the Statistical Sciences and the Florence Nightingale David Award. Dominici currently chairs the University Committee for the Advancement of Women Faculty at the Harvard T.H. Chan School of Public Health. Prior to Harvard\, she was on the faculty of the Johns Hopkins Bloomberg School of Public Health\, where she also co-chaired the University Committee on the Status of Women. Dominici has degrees from University La Sapienza and University of Padua. \n  \nPress coverage links\nNPR: http://www.npr.org/sections/health-shots/2017/06/28/534594373/u-s-air-pollution-still-kills-thousands-every-year-study-concludes\nLos Angeles Times: http://www.latimes.com/science/sciencenow/la-sci-sn-air-pollution-death-20170628-story.html\nNew York Times: https://www.nytimes.com/2017/06/28/well/even-safe-pollution-levels-can-be-deadly.html?_r=0\nPodcast: https://www.hsph.harvard.edu/news/multimedia-article/harvard-chan-this-week-in-health-archive/
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-francesca-dominici/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190322T110000
DTEND;TZID=America/New_York:20190322T120000
DTSTAMP:20260408T094529
CREATED:20190204T195726Z
LAST-MODIFIED:20190319T124452Z
UID:8818-1553252400-1553256000@idss-stage.mit.edu
SUMMARY:Optimization of random polynomials on the sphere in the full-RSB regime
DESCRIPTION:Abstract: \nThe talk will focus on optimization on the high-dimensional sphere when the objective function is a linear combination of homogeneous polynomials with standard Gaussian coefficients. Such random processes are called spherical spin glasses in physics\, and have been extensively studied since the 80s. I will describe certain geometric properties of spherical spin glasses unique to the full-RSB case\, and explain how they can be used to design a polynomial time algorithm that finds points within small multiplicative error from the global minimum. \nBiography: \nEliran Subag is a Junior Fellow in the Simons Society of Fellows\, at the Courant Institute\, NYU.\nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/tbd-eliransubag/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190315T110000
DTEND;TZID=America/New_York:20190315T120000
DTSTAMP:20260408T094529
CREATED:20190219T155710Z
LAST-MODIFIED:20190219T163823Z
UID:8905-1552647600-1552651200@idss-stage.mit.edu
SUMMARY:Subvector Inference in Partially Identified Models with Many Moment Inequalities
DESCRIPTION:Abstract: \nIn this work we consider bootstrap-based inference methods for functions of the parameter vector in the presence of many moment inequalities where the number of moment inequalities\, denoted by p\, is possibly much larger than the sample size n. In particular this covers the case of subvector inference\, such as the inference on a single component associated with a treatment/policy variable of interest. We consider a min-max of (centered and non-centered) Studentized statistics and study the properties of the associated critical values. In order to establish that we provide a new finite sample analysis that does not rely on Donsker’s properties and establish new central limit theorems for the min-max of the components of random matrices. Furthermore\, we consider the anti-concentration properties of the min-max of the components of a Gaussian matrix and propose bootstrap based methods to estimate them. In turn this provides a valid data-driven to set the tuning parameters of the bootstrap-based inference methods. Importantly\, the tuning parameters generalize choices of literature for Donsker’s classes (and showing why those would not be appropriate in our setting) which might better characterize finite sample behavior. This is co-authored with Federico Bugni and Victor Chernozhukov. \nLink to paper: https://arxiv.org/abs/1806.11466 \nBiography: \nAlexandre Belloni is a Professor at Duke University. He received his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2006) and a M.Sc. in Mathematical Economics from IMPA (2002). He deferred the offer to join the faculty at Duke University to accept the IBM Herman Goldstein Postdoctoral Fellowship (2006-2007). Professor Belloni’s research interests are on econometrics\, statistics and optimization. He received the 2007 Young Researchers Competition in Continuous Optimization Award. His research papers have appeared in journals such as Econometrica\, Review of Economic Studies\, Annals of Statistics\, Marketing Science\, Management Science and Operations Research. He serves as associate editor for different journals and is currently the Area Editor for Machine Learning and Data Science at Operations Research.
URL:https://stat.mit.edu/calendar/tbd-alexbelloni/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190312T160000
DTEND;TZID=America/New_York:20190312T170000
DTSTAMP:20260408T094529
CREATED:20190301T170833Z
LAST-MODIFIED:20190501T142423Z
UID:8981-1552406400-1552410000@idss-stage.mit.edu
SUMMARY:Automatic Computation of Exact Worst-Case Performance for First-Order Methods
DESCRIPTION:Joint work with Adrien Taylor (INRIA) and Francois Glineur (UCLouvain). \nWe show that the exact worst-case performances of a wide class of first-order convex optimization algorithms can be obtained as solutions to semi-definite programs\, which provide both the performance bounds and functions on which these are reached.  Our formulation is based on a necessary and sufficient condition for smooth (strongly) convex interpolation\, allowing for a finite representation for smooth (strongly) convex functions in this context. These results allow improving the performance bounds of many classical algorithms\, and better understanding their dependence on the algorithm’s parameters\, leading to new optimized parameters\, and thus stronger performances. \nOur approach can be applied via the PESTO Toolbox\, which let the user describe algorithms in a natural way. \nBio: Julien M. Hendrickx is professor of mathematical engineering at Université catholique de Louvain\, in the Ecole Polytechnique de Louvain since 2010. He is on sabbatical at Boston University in 2018-19\, holding a WBI-World excellence fellowship. \nHe obtained an engineering degree in applied mathematics (2004) and a PhD in mathematical engineering (2008) from the same university. He has been a visiting researcher at the University of Illinois at Urbana Champaign in 2003-2004\, at the National ICT Australia in 2005 and 2006\, and at the Massachusetts Institute of Technology in 2006 and 2008. He was a postdoctoral fellow at the Laboratory for Information and Decision Systems of the Massachusetts Institute of Technology 2009 and 2010\, holding postdoctoral fellowships of the F.R.S.-FNRS (Fund for Scientific Research) and of Belgian American Education Foundation. \nDoctor Hendrickx is the recipient of the 2008 EECI award for the best PhD thesis in Europe in the field of Embedded and Networked Control\, and of the Alcatel-Lucent-Bell 2009 award for a PhD thesis on original new concepts or application in the domain of information or communication technologies. \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://idss-stage.mit.edu/calendar/automatic-computation-of-exact-worst-case-performance-for-first-order-methods/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190311T160000
DTEND;TZID=America/New_York:20190311T170000
DTSTAMP:20260408T094529
CREATED:20190308T162816Z
LAST-MODIFIED:20190308T163024Z
UID:9013-1552320000-1552323600@idss-stage.mit.edu
SUMMARY:Using Computer Vision to Study Society:  Methods and Challenges
DESCRIPTION:  \nAbstract: \nTargeted socio-economic policies require an accurate understanding of a country’s demographic makeup. To that end\, the United States spends more than 1 billion dollars a year gathering census data such as race\, gender\, education\, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive\, data-driven\, machine learning driven approaches are cheaper and faster–with the potential ability to detect trends in close to real time. In this work\, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income\, per capita carbon emission\, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to determine demographic attributes using the detect cars. To facilitate our work\, we used a graph based algorithm to collect a challenging fine-grained dataset consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources. Our prediction results correlate well with ground truth income (r=0.82)\, race\, education\, voting\, sources investigating crime rates\, income segregation\, per capita carbon emission\, and other market research. Data mining based works such as this one can be used for many types of applications–some ethical and others not. I will finally discuss work (inspired by my experiences while working on this project)\, on auditing and exposing biases found in computer vision systems. Using recent work on exposing the gender and skin type bias found in commercial gender classification systems as a case study\, I will discuss how the lack of standardization and documentation in AI is leading to biased systems used in high stakes scenarios. I will end with the concept of AI datasheets for datasets\, and model cards for model reporting to standardize information for datasets and pre-trained models\, to push the field as a whole towards transparency and accountability. Host: Antonio Torralba. \n Bio: \nTimnit Gebru is a research scientist in the Ethical AI team at Google and just finished her postdoc in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research\, New York. Prior to that\, she was a PhD student in the Stanford Artificial Intelligence Laboratory\, studying computer vision under Fei-Fei Li. Her main research interest is in data mining large-scale\, publicly available images to gain sociological insight\, and working on computer vision problems that arise as a result\, including fine-grained image recognition\, scalable annotation of images\, and domain adaptation. She is currently studying the ethical considerations underlying any data mining project\, and methods of auditing and mitigating bias in sociotechnical systems. The New York Times\, MIT Tech Review and others have recently covered her work. As a cofounder of the group Black in AI\, she works to both increase diversity in the field and reduce the negative impacts of racial bias in training data used for human-centric machine learning models.
URL:https://idss-stage.mit.edu/calendar/using-computer-vision-to-study-society-methods-and-challenges/
LOCATION:32-G449 (KIva/Patel)
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20260408T094529
CREATED:20190314T175210Z
LAST-MODIFIED:20190314T175210Z
UID:9024-1552042800-1552046400@idss-stage.mit.edu
SUMMARY:Univariate total variation denoising\, trend filtering and multivariate Hardy-Krause variation denoising
DESCRIPTION:Abstract: \nTotal variation denoising (TVD) is a popular technique for nonparametric function estimation. I will first present a theoretical optimality result for univariate TVD for estimating piecewise constant functions. I will then present related results for various extensions of univariate TVD including adaptive risk bounds for higher-order TVD (also known as trend filtering) as well as a multivariate extension via the Hardy-Krause Variation which avoids the curse of dimensionality to some extent. I will also mention connections to shape restricted function estimation. The results are based on joint work with Sabyasachi Chatterjee\, Billy Fang\, Donovan Lieu and Bodhisattva Sen. \n Biography: \nAditya Guntuboyina is currently an Associate Professor at the Department of Statistics\, UC Berkeley. He has been at Berkeley since 2012 after finishing his PhD in Statistics from Yale University and a postdoctoral position at the Wharton Statistics Department in the University of Pennsylvania. His research interests include nonparametric and high-dimensional statistics\, shape constrained statistical estimation\, empirical processes and statistical information theory. His research is currently supported by an NSF CAREER award. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/univariate-total-variation-denoising-trend-filtering-multivariate-hardy-krause-variation-denoising-adityaguntuboyina/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20260408T094529
CREATED:20190204T181505Z
LAST-MODIFIED:20190204T181505Z
UID:8816-1552042800-1552046400@idss-stage.mit.edu
SUMMARY:Univariate total variation denoising\, trend filtering and multivariate Hardy-Krause variation denoising
DESCRIPTION:Abstract: \nTotal variation denoising (TVD) is a popular technique for nonparametric function estimation. I will first present a theoretical optimality result for univariate TVD for estimating piecewise constant functions. I will then present related results for various extensions of univariate TVD including adaptive risk bounds for higher-order TVD (also known as trend filtering) as well as a multivariate extension via the Hardy-Krause Variation which avoids the curse of dimensionality to some extent. I will also mention connections to shape restricted function estimation. The results are based on joint work with Sabyasachi Chatterjee\, Billy Fang\, Donovan Lieu and Bodhisattva Sen.  \n Biography: \nAditya Guntuboyina is currently an Associate Professor at the Department of Statistics\, UC Berkeley. He has been at Berkeley since 2012 after finishing his PhD in Statistics from Yale University and a postdoctoral position at the Wharton Statistics Department in the University of Pennsylvania. His research interests include nonparametric and high-dimensional statistics\, shape constrained statistical estimation\, empirical processes and statistical information theory. His research is currently supported by an NSF CAREER award.
URL:https://stat.mit.edu/calendar/univariate-total-variation-denoising-trend-filtering-multivariate-hardy-krause-variation-denoising-adityaguntuboyina/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190305T160000
DTEND;TZID=America/New_York:20190305T170000
DTSTAMP:20260408T094529
CREATED:20190129T144952Z
LAST-MODIFIED:20190312T132639Z
UID:8791-1551801600-1551805200@idss-stage.mit.edu
SUMMARY:A Theory for Representation Learning via Contrastive Objectives
DESCRIPTION:Abstract:\nRepresentation learning seeks to represent complicated data (images\, text etc.) using a vector embedding\, which can then be used to solve complicated new classification tasks using simple methods like a linear classifier. Learning such embeddings is an important type of unsupervised learning (learning from unlabeled data) today. Several recent methods leverage pairs of “semantically similar” data points (eg sentences occuring next to each other in a text corpus). We call such methods contrastive learning (another term would be “like word2vec”) and propose a theoretical framework for analysing them. The challenge for theory here is that the training objective seems to have little to do with the downstream task. Our framework bridges this challenge and can show provable guarantees on the performance of the learnt representation on downstream classification tasks. I’ll show experiments supporting the theory.\nThe talk will be self-contained.\n(Joint work with Hrishikesh Khandeparkar\, Mikhail Khodak\, Orestis Plevrakis\, and Nikunj Saunshi.) \nAbout the Speaker:\nSanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University and Visiting Professor in Mathematics at the Institute for Advanced Study. He works on theoretical computer science and theoretical machine learning. He has received the Packard Fellowship (1997)\, Simons Investigator Award (2012)\, Gödel Prize (2001 and 2010)\, ACM Prize in Computing (formerly the ACM-Infosys Foundation Award in the Computing Sciences) (2012)\, and the Fulkerson Prize in Discrete Math (2012). He is a fellow of the American Academy of Arts and Sciences and member of the National Academy of Science.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-march/
LOCATION:32-155\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190304T080000
DTEND;TZID=America/New_York:20190304T170000
DTSTAMP:20260408T094529
CREATED:20180717T190526Z
LAST-MODIFIED:20191230T170939Z
UID:8040-1551686400-1551718800@idss-stage.mit.edu
SUMMARY:Women in Data Science (WiDS) – Cambridge\, MA
DESCRIPTION:This one-day technical conference brings together local academic leaders\,  industrial professionals and students to hear about the latest data science-related research in a number of domains\, to learn how leading-edge companies are leveraging data science for success\, and to connect with potential mentors\, collaborators\, and others in the field. \nWatch WiDS Cambridge on YouTube.
URL:https://idss-stage.mit.edu/calendar/women-in-data-science-wids-cambridge-ma-2/
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190301T110000
DTEND;TZID=America/New_York:20190301T120000
DTSTAMP:20260408T094529
CREATED:20190204T180630Z
LAST-MODIFIED:20190204T181211Z
UID:8814-1551438000-1551441600@idss-stage.mit.edu
SUMMARY:Why Aren’t Network Statistics Accompanied By Uncertainty Statements?
DESCRIPTION:Abstract: \nOver 500K scientific articles have been published since 1999 with the word “network” in the title. And the vast majority of these report network summary statistics of one type or another.  However\, these numbers are rarely accompanied by any quantification of uncertainty. Yet any error inherent in the measurements underlying the construction of the network\, or in the network construction procedure itself\, necessarily must propagate to any summary statistics reported. Perhaps surprisingly\, there is little in the way of formal statistical methodology for this problem.  I summarize results from our recent work\, for the case of estimating the density of low-order subgraphs. Under a simple model of network error\, we show that consistent estimation of such densities is impossible when the rates of error are unknown and only a single network is observed. We then develop method-of-moment estimators of subgraph density and error rates for the case where a minimal number of network replicates are available (i.e.\, just 2 or 3). These estimators are shown to be asymptotically normal as the number of vertices increases to infinity. We also provide confidence intervals for quantifying the uncertainty in these estimates\, implemented through a novel bootstrap algorithm. We illustrate the use of our estimators in the context of gene coexpression networks — the correction for measurement error is found to have potentially substantial impact on standard summary statistics.  This is joint work with Qiwei Yao and Jinyuan Chang. \n Biography: \nEric Kolaczyk is a Professor of Statistics and Director of the Program in Statistics in the Department of Mathematics & Statistics at Boston University.  He is also a university Data Science Faculty Fellow\, and affiliated with the Division of Systems Engineering and the Programs in Bioinformatics and in Computational Neuroscience.   His current research interests revolve mainly around the statistical analysis of network-indexed data\, including both theory/methods development and collaborative research.  He has published several books on the topic of network analysis.  As an associate editor\, he has served on the boards of JASA and JRSS-B in statistics\, IEEE IP and TNSE in engineering\, and SIMODS in mathematics.  Currently he is the co-chair of the NAS Roundtable on Data Science Education.  He is an elected fellow of the AAAS\, ASA\, and IMS\, an elected senior member of the IEEE\, and an elected member of the ISI.
URL:https://stat.mit.edu/calendar/arent-network-statistics-accompanied-uncertainty-statements-erickolaczyk/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190226T160000
DTEND;TZID=America/New_York:20190226T170000
DTSTAMP:20260408T094529
CREATED:20190301T170447Z
LAST-MODIFIED:20190501T142448Z
UID:8979-1551196800-1551200400@idss-stage.mit.edu
SUMMARY:Coded Computing: A Transformative Framework for Resilient\, Secure\, and Private Distributed Learning
DESCRIPTION:This talk introduces “Coded Computing”\, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing and machine learning\, such as resiliency to stragglers and bandwidth bottleneck. Furthermore\, coded computing can enable (information-theoretically) secure and private learning over untrusted workers that is gaining increasing importance in various application domains. In particular\, we present CodedPrivateML for distributed learning\, which keeps both the data and the model private while allowing efficient parallelization of training across untrusted distributed workers. We demonstrate that CodedPrivateML can provide an order of magnitude speedup (up to ~30x) over the cryptographic approaches that rely on secure multiparty computing. \nBio: Salman Avestimehr is a Professor of Electrical Engineering and co-director of Communication Sciences Institute at the University of Southern California. He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science\, both from the University of California\, Berkeley. Prior to that\, he obtained his B.S. in Electrical Engineering from Sharif University of Technology in 2003.  His research interests include information theory and coding\, distributed computing\, and machine learning. Dr. Avestimehr has received a number of awards\, including a Communications Society and Information Theory Society Joint Paper Award\, the Presidential Early Career Award for Scientists and Engineers (PECASE)\, a Young Investigator Program (YIP) award from the U. S. Air Force Office of Scientific Research\, a National Science Foundation CAREER award\, and several best paper awards. He is currently an Associate Editor for the IEEE Transactions on Information Theory and a General Co-Chair of the 2020 International Symposium on Information Theory (ISIT). \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://idss-stage.mit.edu/calendar/coded-computing-a-transformative-framework-for-resilient-secure-and-private-distributed-learning/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190222T110000
DTEND;TZID=America/New_York:20190222T120000
DTSTAMP:20260408T094529
CREATED:20190204T175935Z
LAST-MODIFIED:20190213T164650Z
UID:8812-1550833200-1550836800@idss-stage.mit.edu
SUMMARY:Capacity lower bound for the Ising perceptron
DESCRIPTION:Abstract: \nThe perceptron is a toy model of a simple neural network that stores a collection of given patterns. Its analysis reduces to a simple problem in high-dimensional geometry\, namely\, understanding the intersection of the cube (or sphere) with a collection of random half-spaces. Despite the simplicity of this model\, its high-dimensional asymptotics are not well understood. I will describe what is known and present recent results. \nThis is joint work with Jian Ding. \n Biography: \nNike Sun is a faculty member in the MIT mathematics department.
URL:https://stat.mit.edu/calendar/capacity-lower-bound-ising-perceptron-nikesun/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190219T160000
DTEND;TZID=America/New_York:20190219T170000
DTSTAMP:20260408T094529
CREATED:20190301T165622Z
LAST-MODIFIED:20190501T142520Z
UID:8976-1550592000-1550595600@idss-stage.mit.edu
SUMMARY:Safeguarding Privacy in Dynamic Decision-Making Problems
DESCRIPTION:The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. The central question is: how can a decision maker take actions that are efficient for her goal\, while simultaneously ensuring these actions do not inadvertently reveal her private information\, even when observed and analyzed by a powerful adversary? We will examine two well-known decision problems (path planning and online learning)\, and in both cases establish sharp\, information-theoretic complexity vs. privacy tradeoff. As a by-product\, our analysis also leads to simple yet provably efficient algorithms for both the decision maker and eavesdropping adversary. Based in part on joint work with John N. Tsitsiklis and Zhi Xu (MIT). \nBio: Kuang Xu was born in Suzhou\, China. He is an Assistant Professor of Operations\, Information and Technology at the Stanford Graduate School of Business\, Stanford University. He received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign\, Urbana\, Illinois\, USA\, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology\, Cambridge\, Massachusetts\, USA. His research interests lie in the fields of applied probability theory\, optimization\, and operations research\, seeking to understand fundamental properties and design principles of large-scale stochastic systems\, with applications in queueing networks\, healthcare\, privacy\, and statistical learning theory. He has received several awards including a First Place in INFORMS George E. Nicholson Student Paper Competition\, a Best Paper Award\, as well as a Kenneth C. Sevcik Outstanding Student Paper Award from ACM SIGMETRICS. \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://idss-stage.mit.edu/calendar/kuang-xu/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190215T110000
DTEND;TZID=America/New_York:20190215T120000
DTSTAMP:20260408T094529
CREATED:20190204T172955Z
LAST-MODIFIED:20190204T173354Z
UID:8809-1550228400-1550232000@idss-stage.mit.edu
SUMMARY:TAP free energy\, spin glasses\, and variational inference
DESCRIPTION:Abstract: \nWe consider the Sherrington-Kirkpatrick model of spin glasses with ferromagnetically biased couplings. For a specific choice of the couplings mean\, the resulting Gibbs measure is equivalent to the Bayesian posterior for a high-dimensional estimation problem known as “Z2 synchronization”. Statistical physics suggests to compute the expectation with respect to this Gibbs measure (the posterior mean in the synchronization problem)\, by minimizing the so-called Thouless-Anderson-Palmer (TAP) free energy\, instead of the mean field (MF) free energy. We prove that this identification is correct\, provided the ferromagnetic bias is larger than a constant (i.e. the noise level is small enough in synchronization). Namely\, we prove that the scaled l_2 distance between any low energy local minimizers of the TAP free energy and the mean of the Gibbs measure vanishes in the large size limit. Our proof technique is based on upper bounding the expected number of critical points of the TAP free energy using the Kac-Rice formula. \nThis is joint work with Song Mei and Andrea Montanari. \n Biography: \nZhou Fan is an Assistant Professor in the Department of Statistics and Data Science at Yale University. His research interests include random matrix theory\, high dimensional and multivariate statistics\, inference in random graphs and networks\, discrete algorithms\, and applications in genetics and computational biology. Zhou received his Ph.D. in Statistics at Stanford University\, working with Iain M. Johnstone and Andrea Montanari. Prior to this\, Zhou developed statistical and software tools for molecular dynamics simulations at D. E. Shaw Research.
URL:https://stat.mit.edu/calendar/tap-free-energy-spin-glasses-variational-inference/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190208T110000
DTEND;TZID=America/New_York:20190208T120000
DTSTAMP:20260408T094529
CREATED:20190128T195456Z
LAST-MODIFIED:20190128T195657Z
UID:8782-1549623600-1549627200@idss-stage.mit.edu
SUMMARY:Medical Image Imputation
DESCRIPTION:Abstract: \nWe present an algorithm for creating high resolution anatomically\nplausible images that are consistent with acquired clinical brain MRI\nscans with large inter-slice spacing. Although large databases of\nclinical images contain a wealth of information\, medical acquisition\nconstraints result in sparse scans that miss much of the\nanatomy. These characteristics often render computational analysis\nimpractical as standard processing algorithms tend to fail when\napplied to such images. Our goal is to enable application of existing\nalgorithms that were originally developed for high resolution research\nscans to severely undersampled images. We illustrate the applications\nof the method in the context of neurodegeneration and white matter\ndisease studies in stroke patients. \nBiography:\nPolina Golland is a Henry Ellis Warren (1894) professor of Electrical\nEngineering and Computer Science at MIT and a principal investigator\nin the MIT Computer Science and Artificial Intelligence Laboratory\n(CSAIL). She received her PhD in 2001 from MIT and her Bachelor and\nMasters degrees in 1993 and 1995 from Technion\, Israel. Polina’s\nprimary research interest is in developing novel techniques for\nmedical image analysis and understanding. With her students\, Polina\nhas demonstrated novel approaches to image segmentation\, shape\nanalysis\, functional image analysis and population studies. She has\nserved as an associate editor of the IEEE Transactions on Medical\nImaging and of the IEEE Transactions on Pattern Analysis. Polina is\ncurrently on the editorial board of the Journal of Medical Image\nAnalysis. She is a Fellow of the International Society for Medical\nImage Computing and Computer Assisted Interventions. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/medical-image-imputation/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190205T160000
DTEND;TZID=America/New_York:20190205T170000
DTSTAMP:20260408T094529
CREATED:20190116T163023Z
LAST-MODIFIED:20190206T203341Z
UID:8749-1549382400-1549386000@idss-stage.mit.edu
SUMMARY:Collective Decision Making: Theory and Experiments
DESCRIPTION:Abstract:\nRanging from jury decisions to political elections\, situations in which groups of individuals determine a collective outcome are ubiquitous. There are two important observations that pertain to almost all collective processes observed in reality. First\, decisions are commonly preceded by some form of communication among individual decision makers\, such as jury deliberations\, or election polls. Second\, even when looking at a particular context\, say U.S. civil jurisdiction\, there is great variance in the type of institutions that are employed to aggregate private information or preferences into group decisions. In this talk\, I will present some theoretical models and experimental results that provide insight into how groups aggregate information and opinions\, and the sorts of instruments that might be beneficial for improving collective outcomes in various settings. \nAbout the speaker:\nLeeat Yariv is the Uwe E. Reinhardt Professor of Economics at Princeton University. She is also the director of the Princeton Experimental Laboratory for the Social Sciences (PExL)\, which provides a platform for experimental research in the social sciences. Yariv’s research combines experimental and empirical evidence together with economic theory to study how individuals connect with one another and how they make decisions together. Her research has touched upon a wide range of topics within the areas of social networks\, political economy\, and market design. Yariv received a B.Sc. in Mathematics\, a B.Sc. in Physics\, and an M.Sc. in Mathematics from Tel-Aviv University. She received an M.A. and a Ph.D. in Economics from Harvard University. Prior to joining Princeton University\, she was a professor at UCLA and Caltech. Yariv is a fellow of the Econometric Society and of the Society for the Advancement of Economic Theory. She has served on multiple journal editorial boards\, including those of Econometrica\, American Economic Review\, and Journal of Economic Literature.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-leeat-yariv/
LOCATION:32-155\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190204
DTEND;VALUE=DATE:20190205
DTSTAMP:20260408T094529
CREATED:20190117T000618Z
LAST-MODIFIED:20190117T155814Z
UID:8734-1549238400-1549324799@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 Feb 4\, 2019.
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+1T2019/about?utm_medium=website&utm_source=idss&utm_campaign=ds-sp19&utm_content=event-calendar
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190201T110000
DTEND;TZID=America/New_York:20190201T120000
DTSTAMP:20260408T094529
CREATED:20190128T171951Z
LAST-MODIFIED:20190128T192955Z
UID:8777-1549018800-1549022400@idss-stage.mit.edu
SUMMARY:Optimization of the Sherrington-Kirkpatrick Hamiltonian
DESCRIPTION:Andrea Montanari\nProfessor\, Department of Electrical Engineering\, Department of Statistics Stanford University \nThis lecture is in conjunction with the LIDS Student Conference. \nAbstract: Let A be n × n symmetric random matrix with independent and identically distributed Gaussian entries above the diagonal. We consider the problem of maximizing xT Ax over binary vectors with ±1 entries. In the language of statistical physics\, this amounts to finding the ground state of the Sherrington-Kirkpatrick model of spin glasses. The asymptotic value of this optimization problem was characterized by Parisi via a celebrated variational principle\, subsequently proved by Talagrand. We give an algorithm that\, for any > 0\, outputs a feasible solution that is at least 1 − of the optimum value\, with probability converging to one as n goes to infinity. The algorithm’s time complexity is 0(n2). It is a message-passing algorithm\, but the specific structure of its update rules is new. As a side result\, we prove that\, at (low) non-zero temperature\, the algorithm constructs approximate solutions of the celebrated Thouless-Anderson-Palmer equations. \nBiography: \nAndrea Montanari received a Laurea degree in Physics in 1997\, and a Ph. D. in Theoretical Physics in 2001 (both from Scuola Normale Superiore in Pisa\, Italy). He has been post-doctoral fellow at Laboratoire de Physique Théorique de l’Ecole Normale Supérieure (LPTENS)\, Paris\, France\, and the Mathematical Sciences Research Institute\, Berkeley\, USA. Since 2002 he is Chargé de Recherche (with Centre National de la Recherche Scientifique\, CNRS) at LPTENS. In September 2006 he joined Stanford University as a faculty\, and since 2015 he is Full Professor in the Departments of Electrical Engineering and Statistics. \nHe was co-awarded the ACM SIGMETRICS best paper award in 2008. He received the CNRS bronze medal for theoretical physics in 2006\, the National Science Foundation CAREER award in 2008\, the Okawa Foundation Research Grant in 2013\, and the Applied Probability Society Best Publication Award in 2015. He is an Information Theory Society distinguished lecturer for 2015-2016. In 2016 he received the James L. Massey Research & Teaching Award of the Information Theory Society for young scholars. In 2018 he was an invited sectional speaker at the International Congress of Mathematicians. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/andrea-montanari/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190131
DTEND;VALUE=DATE:20190202
DTSTAMP:20260408T094529
CREATED:20180801T184336Z
LAST-MODIFIED:20180801T190825Z
UID:8109-1548892800-1549065599@idss-stage.mit.edu
SUMMARY:Laboratory for Information & Decision Systems (LIDS) Student Conference
DESCRIPTION:The annual LIDS Student Conference is a student-organized\, student-run event that provides an opportunity for grad students to present their research to peers as well as to the community at large.
URL:https://idss-stage.mit.edu/calendar/laboratory-for-information-decision-systems-lids-student-conference/
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181214T110000
DTEND;TZID=America/New_York:20181214T120000
DTSTAMP:20260408T094529
CREATED:20180621T193833Z
LAST-MODIFIED:20181204T175526Z
UID:7926-1544785200-1544788800@idss-stage.mit.edu
SUMMARY:Large girth approximate Steiner triple systems
DESCRIPTION:Abstract:  In 1973 Erdos asked whether there are n-vertex partial Steiner triple systems with arbitrary high girth and quadratically many triples. (Here girth is defined as the smallest integer g \ge 4 for which some g-element vertex-set contains at least g-2 triples.) \nWe answer this question\, by showing existence of approximate Steiner triple systems with arbitrary high girth. More concretely\, for any fixed \ell \ge 4 we show that a natural constrained random process typically produces a partial Steiner triple system with (1/6-o(1))n^2 triples and girth larger than \ell. The process iteratively adds random triples subject to the constraint that the girth remains larger than \ell. Our result is best possible up to the o(1)-term\, which is a negative power of n. \nJoint work with Tom Bohman.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-22/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181212T160000
DTEND;TZID=America/New_York:20181212T170000
DTSTAMP:20260408T094529
CREATED:20181210T230157Z
LAST-MODIFIED:20181210T230531Z
UID:8673-1544630400-1544634000@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.\nSee the flier or our website for more information.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-info-session/
LOCATION:E18-411\, 50 Ames St.\, Bldg. E18\, Room 411\, Cambridge\, MA\, 02142\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181210T160000
DTEND;TZID=America/New_York:20181210T170000
DTSTAMP:20260408T094529
CREATED:20180810T161120Z
LAST-MODIFIED:20190501T143433Z
UID:8174-1544457600-1544461200@idss-stage.mit.edu
SUMMARY:Symmetry\, Bifurcation\, and Multi-Agent Decision-Making
DESCRIPTION:Prof. Leonard will present nonlinear dynamics for distributed decision-making that derive from principles of symmetry and bifurcation. Inspired by studies of animal groups\, including house-hunting honeybees and schooling fish\, the nonlinear dynamics describe a group of interacting agents that can manage flexibility as well as stability in response to a changing environment. \nBio: Prof. Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty in Applied and Computational Mathematics at Princeton University. She is a MacArthur Fellow\, and Fellow of the American Academy of Arts and Sciences\, SIAM\, IEEE\, IFAC\, and ASME. She received her BSE in Mechanical Engineering from Princeton University and her PhD in Electrical Engineering from the University of Maryland. Her research is in control and dynamics with application to multi-agent systems\, mobile sensor networks\, collective animal behavior\, and human decision dynamics. \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-6
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181207T110000
DTEND;TZID=America/New_York:20181207T120000
DTSTAMP:20260408T094529
CREATED:20180621T193538Z
LAST-MODIFIED:20181130T173240Z
UID:7924-1544180400-1544184000@idss-stage.mit.edu
SUMMARY:Reducibility and Computational Lower Bounds for Some High-dimensional Statistics Problems
DESCRIPTION:Abstract: The prototypical high-dimensional statistics problem entails finding a structured signal in noise. Many of these problems exhibit an intriguing phenomenon: the amount of data needed by all known computationally efficient algorithms far exceeds what is needed for inefficient algorithms that search over all possible structures. A line of work initiated by Berthet and Rigollet in 2013 has aimed to explain these gaps by reducing from conjecturally hard problems in computer science. However\, the delicate nature of average-case reductions has limited the applicability of this approach. In this work we introduce several new techniques to give a web of average-case reductions showing strong computational lower bounds based on the planted clique conjecture. These include tight lower bounds for Planted Independent Set\, Planted Dense Subgraph\, Biclustering\, Sparse Spiked Wigner\, Sparse PCA\, as well as for new models we introduce. Joint work with Matthew Brennan and Wasim Huleihel. \n Bio:  Guy Bresler is an assistant professor in the Department of Electrical Engineering and Computer Science at MIT\, and a member of LIDS and IDSS.\nPreviously\, he was a postdoc at MIT and before that received his PhD from the Department of EECS at UC Berkeley.\nHe seeks to obtain engineering insight into practically relevant problems by formulating and solving mathematical models. Concretely\, he wants to understand the relationship between combinatorial structure and computational tractability of high-dimensional inference in the context of graphical models and other statistical models\, recommendation systems\, and biology.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-21/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181206T090000
DTEND;TZID=America/New_York:20181206T100000
DTSTAMP:20260408T094529
CREATED:20181005T212647Z
LAST-MODIFIED:20181126T183119Z
UID:8367-1544086800-1544090400@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. \nSES Webinar Flier.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar-4/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181203T160000
DTEND;TZID=America/New_York:20181203T170000
DTSTAMP:20260408T094529
CREATED:20180712T160757Z
LAST-MODIFIED:20181206T141227Z
UID:7987-1543852800-1543856400@idss-stage.mit.edu
SUMMARY:The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility
DESCRIPTION:Abstract:\nWe construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract\, we estimate children’s earnings distributions\, incarceration rates\, and other outcomes in adulthood by parental income\, race\, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby areas: for children of parents at the 25th percentile of the income distribution\, the standard deviation of mean household income at age 35 is $5\,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First\, the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract\, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second\, we show that the observational estimates are highly predictive of neighborhoods’ causal effects\, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low income families\, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth\, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Read the whole paper here.\n \nAbout the speaker:\nRaj Chetty is the William A. Ackman Professor of Economics at Harvard University. He is also the Director of the Equality of Opportunity Project\, which uses “big data” to understand how we can give children from disadvantaged backgrounds better chances of succeeding. Chetty’s research combines empirical evidence and economic theory to help design more effective government policies. His work on topics ranging from tax policy and unemployment insurance to education and affordable housing has been widely cited in academia\, media outlets\, and Congressional testimony. \nChetty received his Ph.D. from Harvard University in 2003 and is one of the youngest tenured professors in Harvard’s history. Before joining the faculty at Harvard\, he was a professor at UC-Berkeley and Stanford University. Chetty has received numerous awards for his research\, including a MacArthur “Genius” Fellowship and the John Bates Clark medal\, given to the economist under 40 whose work is judged to have made the most significant contribution to the field.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-series/
LOCATION:32-155
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181130T110000
DTEND;TZID=America/New_York:20181130T120000
DTSTAMP:20260408T094529
CREATED:20180621T193346Z
LAST-MODIFIED:20181121T171911Z
UID:7922-1543575600-1543579200@idss-stage.mit.edu
SUMMARY:Bias Reduction and Asymptotic Efficiency in Estimation of Smooth Functionals of High-Dimensional Covariance
DESCRIPTION:Abstract:  We discuss a recent approach to bias reduction in a problem of estimation of smooth functionals of high-dimensional parameters of statistical models. In particular\, this approach has been developed in the case of estimation of functionals of covariance operator Σ : Rd → Rd of the form f(Σ)\, B based on n i.i.d. observations X1\, . . . \, Xn sampled from the normal distribution with mean zero and covariance Σ\, f : R → R being a sufficiently smooth\nfunction and B being an operator with nuclear norm bounded by a constant. This includes such problems as estimation of bilinear forms (for instance\, matrix entries in a given basis) of spectral projections of unknown covari-ance that are of importance in principal component analysis. A “bootstrap chain” bias reduction method\, based on an approximate solution of a certain integral equation (the Wishart equation) on the cone of self-adjoint positive semidefinite operators\, yields asymptotically efficient estimators of the func-tional f(Σ)\, B under proper assumptions on the growth of dimension d and smoothness of function f. In particular\, this holds under the assumption that d ≤ nα for some α ∈ (0\, 1) and that f belongs to a Besov space Bs∞\,1(R) for s > 1 . The proof of asymptotic efficiency relies on a number of probabilistic and analytic tools (operator differentiability; Gaussian concentration; properties of Wishart operators and orthogonally invariant functions on the cone of positive semidefinite operators; information-theoretic lower bounds).\n Biography:  Vladimir Koltchinskii is a professor in Mathematics at Georgia Tech. His current research is primarily in high-dimensional statistics and probability.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-20/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
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