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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181129T120000
DTEND;TZID=America/New_York:20181129T133000
DTSTAMP:20260408T041424
CREATED:20181017T142737Z
LAST-MODIFIED:20181017T143418Z
UID:8487-1543492800-1543498200@idss-stage.mit.edu
SUMMARY:IDSS Science Speed Dating Event
DESCRIPTION:Join IDSS faculty\, postdocs\, and graduate students for the first IDSS Science Speed Dating Event on Thursday\, November 29. The purpose of this event is to help the participants to expand their network\, find new research partners\, and strengthen the IDSS community. The event includes lunch. \nTo register for the IDSS Science Speed Dating Event\, please sign up here. Please contact Doro Unger-Lee at dor@mit.edu with any questions you may have about this event.
URL:https://idss-stage.mit.edu/calendar/idss-science-speed-dating-event/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181127T160000
DTEND;TZID=America/New_York:20181127T170000
DTSTAMP:20260408T041424
CREATED:20181031T164820Z
LAST-MODIFIED:20181129T142536Z
UID:8542-1543334400-1543338000@idss-stage.mit.edu
SUMMARY:Censored: Distraction and Diversion Inside China's Great Firewall
DESCRIPTION:Abstract:\nAs authoritarian governments around the world develop sophisticated technologies for controlling information\, many observers have predicted that these controls would be ineffective because they are easily thwarted and evaded by savvy Internet users. In Censored\, Margaret Roberts demonstrates that even censorship that is easy to circumvent can still be enormously effective. Taking advantage of digital data harvested from the Chinese Internet and leaks from China’s Propaganda Department\, this book sheds light on how and when censorship influences the Chinese public. \nRoberts finds that much of censorship in China works not by making information impossible to access but by requiring those seeking information to spend extra time and money for access. By inconveniencing users\, censorship diverts the attention of citizens and powerfully shapes the spread of information. When Internet users notice blatant censorship\, they are willing to compensate for better access. But subtler censorship\, such as burying search results or introducing distracting information on the web\, is more effective because users are less aware of it. Roberts challenges the conventional wisdom that online censorship is undermined when it is incomplete and shows instead how censorship’s porous nature is used strategically to divide the public. \nDrawing parallels between censorship in China and the way information is manipulated in the United States and other democracies\, Roberts reveals how Internet users are susceptible to control even in the most open societies. Demonstrating how censorship travels across countries and technologies\, Censored gives an unprecedented view of how governments encroach on the media consumption of citizens. \n  \nAbout the Speaker:\nMargaret Roberts is an Associate Professor in the Department of Political Science at the University of California\, San Diego. Roberts research focuses on better measuring and understanding the political information strategies of authoritarian governments\, with a specific focus on studying censorship and propaganda in China. She has also developed widely used methods for automated content analysis in the social sciences. Roberts received her PhD in Government from Harvard University in 2014\, an M.S. in Statistics and B.A. in International Relations and Economics from Stanford in 2009. Her work has appeared in venues such as the American Political Science Review\, American Journal of Political Science\, Political Analysis\, Journal of the American Statistical Association and Science.
URL:https://idss-stage.mit.edu/calendar/censored-distraction-and-diversion-inside-chinas-great-firewall/
LOCATION:32-141\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181127T090000
DTEND;TZID=America/New_York:20181127T100000
DTSTAMP:20260408T041424
CREATED:20181005T212414Z
LAST-MODIFIED:20181126T183046Z
UID:8365-1543309200-1543312800@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-3/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181126T160000
DTEND;TZID=America/New_York:20181126T170000
DTSTAMP:20260408T041424
CREATED:20180810T160857Z
LAST-MODIFIED:20190501T143456Z
UID:8172-1543248000-1543251600@idss-stage.mit.edu
SUMMARY:Transportation Systems Resilience: Capacity-Aware Control and Value of Information
DESCRIPTION:Resilience of a transportation system is its ability to operate under adverse events like incidents and storms. Availability of real-time traffic data provides new opportunities for predicting travelers’ routing behavior and implementing network control operations during adverse events. In this talk\, we will discuss two problems: controlling highway corridors in response to disruptions and modeling strategic route choices of travelers with heterogeneous access to incident information. Firstly\, we present an approach to designing control strategies for highway corridors facing stochastic capacity disruptions such random incidents and vehicle platoons/moving bottlenecks. We exploit the properties of traffic flow dynamics under recurrent incidents to derive verifiable conditions for stability of traffic queues\, and also obtain guarantees on the system throughput. Secondly\, we introduce a routing game in which travelers receive asymmetric and incomplete information about uncertain network state\, and make route choices based on their private beliefs about the state and other travelers’ behavior. We study the effects of information heterogeneity on travelers’ equilibrium route choices and costs. Our analysis is useful for evaluating the value of receiving state information for travelers\, which can be positive\, zero\, or negative in equilibrium. These results demonstrate the advantages of considering network state uncertainty in both strategic and operational aspects of system resilience. \nBio: Saurabh Amin is Robert N. Noyce Career Development Associate Professor in the Department of Civil and Environmental Engineering at MIT. He is also affiliated with the Institute of Data\, Systems and Society and the Operations Research Center at MIT. His research focuses on the design of network inspection and control algorithms for infrastructure systems resilience. He studies the effects of security attacks and natural events on the survivability of cyber-physical systems\, and designs incentive mechanisms to reduce network risks. Dr. Amin received his Ph.D. from the University of California\, Berkeley in 2011. His research is supported by NSF CPS FORCES Frontiers project\, NSF CAREER award\, Google Faculty Research award\, DoD-Science of Security Program\, and Siebel Energy Institute Grant. \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-saurabh-amin
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181119T160000
DTEND;TZID=America/New_York:20181119T170000
DTSTAMP:20260408T041424
CREATED:20180810T160618Z
LAST-MODIFIED:20190501T143517Z
UID:8170-1542643200-1542646800@idss-stage.mit.edu
SUMMARY:Modeling Electricity Markets with Complementarity: Why It's Important (and Fun)
DESCRIPTION:Electric power: done wrong\, it drags the economy and environment down; done right\, it could help to create a more efficient\, brighter\, and cleaner future. Better policy\, planning\, and operations models–both simple analytical\, and complex computational ones–are essential if we’re going to do it right. Better modeling is also fun\, as the math of electricity models is inherently interesting and revealing –models often show flaws in our intuition. Used intelligently\, models can point us towards better regulations\, investments\, and operating policies. Simple models provide insights\, while complex models provide the numbers needed to choose specific investments and policies. \n\nComplementarity is one optimization-based approach to modeling energy markets that has more flexibility to model market failures than standard optimization methods. Prof. Hobbs will highlight one application using the power market model COMPETES: the design of renewable portfolio standards\, and an analysis of their price and economic efficiency impacts in the Year 2030. The focus is on energy versus capacity subsidies in the European Union; capacity subsidies are being promoted as potentially being more effective in promoting technology learning. They also have less of an impact upon electricity prices. Prof. Hobbs will also examine the cost of country-specific targets versus EU-wide targets. \nAcknowledgments: Government of the Netherlands and NSF for funding; my PBL colleagues Ozge Ozdemir\, Paul Koustaal\, and Marit van Hout. \n\nBio: B.F. Hobbs earned a Ph.D. (Environmental Systems Engineering) in 1983 from Cornell University. He holds the Theodore M. and Kay W. Schad Chair of Environmental Management at the Johns Hopkins University\, where he has been in the Department of Geography & Environmental Engineering (now Environmental Health & Engineering) since 1995. He also holds a joint appointment in the Department of Applied Mathematics & Statistics and is founding director of the JHU Environment\, Energy\, Sustainability & Health Institute. He co-directs the EPA Yale-JHU Center for Solutions for Energy\, Air\, Climate and Health (SEArCH). Previously\, he was at Brookhaven and Oak Ridge National Laboratories and a member of the Systems Engineering and Civil Engineering faculty at Case Western Reserve University. \nHis research and teaching concern the application of systems analysis and economics to electric utility regulation\, planning\, and operations\, as well as environmental and water resources systems. Dr. Hobbs has previously held visiting appointments at CalTech\, Comillas Pontifical University\, Helsinki University of Technology\, University of Washington\, Netherlands Energy Research Center\, and Cambridge University. He chairs the Market Surveillance Committee of the California Independent System Operator. He was named an NSF Presidential Young Investigator in 1986. Dr. Hobbs is a Fellow of the IEEE and INFORMS. \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-5
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181116T110000
DTEND;TZID=America/New_York:20181116T120000
DTSTAMP:20260408T041424
CREATED:20180621T193041Z
LAST-MODIFIED:20181108T190207Z
UID:7920-1542366000-1542369600@idss-stage.mit.edu
SUMMARY:Model-X knockoffs for controlled variable selection in high dimensional nonlinear regression
DESCRIPTION:Abstract:  Many contemporary large-scale applications\, from genomics to advertising\, involve linking a response of interest to a large set of potential explanatory variables in a nonlinear fashion\, such as when the response is binary. Although this modeling problem has been extensively studied\, it remains unclear how to effectively select important variables while controlling the fraction of false discoveries\, even in high-dimensional logistic regression\, not to mention general high-dimensional nonlinear models. To address such a practical problem\, we propose a new framework of model-X knockoffs\, which reads from a different perspective the knockoff procedure (Barber and Candès\, 2015) originally designed for controlling the false discovery rate in low-dimensional linear models. Model-X knockoffs can deal with arbitrary (and unknown) conditional models and any dimensions\, including when the number of explanatory variables p exceeds the sample size n. Our approach requires the design matrix be random (independent and identically distributed rows) with a known distribution for the explanatory variables\, although we show preliminary evidence that our procedure is robust to unknown/estimated distributions. As we require no knowledge/assumptions about the conditional distribution of the response\, we effectively shift the burden of knowledge from the response to the explanatory variables\, in contrast to the canonical model-based approach which assumes a parametric model for the response but very little about the explanatory variables. To our knowledge\, no other procedure solves the controlled variable selection problem in such generality\, but in the restricted settings where competitors exist\, we demonstrate the superior power of knockoffs through simulations. We also apply our procedure to data from a case-control study of Crohn’s disease in the United Kingdom\, making twice as many discoveries as the original analysis of the same data. \n Biography:  Lucas Janson is an Assistant Professor in the Department of Statistics at Harvard University\, where he works on high-dimensional inference\, autonomous robotic motion planning\, and statistical machine learning. Prior to Harvard\, he was a PhD student in Statistics at Stanford University advised by Professor Emmanuel Candès.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-19/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181113T080000
DTEND;TZID=America/New_York:20181113T170000
DTSTAMP:20260408T041424
CREATED:20180810T160435Z
LAST-MODIFIED:20190501T143540Z
UID:8168-1542096000-1542128400@idss-stage.mit.edu
SUMMARY:Functional Representation of Random variables and Applications
DESCRIPTION:The functional representation lemma says that given random variables X and Y\, there exists a random variable Z\, independent of X\, and a function g(x\,z) such that Y=g(X\,Z). This lemma has had several applications in information theory aimed at simplifying computations of certain information functional. I will present a strengthened version of this lemma and applications to several one-shot coding problems. The first application is to channel simulation with common randomness\, where we obtain an improved bound on the achievable rate by Harsha et al. that applies to arbitrary (not just discrete) random variables. More interestingly\, the Poisson construction used in the proof of the strengthened lemma leads to new and simple achievability results for one-shot coding theorems\, including lossy source coding\, multiple description coding\, and the Gray–Wyner system. I will end with an application of the Poisson construction to minimax learning for remote inference. \nThe new results presented in this talk are joint with Cheuk Ting Li\, Xiugang Wu\, and Ayfer Ozgur. \nBio: Abbas El Gamal is the Hitachi America Professor in the School of Engineering at Stanford University. He received his B.Sc. Honors degree from Cairo University in 1972\, and his M.S. in Statistics and Ph.D. in Electrical Engineering both from Stanford University in 1977 and 1978\, respectively. From 1978 to 1980\, he was an Assistant Professor of Electrical Engineering at USC. From 2003 to 2012\, he was Director of the Information Systems Laboratory at Stanford University. From 2012-2017 he was the Fortinet Founders Chair of the Department of Electrical Engineering at Stanford University. His research contributions have been in network information theory\, FPGAs\, digital imaging devices and systems\, and smart grid modeling and control. He has authored or coauthored over 230 papers and holds 35 patents in these areas. He is a coauthor of the book Network Information Theory (Cambridge Press 2011). He is a member of the US National Academy of Engineering and a Fellow of the IEEE. He received several awards for his research contributions\, including the 2016 IEEE Richard Hamming Medal and the 2012 Claude E. Shannon Award. He served on the Board of Governors of the Information Theory Society from 2009 to 2016 and was its President in 2014. He has been involved in several Silicon Valley startups as co-founder\, a board of director member\, advisor and in several key technical and management positions. \n\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-abbas-el-gamal
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181109T110000
DTEND;TZID=America/New_York:20181109T120000
DTSTAMP:20260408T041424
CREATED:20180621T192716Z
LAST-MODIFIED:20181107T151946Z
UID:7918-1541761200-1541764800@idss-stage.mit.edu
SUMMARY:Optimal hypothesis testing for stochastic block models with growing degrees
DESCRIPTION:Abstract:  In this talk\, we discuss optimal hypothesis testing for distinguishing a stochastic block model from an Erdos–Renyi random graph when the average degree grows to infinity with the graph size. We show that linear spectral statistics based on Chebyshev polynomials of the adjacency matrix can approximate signed cycles of growing lengths when the graph is sufficiently dense. The signed cycles have been shown by Banerjee (2018) to determine the likelihood ratio statistic asymptotically. In this way one achieves sharp asymptotic optimal power of the testing problem within polynomial time complexity. Time permitting\, we will also discuss how linear spectral statistics of a weighted non-backtracking matrix can be used to approximate the likelihood ratio. The talk is based on joint work with Debapratim Banerjee. \n Biography:  Dr.Zongming Ma is an Associate Professor of Statistics of the Wharton School at the University of Pennsylvania. He received his PhD in Statistics from Stanford University in 2010 and has since then been on the faculty of the Wharton Statistics Department. Dr.Ma’s research interests include high-dimensional statistical inference\, non-parametric statistics\, network data analysis\, and their applications in biomedical data analysis. He is a recipient of a Sloan Research Fellowship and an NSF CAREER Award.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-18/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181105T160000
DTEND;TZID=America/New_York:20181105T170000
DTSTAMP:20260408T041424
CREATED:20180712T160448Z
LAST-MODIFIED:20181121T141808Z
UID:7982-1541433600-1541437200@idss-stage.mit.edu
SUMMARY:The Regression Discontinuity Design: Methods and Applications
DESCRIPTION:Abstract:\nThe Regression Discontinuity (RD) design is one of the most widely used non-experimental strategies for the study of treatment effects in the social\, behavioral\, biomedical\, and statistical sciences. In this design\, units are assigned a score and a treatment is offered if the value of that score exceeds a known threshold—and withheld otherwise. In this talk\, I will discuss the assumptions under which the RD design can be used to learn about treatment effects\, and how to make valid inferences about them based on modern theoretical results in nonparametrics that emphasize the importance of extrapolation of regression functions and misspecification biases near the RD cutoff. I will also discuss the common approach of augmenting nonparametric regression models using predetermined covariates in RD setups\, and how this affects nonparametric identification of as well as statistical inference about the RD parameter. If time permits\, I will also discuss a more general version of the RD design based on multiple cutoffs\, which expands the generalizability of the standard RD design by allowing researchers to test richer hypotheses regarding the heterogeneity of the treatment effect and\, under additional assumptions\, to extrapolate the treatment effect to score values far from the cutoff. \n \nRocío Titiunik is the James Orin Murfin Professor of Political Science at the University of Michigan. She specializes in quantitative methodology for the social sciences\, with emphasis on quasi-experimental methods for causal inference and political methodology. Her research interests lie at the intersection of political science\, political economy\, and applied statistics\, particularly on the development and application of quantitative methods to the study of political institutions. Her recent methodological research includes the development of statistical methods for the analysis and interpretation of treatment effects and program evaluation\, with emphasis on regression discontinuity (RD) designs. Her recent substantive research centers on democratic accountability and the role of party systems in developing democracies. Rocio’s work appears in various journals in the social sciences and statistics\, including the American Political Science Review\, the American Journal of Political Science\, the Journal of Politics\, Econometrica\, the Journal of the American Statistical Association\, and the Journal of the Royal Statistical Society. In 2016\, she received the Emerging Scholar Award from the Society for Political Methodology\, which honors a young researcher who is making notable contributions to the field of political methodology. She is a member of the leadership team of the Empirical Implications of Theoretical Models (EITM) Summer Institute\, member-at-large of the Society for Poltical Methodology\, and member of Evidence in Governance and Politics (EGAP). She is also an Associate Editor for Political Science Research and Methods and the American Journal of Political Science\, and has served in the advisory panel for the Methodology\, Measurement\, and Statistics program of the National Science Foundation.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-rocio-titiunik/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181102T110000
DTEND;TZID=America/New_York:20181102T120000
DTSTAMP:20260408T041424
CREATED:20180621T192450Z
LAST-MODIFIED:20181029T150448Z
UID:7916-1541156400-1541160000@idss-stage.mit.edu
SUMMARY:Joint estimation of parameters in Ising Model
DESCRIPTION:Abstract:  Inference in the framework of Ising models has received significant attention in Statistics and Machine Learning in recent years. In this talk we study joint estimation of the inverse temperature parameter β\, and the magnetization parameter B\, given one realization from the Ising model\, under the assumption that the underlying graph of the Ising model is completely specified. We show that if the graph is either irregular or sparse\, then both the parameters can be estimated at rate n−1/2  using Besag’s pseudo-likelihood. Conversely\, if the underlying graph is dense and regular\, we show that no consistent estimates exist for (β\, B).\nThis is joint work with Promit Ghosal from Columbia University. \n Biography:  Sumit is currently an Assistant Professor in the Statistics Department at Columbia. Prior to this\, he received his PhD in Statistics at Stanford\, under the guidance of Persi Diaconis.\nHis research interests lie in the intersection of Theoretical Statistics and Applied Probability. In Statistics\, his main focus is developing inferential procedures on probability distributions on combinatorial spaces\, such as permutations\, graphs\, and spin configurations. On the probability side\, his main focus is studying persistence of stochastic processes\, and graph coloring problems.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-17/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181030T170000
DTEND;TZID=America/New_York:20181030T180000
DTSTAMP:20260408T041424
CREATED:20181005T212240Z
LAST-MODIFIED:20181005T213127Z
UID:8362-1540918800-1540922400@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-2/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181029T160000
DTEND;TZID=America/New_York:20181029T170000
DTSTAMP:20260408T041424
CREATED:20180810T160254Z
LAST-MODIFIED:20190501T143409Z
UID:8166-1540828800-1540832400@idss-stage.mit.edu
SUMMARY:Computing with Assemblies
DESCRIPTION:Computation in the brain has been modeled productively at many scales\, ranging from molecules to dendrites\, neurons\, and synapses\, all the way to the whole brain models useful in cognitive science. I will discuss recent work on an intermediate layer\, involving assemblies of neurons — that is to say\, sets of neurons firing together in a repetitive pattern whenever we think of a particular memory\, concept or idea. Assemblies have been conjectured six decades ago by Hebb\, and have been over the past decade noticed in both the animal and the human brain. Further\, experiments\, simulations\, and theoretical analysis suggest that assemblies can be copied from one brain area to another\, and associated with other assemblies to encode affinity. We propose a broader “calculus” of assemblies\, including operations such as “reciprocal-project” and “merge”\, comprising a powerful computational model. One interesting hypothesis is that assembly operations may underlie some of the most advanced functions of the brain\, such as reasoning\, planning\, language\, math. Work with Santosh Vempala\, Wolfgang Maass\, and Michael Collins. \n\n\nBio: Christos H. Papadimitriou is the Donovan Family professor of computer science at Columbia University. Before joining Columbia in 2017\, he taught at UC Berkeley for 22 years\, and before that at Harvard\, MIT\, NTU Athens\, Stanford\, and UCSD. He has written five textbooks and many articles on algorithms and complexity\, and their applications to optimization\, databases\, control\, AI\, robotics\, economics and game theory\, the Internet\, evolution\, and more recently the study of the brain. He holds a Ph.D. from Princeton as well as eight honorary doctorates\, and he has won the Knuth prize\, the Goedel prize\, and the von Neumann Medal. He is a member of the National Academy of Sciences of the US\, the American Academy of Arts and Sciences\, and the National Academy of Engineering; in 2013 the president of Greece named him Commander of the Order of the Phoenix. He has also written three novels: “Turing”\, “Logicomix” and his latest “Independence”. \n\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-4
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181026T110000
DTEND;TZID=America/New_York:20181026T120000
DTSTAMP:20260408T041424
CREATED:20180621T192221Z
LAST-MODIFIED:20180626T142057Z
UID:7914-1540551600-1540555200@idss-stage.mit.edu
SUMMARY:Alan Frieze
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-16/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181025T090000
DTEND;TZID=America/New_York:20181025T170000
DTSTAMP:20260408T041424
CREATED:20180717T192722Z
LAST-MODIFIED:20180906T134254Z
UID:8046-1540458000-1540486800@idss-stage.mit.edu
SUMMARY:IDSS Retail Conference 2018
DESCRIPTION:The retail sector is undergoing fundamental changes as a result of data analytics and computer science. The IDSS Retail Conference 2018 will bring together experts from academia and industry to discuss a variety of topics addressing “Data Driven Disruption” in the Retail Sector.
URL:https://idssretail2018.mit.edu/
LOCATION:Microsoft New England Research and Development Center\, One Memorial Drive\, Cambridge\, 02139\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181022T160000
DTEND;TZID=America/New_York:20181022T170000
DTSTAMP:20260408T041424
CREATED:20180810T160117Z
LAST-MODIFIED:20190501T143713Z
UID:8164-1540224000-1540227600@idss-stage.mit.edu
SUMMARY:Distributed Statistical Estimation of High-Dimensional Distributions and Parameters under Communication Constraints
DESCRIPTION:Modern data sets are often distributed across multiple machines and processors\, and bandwidth and energy limitations in networks and within multiprocessor systems often impose significant bottlenecks on the performance of algorithms. Motivated by this trend\, we consider the problem of estimating high-dimensional distributions and parameters in a distributed network\, where each node in the network observes an independent sample from the underlying distribution and can communicate it to a central processor by writing at most k bits on a public blackboard. We obtain matching upper and lower bounds for the minimax risk of estimating the underlying distribution or parameter under various common statistical models. Our results show that the impact of the communication constraint can be qualitatively different depending on the tail behavior of the score function associated with each model. The key ingredient in our proof is a geometric characterization of Fisher information from quantized samples. \nJoint work with Leighton Barnes\, Yanjun Han\, and Tsachy Weissman. \nBio: Ayfer Ozgur received her Ph.D. degree in 2009 from the Information Processing Group at EPFL\, Switzerland. In 2010 and 2011\, she was a post-doctoral scholar at the same institution. She is an Assistant Professor in the Electrical Engineering Department at Stanford University since 2012. Her research interests include distributed communication and learning\, wireless systems\, and information theory. Dr. Ozgur received the EPFL Best Ph.D. Thesis Award in 2010\, an NSF CAREER award in 2013\, the Okawa Foundation Research Grant and the IEEE Communication Theory Technical Committee (CTTC) Early Achievement Award in 2018. \n\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-3
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181019T110000
DTEND;TZID=America/New_York:20181019T120000
DTSTAMP:20260408T041424
CREATED:20180621T191820Z
LAST-MODIFIED:20181010T202034Z
UID:7912-1539946800-1539950400@idss-stage.mit.edu
SUMMARY:Algorithmic thresholds for tensor principle component analysis
DESCRIPTION:Abstract:  Consider the problem of recovering a rank 1 tensor of order k that has been subject to Gaussian noise. The log-likelihood for this problem is highly non-convex. It is information theoretically possible to recover the tensor with a finite number of samples via maximum likelihood estimation\, however\, it is expected that one needs a polynomially diverging number of samples to efficiently recover it. What is the cause of this large statistical–to–algorithmic gap? To study this question\, we investigate the thresholds for efficient recovery for a simple family of algorithms\, Langevin dynamics and gradient descent. We view this problem as a member of a broader class of problems which correspond to recovering a signal from a non-linear observation that has been perturbed by isotropic Gaussian noise. We propose a mechanism for success/failure of recovery of such algorithms in terms of the strength of the signal on the high entropy region of the initialization. Joint work with G. Ben Arous (NYU) and R. Gheissari (NYU). \n Biography:  Aukosh Jagannath is a Benjamin Pierce Fellow and NSF Postdoctoral fellow at Harvard University with undergraduate and graduate degree from NYU. He works in probability at the interface of statistical physics\, data science\, combinatorics\, and statistics.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-15/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181017T120000
DTEND;TZID=America/New_York:20181017T130000
DTSTAMP:20260408T041424
CREATED:20181005T211137Z
LAST-MODIFIED:20181005T213025Z
UID:8360-1539777600-1539781200@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/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181016T160000
DTEND;TZID=America/New_York:20181016T170000
DTSTAMP:20260408T041424
CREATED:20180712T160153Z
LAST-MODIFIED:20181024T145321Z
UID:7973-1539705600-1539709200@idss-stage.mit.edu
SUMMARY:Can machine learning survive the artificial intelligence revolution?
DESCRIPTION:  \nAbstract:\nData and algorithms are ubiquitous in all scientific\, industrial and personal domains. Data now come in multiple forms (text\, image\, video\, web\, sensors\, etc.)\, are massive\, and require more and more complex processing beyond their mere indexation or the computation of simple statistics\, such as recognizing objects in images or translating texts. For all of these tasks\, commonly referred to as artificial intelligence (AI)\, significant recent progress has allowed algorithms to reach performances that were deemed unreachable a few years ago and that make these algorithms useful to everyone.\nMany scientific fields contribute to AI\, but most of the visible progress come from machine learning and tightly connected fields such as computer vision and natural language processing. Indeed\, many of the recent advances are due to the availability of massive data to learn from\, large computing infrastructures and new machine learning models (in particular deep neural networks).\nBeyond the well publicized visibility of some advances\, machine learning has always been a field characterized by the constant exchanges between theory and practice\, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Is this still possible? \nIn this talk\, Francis Bach will present recent illustrating machine learning successes and propose some answers to the question above. \nFrancis Bach is a researcher at Inria\, leading since 2011 the machine learning team which is part of the Computer Science Department at Ecole Normale Supérieure. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005\, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris\, then he joined the computer vision project-team at Inria/Ecole Normale Supérieure from 2007 to 2010. Francis Bach is primarily interested in machine learning\, and especially in graphical models\, sparse methods\, kernel-based learning\, large-scale convex optimization\, computer vision and signal processing. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council\, and received the Inria young researcher prize in 2012\, the ICML test-of-time award in 2014\, as well as the Lagrange prize in continuous optimization in 2018. In 2015\, he was program co-chair of the International Conference in Machine learning (ICML)\, and general chair in 2018; he is now co-editor-in-chief of the Journal of Machine Learning Research.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-francis-bach/
LOCATION:32-141\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181015T160000
DTEND;TZID=America/New_York:20181015T170000
DTSTAMP:20260408T041424
CREATED:20180810T155930Z
LAST-MODIFIED:20190501T143758Z
UID:8162-1539619200-1539622800@idss-stage.mit.edu
SUMMARY:Augmented Lagrangians and Decomposition in Convex and Nonconvex Programming
DESCRIPTION:Multiplier methods based on augmented Lagrangians are attractive in convex and nonconvex programming for their stabilizing and even convexifying properties. They have widely been seen\, however\, as incompatible with taking advantage of a block-separable structure. \nIn fact\, when articulated in the right way\, they can produce decomposition algorithms in which low-dimensional subproblems can be solved in parallel. Convergence in the nonconvex case is\, of course\, just local\, but is available under a broad analog of the strong second-order sufficient condition for local optimality that dominates much of computational methodology outside of convex optimization. This carries over also to extended nonlinear programming with its greater flexibility to handle composite terms. \nBio: Ralph Tyrrell (Terry) Rockafellar has long been associated with the University of Washington\, Seattle\, where he is Professor Emeritus of Mathematics\, but has also contributed in recent years as Adjunct Research Professor of Systems and Industrial Engineering at the University of Florida\, Gainesville\, and as Honorary Professor of the Department of Applied Mathematics at Hong Kong Polytechnic University. \nHis interests span from convex and variational analysis to problems of optimization and equilibrium\, especially nowadays applications in finance\, engineering\, and economics involving risk and reliability\, along with schemes of problem decomposition on convex and nonconvex programming. \nIn addition to being a winner of the Dantzig Prize given jointly by SIAM and the Mathematical Programming Society (1983)\, Prof. Rockafellar has gained international recognition for his work through honorary doctorates bestowed by universities in a number of countries. INFORMS awarded him and Roger Wets the 1997 Lancaster Prize for their book Variational Analysis\, and in 1999 he was further honored by INFORMS with John von Neumann Theory Prize for his fundamental contributions to the methodology of optimization. He has authored over 240 publications\, including one of the all-time most highly cited books in mathematics\, Convex Analysis. \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-2
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20181015
DTEND;VALUE=DATE:20181016
DTSTAMP:20260408T041424
CREATED:20180802T010632Z
LAST-MODIFIED:20180802T010844Z
UID:8118-1539561600-1539647999@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data-Driven Decisions
DESCRIPTION:
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+4T2018/about?utm_medium=website&utm_source=idss&utm_campaign=ds-fl18-sept&utm_content=event-calendar
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181012T110000
DTEND;TZID=America/New_York:20181012T120000
DTSTAMP:20260408T041424
CREATED:20180621T191525Z
LAST-MODIFIED:20181004T134355Z
UID:7910-1539342000-1539345600@idss-stage.mit.edu
SUMMARY:Locally private estimation\, learning\, inference\, and optimality
DESCRIPTION:Abstract: In this talk\, we investigate statistical learning and estimation under local privacy constraints\, where data providers do not trust the collector of the data and so privatize their data before it is even collected. We identify fundamental tradeoffs between statistical utility and privacy in such local models of privacy\, providing instance-specific bounds for private estimation and learning problems by developing local minimax risks. In contrast to approaches based on worst-case (minimax) error\, which are conservative\, this allows us to evaluate the difficulty of individual problem instances and delineate the possibilities for adaptation in private estimation and inference. As part of this\, we identify an alternative to the Fisher information for private estimation\, giving a more nuanced understanding of the challenges of adaptivity and optimality. We also provide optimal procedures for private inference\, highlighting the importance of a more careful development of optimal tradeoffs between estimation and privacy. One consequence of our results is to identify settings where standard local privacy restrictions may be too strong for practice; time permitting\, I will then discuss a few new directions that maintain limited amounts of privacy while simultaneously allowing the development of high-performance statistical and learning procedures.\nBased on joint work with Feng Ruan.\n\nBiography: John Duchi is an assistant professor of Statistics and Electrical Engineering and (by courtesy) Computer Science at Stanford University\, with graduate degrees from UC Berkeley and undergraduate degrees from Stanford. His work focuses on large scale optimization problems arising out of statistical and machine learning problems\, robustness and uncertain data problems\, and information theoretic aspects of statistical learning. He has won a number of awards and fellowships\, including best paper awards at the Neural Information Processing Systems conference\, the International Conference on Machine Learning\, an NSF CAREER award\, a Sloan Fellowship in Mathematics\, the Okawa Foundation Award\, and the Association for Computing Machinery (ACM) Doctoral Dissertation Award (honorable mention).
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-14/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181011T160000
DTEND;TZID=America/New_York:20181011T170000
DTSTAMP:20260408T041424
CREATED:20181010T144504Z
LAST-MODIFIED:20181010T145040Z
UID:8389-1539273600-1539277200@idss-stage.mit.edu
SUMMARY:Local Geometric Analysis and Applications
DESCRIPTION:Abstract: Local geometric analysis is a method to define a coordinate system in a small neighborhood in the space of distributions over a given alphabet. It is a powerful technique since the notions of distance\, projection\, and inner product defined this way are useful in the optimization problems involving distributions\, such as regressions. It has been used in many places in the literature such as correlation analysis\, correspondence analysis. In this talk\, we will go through some of the basic setups and properties\, and discuss a few applications in information theory\, dimension reduction and softmax regression. \n About this Seminar: This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory\, inference\, causality\, estimation\, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers\, and with the exception of the two lectures on randomness and information\, there is no planned coherence or dependency among them. Ad hoc follow-on meetings about any of the topics presented are highly encouraged.
URL:https://idss-stage.mit.edu/calendar/local-geometric-analysis-and-applications/
LOCATION:32-D677\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181008T093000
DTEND;TZID=America/New_York:20181008T163000
DTSTAMP:20260408T041424
CREATED:20181002T125917Z
LAST-MODIFIED:20181002T130105Z
UID:8310-1538991000-1539016200@idss-stage.mit.edu
SUMMARY:HUBweek Policy Hackathon
DESCRIPTION:
URL:https://www.mitpolicyhackathon.org/hubweek/
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181005T110000
DTEND;TZID=America/New_York:20181005T120000
DTSTAMP:20260408T041424
CREATED:20180621T191105Z
LAST-MODIFIED:20181004T152601Z
UID:7908-1538737200-1538740800@idss-stage.mit.edu
SUMMARY:Efficient Algorithms for the Graph Matching Problem in Correlated Random Graphs
DESCRIPTION:Abstract:  The Graph Matching problem is a robust version of the Graph Isomorphism problem: given two not-necessarily-isomorphic graphs\, the goal is to find a permutation of the vertices which maximizes the number of common edges. We study a popular average-case variant; we deviate from the common heuristic strategy and give the first quasi-polynomial time algorithm\, where previously only sub-exponential time algorithms were known.\nBased on joint work with Boaz Barak\, Chi-Ning Chou\, Zhixian Lei\, and Yueqi Sheng.\n\n\nBiography: Tselil Schramm is a postdoc in theoretical computer science at Harvard and MIT\, hosted by Boaz Barak\, Jon Kelner\, Ankur Moitra\, and Pablo Parrilo. She obtained her PhD in computer science from U.C. Berkeley under the advisement of Prasad Raghavendra and Satish Rao. Her research interests include inference and average-case problems\, optimization via convex programs (especially the sum-of-squares hierarchy)\, spectral algorithms\, spectral graph theory\, and more.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-13/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180928T110000
DTEND;TZID=America/New_York:20180928T120000
DTSTAMP:20260408T041424
CREATED:20180621T194142Z
LAST-MODIFIED:20180919T020411Z
UID:7928-1538132400-1538136000@idss-stage.mit.edu
SUMMARY:Jingbo Liu
DESCRIPTION:Abstract  Concentration of measure refers to a collection of tools and results from analysis and probability theory that have been used in many areas of pure and applied mathematics. Arguably\, the first data science application of measure concentration (under the name ‘‘blowing-up lemma’’) is the proof of strong converses in multiuser information theory by Ahlswede\, G’acs and K”orner in 1976. Since then\, measure concentration has found applications in many other information theoretic problems\, most notably the converse (impossibility) results in information theory. Motivated by this\, information theorists (e.g. Marton) have also contributed to the mathematical foundations of measure concentration using their information-theoretic techniques. \nNow\, after all the past 40 years of such progress\, we found that\, amusingly\, measure concentration is not the right hammer for many of these information theoretic applications. We introduce a new machinery based on functional inequalities and reverse hypercontractivity which yields strict improvements in terms of sharpness of the bounds\, generality of the source/channel distributions\, and simplicity of the proofs. Examples covered in the talk include: 1. optimal second-order converses to distributed source-type problems (hypothesis testing\, common randomness generation\, and source coding); 2. sharpening the recent relay channel converse bounds by Wu and Ozgur with much simpler proofs. \nThe work benefited from collaborations with Thomas Courtade\, Paul Cuff\, Ayfer Ozgur\, Ramon van Handel\, and Sergio Verd’u \n Biography:  jingbo Liu received the B.E. degree from Tsinghua University\, Beijing\, China in 2012\, and the M.A. and Ph.D. degrees from Princeton University\, Princeton\, NJ\, USA\, in 2014 and 2018\, all in electrical engineering. His research interests include signal processing\, information theory\, coding theory\, high dimensional statistics\, and the related fields. His undergraduate thesis received the best undergraduate thesis award at Tsinghua University (2012). He gave a semi-plenary presentation at the 2015 IEEE Int. Symposium on Information Theory\, Hong-Kong\, China. He was a recipient of the Princeton University Wallace Memorial Honorific Fellowship in 2016.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-12/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180925T160000
DTEND;TZID=America/New_York:20180925T170000
DTSTAMP:20260408T041424
CREATED:20180726T171445Z
LAST-MODIFIED:20180904T145459Z
UID:8078-1537891200-1537894800@idss-stage.mit.edu
SUMMARY:Text as Data in Social Science: Discovery\, Measurement and Causal Inference
DESCRIPTION:Social scientists are increasingly turning to computer-assisted text analysis as a way of understanding the digital footprints left by communities and individuals.  Much of the technology that powers these approaches is borrowed from the fields of computer science and statistics; yet\, social scientists have substantially different goals.  We focus on the development of methods that support three core tasks: discovery\, measurement and causal inference with text.  We introduce the Structural Topic Model (STM)\, a bayesian generative model of text which is built for social science inference.  Using this model as a running example\, we will discuss the challenges of discovery\, measurement and causal inference and how to adapt our tools to approach each task.  The tasks will be illustrated with multiple examples across many different domains.  The talk will end with future directions for this fast-moving\, inter-disciplinary field. [Includes joint work with Molly Roberts\, Justin Grimmer\, Dustin Tingley\, Edo Airoldi\, Richard Nielsen and others.]\nAbout the speaker: Brandon Stewart is an Assistant Professor in the Department of Sociology and is also affiliated with the Department of Politics and the Office of Population Research. He develops new quantitative statistical methods for applications across the social sciences. Methodologically his focus is in tools which facilitate automated text analysis and model complex heterogeneity in regression. Many recent applications of these methods have centered on using large corpora of text to better understand propaganda in contemporary China. His research has been published in journals such as American Journal of Political Science\, Political Analysis and the Proceedings of the Association of Computational Linguistics. His work has won the Edward R Chase Dissertation Prize\, the Gosnell Prize for Excellence in Political Methodology\, and the Political Analysis Editor’s Choice Award.
URL:https://idss-stage.mit.edu/calendar/idss-seminar-brandon-stewart/
LOCATION:32-141\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180924T160000
DTEND;TZID=America/New_York:20180924T170000
DTSTAMP:20260408T041424
CREATED:20180810T155456Z
LAST-MODIFIED:20190501T143856Z
UID:8160-1537804800-1537808400@idss-stage.mit.edu
SUMMARY:The Power of Multiple Samples in Generative Adversarial Networks
DESCRIPTION:We bring the tools from Blackwell’s seminal result on comparing two stochastic experiments from 1953\, to shine a new light on a modern application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs\, where a trained neural network (called a critic) determines whether a given sample is from the real data or the generated (fake) data. By jointly training the generator and the critic\, the hope is that eventually the trained generator will generate realistic samples. One of the major challenges in GAN is known as “mode collapse”; the lack of diversity in the samples generated by thus trained generators. We propose a new training framework\, where the critic is fed with multiple samples jointly (which we call packing)\, as opposed to each sample separately as done in standard GAN training. With this simple but fundamental departure from standard GANs\, experimental results show that the diversity of the generated samples improve significantly. We analyze this practical gain by first providing a formal mathematical definition of mode collapse and making a fundamental connection between the idea of packing and the intensity of mode collapse. Precisely\, we show that the packed critic naturally penalizes mode collapse\, thus encouraging generators with less mode collapse. The analyses critically rely on operational interpretation of hypothesis testing and corresponding data processing inequalities\, which lead to sharp analyses with simple proofs. For this talk\, I will assume no prior background on GANs. \nThis is joint work with Zinan Lin (CMU)\, Ahsish Khetan (Amazon AI)\, and Giulia Fanti (CMU). \nBio: Sewoong Oh is an Associate Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD\, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. His research interest is in theoretical machine learning\, including spectral methods\, ranking\, crowdsourcing\, estimation of information measures\, differential privacy\, and generative adversarial networks. He was co-awarded the best paper award at the SIGMETRICS in 2015\, NSF CAREER award in 2016 and GOOGLE Faculty Research Award. \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-1
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180921T110000
DTEND;TZID=America/New_York:20180921T120000
DTSTAMP:20260408T041424
CREATED:20180621T184900Z
LAST-MODIFIED:20180626T141436Z
UID:7904-1537527600-1537531200@idss-stage.mit.edu
SUMMARY:Boaz Nadler
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-11/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180920T160000
DTEND;TZID=America/New_York:20180920T170000
DTSTAMP:20260408T041424
CREATED:20180910T161945Z
LAST-MODIFIED:20180919T022223Z
UID:8249-1537459200-1537462800@idss-stage.mit.edu
SUMMARY:Topics in Information and Inference Seminar
DESCRIPTION:Title: Strong data processing inequalities and information percolation\n\n Abstract: The data-processing inequality\, that is\, $I(U;Y) \le I(U;X)$ for a Markov chain $U \to X \to Y$\, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. A channel-dependent improvement is called the strong data-processing inequality (or SDPI). In this talk we will: a) review SDPIs; b) show how point-to-point SDPIs can be combined into an SDPI for a network; c) show recent applications to problems of statistical inference on graphs (spiked Wigner model\, community detection etc.)
URL:https://idss-stage.mit.edu/calendar/topics-in-information-and-inference-seminar/
LOCATION:32-D677\, United States
CATEGORIES:IDSS Special Seminars
ORGANIZER;CN="":MAILTO:jeannille.hiciano@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180917T160000
DTEND;TZID=America/New_York:20180917T170000
DTSTAMP:20260408T041424
CREATED:20180810T155258Z
LAST-MODIFIED:20190501T143950Z
UID:8158-1537200000-1537203600@idss-stage.mit.edu
SUMMARY:Regret of Queueing Bandits
DESCRIPTION:We consider a variant of the multiarmed bandit (MAB) problem where jobs or tasks queue for service\, and service rates of different servers (agents) may be unknown. Such (queueing+learning) problems are motivated by a vast range of service systems\, including supply and demand in online platforms (e.g.\, Uber\, Lyft\, Airbnb\, Upwork\, etc.)\, order flow in financial markets (e.g.\, limit order books)\, communication systems\, and supply chains. \nWe study algorithms that minimize queue-regret: the expected difference between the queue-lengths (backlogs) obtained by the algorithm\, and those obtained by a genie-aided matching algorithm that knows exact service rates. A naive view of this problem would suggest that queue-regret could grow logarithmically: since queue-regret cannot be larger than classical regret\, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time. Our work shows surprisingly more complex behavior — specifically\, the optimal queue-regret decreases with time and scales as O(1/t). We next consider holding-cost regret in multi-class (multiple types of tasks) multi-server (servers/agents have task-type dependent service rate) systems. Holding costs correspond to a system where a linear cost (with respect to time spent in the queue) is incurred for each incomplete task. We consider learning-based variants of the c-mu rule – a classic and well-studied scheduling policy that is used when server/agent service rates are known. We develop algorithms that result in constant expected holding-cost regret (independent of time). The key insight that allows such a regret bound is that service systems we consider exhibit explore-free learning\, where no penalty is (eventually) incurred for exploring and learning server/agent rates. We finally discuss the implications of our results on building platforms for matching tasks to servers/agents. Base on joint work with Subhashini Krishnasamy\, Rajat Sen\, Ari Arapostathis and Ramesh Johari. \nBio: Sanjay Shakkottai received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign in 2002. He is with The University of Texas at Austin\, where he is currently the Ashley H. Priddy Centennial Professor in Engineering\, the Director of the Wireless Networking and Communications Group (WNCG)\, and a Professor in the Department of Electrical and Computer Engineering. He received the NSF CAREER award in 2004 and was elected as an IEEE Fellow in 2014. His research interests lie at the intersection of algorithms for resource allocation\, statistical learning and networks\, with applications to wireless communication networks and online platforms. \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-0
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
END:VCALENDAR