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VERSION:2.0
PRODID:-//MIT Statistics and Data Science Center - ECPv4.6//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:MIT Statistics and Data Science Center
X-ORIGINAL-URL:https://stat.mit.edu
X-WR-CALDESC:Events for MIT Statistics and Data Science Center
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190222T110000
DTEND;TZID=America/New_York:20190222T120000
DTSTAMP:20190216T102830
CREATED:20190204T175546Z
LAST-MODIFIED:20190213T164459Z
UID:3118-1550833200-1550836800@stat.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 a joint work with Jian Ding. \n Biography: \nNike Sun is a faculty member in the MIT mathematics department. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/capacity-lower-bound-ising-perceptron-nikesun/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190301T110000
DTEND;TZID=America/New_York:20190301T120000
DTSTAMP:20190216T102830
CREATED:20190204T180401Z
LAST-MODIFIED:20190205T181647Z
UID:3121-1551438000-1551441600@stat.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. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/arent-network-statistics-accompanied-uncertainty-statements-erickolaczyk/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190304
DTEND;VALUE=DATE:20190304
DTSTAMP:20190216T102830
CREATED:20190117T153852Z
LAST-MODIFIED:20190117T163824Z
UID:3042-1551657600-1551743999@stat.mit.edu
SUMMARY:Women in Data Science (WiDS) Conference
DESCRIPTION:This one-day technical conference will bring 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. The program will include technical talks\, a student poster session\, recruiting opportunities\, and several networking breaks throughout the day.\n
URL:https://www.widscambridge.org/
LOCATION:100 Memorial Drive\, Cambridge\, MA\, 02142\, United States
CATEGORIES:SDSC Special Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20190216T102830
CREATED:20190204T181008Z
LAST-MODIFIED:20190205T181737Z
UID:3124-1552042800-1552046400@stat.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. \n
URL:https://stat.mit.edu/calendar/univariate-total-variation-denoising-trend-filtering-multivariate-hardy-krause-variation-denoising-adityaguntuboyina/
LOCATION:Massachusetts
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190315T110000
DTEND;TZID=America/New_York:20190315T120000
DTSTAMP:20190216T102830
CREATED:20190204T195456Z
LAST-MODIFIED:20190205T181759Z
UID:3127-1552647600-1552651200@stat.mit.edu
SUMMARY:Stochastics and Statistics Seminar
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/tbd-alexbelloni/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190322T110000
DTEND;TZID=America/New_York:20190322T120000
DTSTAMP:20190216T102830
CREATED:20190204T195930Z
LAST-MODIFIED:20190205T183327Z
UID:3129-1553252400-1553256000@stat.mit.edu
SUMMARY:Stochastics and Statistics Seminar
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/eliransubag/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190405
DTEND;VALUE=DATE:20190405
DTSTAMP:20190216T102830
CREATED:20180801T184809Z
LAST-MODIFIED:20190214T202617Z
UID:2748-1554422400-1554508799@stat.mit.edu
SUMMARY:SDSCon 2019 - Statistics and Data Science Conference
DESCRIPTION:\nSDSCon 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). \n\n
URL:https://sdsc2019.mit.edu/
LOCATION:Massachusetts
CATEGORIES:SDSC Special Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190412T110000
DTEND;TZID=America/New_York:20190412T120000
DTSTAMP:20190216T102830
CREATED:20190204T202247Z
LAST-MODIFIED:20190206T172019Z
UID:3133-1555066800-1555070400@stat.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. \n
URL:https://stat.mit.edu/calendar/aadityaramdas/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190419T110000
DTEND;TZID=America/New_York:20190419T120000
DTSTAMP:20190216T102830
CREATED:20190204T202742Z
LAST-MODIFIED:20190205T181834Z
UID:3135-1555671600-1555675200@stat.mit.edu
SUMMARY:Stochastics and Statistics Seminar
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/tbd-aaronroth/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190426T110000
DTEND;TZID=America/New_York:20190426T120000
DTSTAMP:20190216T102830
CREATED:20190204T203045Z
LAST-MODIFIED:20190206T173655Z
UID:3137-1556276400-1556280000@stat.mit.edu
SUMMARY:Stochastics and Statistics Seminar Series
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/chaogao/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190503T110000
DTEND;TZID=America/New_York:20190503T120000
DTSTAMP:20190216T102830
CREATED:20190204T203453Z
LAST-MODIFIED:20190205T181905Z
UID:3139-1556881200-1556884800@stat.mit.edu
SUMMARY:Stochastics and Statistics Seminar
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/tbd-tracyke/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190510T110000
DTEND;TZID=America/New_York:20190510T120000
DTSTAMP:20190216T102830
CREATED:20190204T204427Z
LAST-MODIFIED:20190206T172558Z
UID:3141-1557486000-1557489600@stat.mit.edu
SUMMARY:Counting and sampling at low temperatures
DESCRIPTION: Abstract: \nWe consider the problem of efficient sampling from the hard-core and Potts models from statistical physics. On certain families of graphs\, phase transitions in the underlying physics model are linked to changes in the performance of some sampling algorithms\, including Markov chains. We develop new sampling and counting algorithms that exploit the phase transition phenomenon and work efficiently on lattices (and bipartite expander graphs) at sufficiently low temperatures in the phase coexistence regime. Our algorithms are based on Pirogov-Sinai theory and the cluster expansion\, classical tools from statistical physics. Joint work with Tyler Helmuth and Guus Regts. \n Biography: \nWill Perkins is an assistant professor in the Department of Mathematics\, Statistics\, and Computer Science at the University of Illinois at Chicago. His research interests are in probability\, combinatorics\, and algorithms. He received his PhD in 2011 from New York University\, then was a postdoc at Georgia Tech and faculty at the University of Birmingham before moving to UIC in 2018. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/tbd-willperkins/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=50 Ames Street Cambridge MA 02139;X-APPLE-RADIUS=500;X-TITLE=50 Ames Street:geo:-71.0878444,42.3620185
CATEGORIES:Stochastics and Statistics Seminar
END:VEVENT
END:VCALENDAR