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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190510T080000
DTEND;TZID=America/New_York:20190510T170000
DTSTAMP:20260408T112347
CREATED:20190204T204606Z
LAST-MODIFIED:20190307T163046Z
UID:8832-1557475200-1557507600@idss-stage.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.
URL:https://stat.mit.edu/calendar/tbd-willperkins/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190508T150000
DTEND;TZID=America/New_York:20190508T160000
DTSTAMP:20260408T112347
CREATED:20190426T181812Z
LAST-MODIFIED:20190501T142054Z
UID:9525-1557327600-1557331200@idss-stage.mit.edu
SUMMARY:Representing Short-Term Uncertainties in Capacity Expansion Planning Using an Rolling-Horizon Operation Model
DESCRIPTION:Flexible resources such as batteries and demand-side management technologies are needed to handle future large shares of variable renewable power. Wind and solar power introduce more short-term uncertainty that have to be considered when making investment decisions as it significantly impacts the value of flexible resources. \nIn this work we present a method for using duals from a rolling horizon operational model\, with wind power uncertainty and market representations\, to represent power system operation in an investment problem. The method is based on benders decomposition and special considerations are made due to the nature of the rolling horizon operational framework. \nBio: Espen Flo Bødal is a PhD student from the Norwegian University of Science and Technology (NTNU) in Trondheim\, Norway. He has a master degree in Electric Power Engineering and are currently starting his third year of the PhD on the topic of ”Large-Scale Hydrogen Production for Wind and Hydro Power in Constrained Transmission Grids”. From September 2018 to May 2019 he is a visiting at LIDS with Audun Botterud. \n____________________________________ \nTea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks\, please email lids_stats_tea@mit.edu.
URL:https://lids.mit.edu/news-and-events/events/representing-short-term-uncertainties-capacity-expansion-planning-using
LOCATION:32 – LIDS Lounge\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS & Stats Tea Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190507T160000
DTEND;TZID=America/New_York:20190507T170000
DTSTAMP:20260408T112347
CREATED:20190129T150047Z
LAST-MODIFIED:20190514T131508Z
UID:8800-1557244800-1557248400@idss-stage.mit.edu
SUMMARY:Design and Analysis of Two-Stage Randomized Experiments
DESCRIPTION:Abstract:\nIn many social science experiments\, subjects often interact with each other and as a result\, one unit’s treatment can influence the outcome of another unit. Over the last decade\, a significant progress has been made towards causal inference in the presence of such interference between units. In this talk\, we will discuss two-stage randomized experiments\, which enable the identification of the average spillover effects as well as that of the average direct effect of one’s own treatment. In particular\, we consider the setting with noncompliance\, in which some units in the treatment group do not receive the treatment while others in the control group may take up one. This implies that there may exist the spillover effect of the treatment assignment on the treatment receipt as well as the spillover effect of the treatment receipt on the outcome. To address this complication\, we generalize the instrumental variables method by allowing for interference between units and show how to identify the average complier direct effect. We also establish the connections between our nonparametric randomization-inference approach and the two-stage least squares regression. The proposed methodology is motivated by and applied to an ongoing randomized evaluation of the India’s National Health Insurance Program (RSBY). Joint work with Zhichao Jiang and Anup Malani. \nAbout the Speaker:\nKosuke Imai is Professor in the Department of Government and the Department of Statistics at Harvard University. He is also an affiliate of the Institute for Quantitative Social Science where his primary office is located. Before moving to Harvard in 2018\, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. He specializes in the development of statistical methods and their applications to social science research and is the author of Quantitative Social Science: An Introduction (Princeton University Press\, 2017). Outside of Harvard\, Imai is currently serving as the President of the Society for Political Methodology. He is also Professor of Visiting Status in the Graduate Schools of Law and Politics at The University of Tokyo.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-may/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190503T110000
DTEND;TZID=America/New_York:20190503T120000
DTSTAMP:20260408T112347
CREATED:20190204T203624Z
LAST-MODIFIED:20190206T173354Z
UID:8827-1556881200-1556884800@idss-stage.mit.edu
SUMMARY:Stochastics and Statistics Seminar Series
DESCRIPTION:
URL:https://stat.mit.edu/calendar/tbd-tracyke/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190501T150000
DTEND;TZID=America/New_York:20190501T160000
DTSTAMP:20260408T112347
CREATED:20190424T142934Z
LAST-MODIFIED:20190501T142123Z
UID:9457-1556722800-1556726400@idss-stage.mit.edu
SUMMARY:Generalization and Learning under Dobrushin's Condition
DESCRIPTION:Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data\, there has been a growing literature on learning and generalization in settings where data is sampled from an ergodic process. This work has also developed complexity measures\, which appropriately extend Rademacher complexity to bound the generalization error and learning rates of hypothesis classes in this setting. Rather than time-series data\, our work is motivated by settings where data is sampled on a network or a spatial domain\, and thus do not fit well the framework of prior work. We provide learning and generalization bounds for data that are complexly dependent\, yet their distribution satisfies the standard Dobrushin condition. Indeed\, we show that the standard complexity measures of (Gaussian) Rademacher complexity and VC dimension are sufficient measures of complexity for the purposes of bounding the generalization error and learning rates of hypothesis classes in our setting. Moreover\, our generalization bounds only degrade by constant factors compared to their i.i.d. analogs and our learnability bounds degrade by log factors in the size of the training set. \nJoint work with Constantinos Daskalakis\, Nishanth Dikkala\, and Siddhartha Jayanti. \nBio: Yuval Dagan is a PhD student at the EECS department of MIT. He received his Bachelor’s and Master’s degrees from the Technion – Israel Institute of Technology. \n____________________________________ \nTea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks\, please email lids_stats_tea@mit.edu.
URL:https://lids.mit.edu/news-and-events/events/generalization-and-learning-under-dobrushins-condition
LOCATION:32 – LIDS Lounge\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS & Stats Tea Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190430T160000
DTEND;TZID=America/New_York:20190430T170000
DTSTAMP:20260408T112347
CREATED:20190301T171816Z
LAST-MODIFIED:20190501T142201Z
UID:8989-1556640000-1556643600@idss-stage.mit.edu
SUMMARY:On Coupling Methods for Nonlinear Filtering and Smoothing
DESCRIPTION:Bayesian inference for non-Gaussian state-space models is a ubiquitous problem with applications ranging from geophysical data assimilation to mathematical finance. We will discuss how deterministic couplings between probability distributions enable new solutions to this problem. \nWe first consider filtering in high-dimensional models with nonlinear (potentially chaotic) dynamics and sparse observations in space and time. While the ensemble Kalman filter (EnKF) yields robust ensemble approximations of the filtering distribution in this setting\, it is limited by linear forecast-to-analysis transformations. To generalize the EnKF\, we propose a methodology that transforms the non-Gaussian forecast ensemble at each assimilation step into samples from the current filtering distribution via a sequence of local nonlinear couplings. These couplings are based on transport maps that can be computed quickly using convex optimization\, and that can be enriched in complexity to reduce the intrinsic bias of the EnKF. We discuss the low-dimensional structure inherited by the transport maps from the filtering problem\, including decay of correlations\, conditional independence\, and local likelihoods. We then exploit this structure to regularize the estimation of the maps in high dimensions and with a limited ensemble size. \nWe also present variational methods—again based on transport—for smoothing and sequential parameter estimation in non-Gaussian state-space models. These methods rely on results linking the Markov properties of a target measure to the existence of low-dimensional couplings\, induced by transport maps that are decomposable. The resulting algorithms can be understood as a generalization\, to the non-Gaussian case\, of the square-root Rauch–Tung–Striebel Gaussian smoother. \nThis is joint work with Ricardo Baptista\, Daniele Bigoni\, and Alessio Spantini. \nBio: Youssef Marzouk is an associate professor in the Department of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Engineering. He is also director of MIT’s Aerospace Computational Design Laboratory and a member of MIT’s Statistics and Data Science Center. \nHis research interests lie at the intersection of physical modeling with statistical inference and computation. In particular\, he develops methodologies for uncertainty quantification\, inverse problems\, large-scale Bayesian computation\, and optimal experimental design in complex physical systems. His methodological work is motivated by a wide variety of engineering\, environmental\, and geophysics applications. \nHe received his SB\, SM\, and PhD degrees from MIT and spent several years at Sandia National Laboratories before joining the MIT faculty in 2009. He is a recipient of the Hertz Foundation Doctoral Thesis Prize (2004)\, the Sandia Laboratories Truman Fellowship (2004-2007)\, the US Department of Energy Early Career Research Award (2010)\, and the Junior Bose Award for Teaching Excellence from the MIT School of Engineering (2012). He is an Associate Fellow of the AIAA and currently serves on the editorial boards of the SIAM Journal on Scientific Computing\, Advances in Computational Mathematics\, and the SIAM/ASA Journal on Uncertainty Quantification. He is an avid coffee drinker and occasional classical pianist. \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-youssef-marzouk
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190426T110000
DTEND;TZID=America/New_York:20190426T120000
DTSTAMP:20260408T112347
CREATED:20190401T154526Z
LAST-MODIFIED:20190423T144817Z
UID:9202-1556276400-1556280000@idss-stage.mit.edu
SUMMARY:Robust Estimation: Optimal Rates\, Computation and Adaptation
DESCRIPTION:Abstract: Chao Gao will discuss the problem of statistical estimation with contaminated data. In the first part of the talk\, I will discuss depth-based approaches that achieve minimax rates in various problems. In general\, the minimax rate of a given problem with contamination consists of two terms: the statistical complexity without contamination\, and the contamination effect in the form of modulus of continuity. In the second part of the talk\, I will discuss computational challenges of these depth-based estimators. An interesting relation between statistical depth function and a general f-learning framework will be discussed\, which leads to a computation strategy via minimax optimization in the framework of generative adversarial nets (GAN). Finally\, I will address the problem of adaptive estimation under contamination model. It turns out adaptive estimation becomes a much harder task with contamination. Besides the classical logarithmic cost of adaptive estimation in some cases\, it can be shown that in certain situation\, adaptation can be completely impossible with any rate. \nBiography: Chao Gao is an assistant professor in statistics at University of Chicago. I graduated from Yale University. My advisor is Harry Zhou. My research lies in nonparametric and high-dimensional statistics\, network analysis\, Bayes theory and robust statistics.MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/chaogao/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190424T150000
DTEND;TZID=America/New_York:20190424T160000
DTSTAMP:20260408T112347
CREATED:20190423T172233Z
LAST-MODIFIED:20190430T195016Z
UID:9425-1556118000-1556121600@idss-stage.mit.edu
SUMMARY:Hierarchical Bayesian Network Model for Probabilistic Estimation of EV Battery Life
DESCRIPTION:Bayesian models are applied to probabilistic analysis of phenomena which deal with multiple external stochastic factors and unmeasurable variables. Considering the large amount of available data for the EV driving\, recharging and grid services such as solar charging which contains uncertainties and measurement errors\, and their hierarchical effect on the battery life\, this application of Bayesian models can be useful for the aging probabilistic analysis. Causality is of utmost importance for batteries as their aging is affected by a high number of hierarchical variables that depend upon external factors to the battery. Acknowledging the advantages of Bayesian models\, we propose a hierarchical Bayesian model for the probabilistic battery degradation evaluation. Priors distributions are defined based on expert knowledge and Marco Chain Monte Carlo (MCMC) sampling is used to draw the posteriors. This modeling approach reflects the uncertainties of measurements and process\, provides more informative results\, and it is applicable to any type of input data with proper training. \nBio: Mehdi Jafari (Ph.D. Michigan Technological University\, 2018; M.Sc. University of Tabriz\, 2011; B.Sc. University of Tabriz\, 2008; all in Electrical Engineering) is a postdoctoral associate in the Laboratory for Information and Decision Systems (LIDS) at MIT. He is working on Energy Storage solutions for the power system applications and renewables integration. He also has worked on probabilistic analysis of the battery energy storage aging behavior\, especially in the electrified transportation and vehicle-to-grid applications. He has authored more than 30 journal and conference papers in the energy storage\, electric vehicles\, renewable energy and power system fields. His current research interests include energy storage role in renewables integration\, battery energy storage performance and degradation in power system and transportation electrification applications. \n____________________________________ \nTea talks are 20-minute-long informal chalk-talks for the purpose of sharing ideas and making others aware about some of the topics that may be of interest to the LIDS and Stats audience. If you are interested in presenting in the upcoming tea talks\, please email lids_stats_tea@mit.edu.
URL:https://lids.mit.edu/news-and-events/events/hierarchical-bayesian-network-model-probabilistic-estimation-ev-battery-life
LOCATION:32 – LIDS Lounge\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS & Stats Tea Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190423T160000
DTEND;TZID=America/New_York:20190423T170000
DTSTAMP:20260408T112347
CREATED:20190301T171624Z
LAST-MODIFIED:20190501T142253Z
UID:8987-1556035200-1556038800@idss-stage.mit.edu
SUMMARY:Memory-Efficient Adaptive Optimization for Humungous-Scale Learning
DESCRIPTION:Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each model parameter\, thus doubling the memory footprint of the optimizer. In behemoth-size applications\, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel\, simple\, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity while allowing for larger models and mini-batches. We give convergence guarantees for our method and demonstrate its effectiveness in training some of the largest deep models used at Google. \nBio: Yoram Singer is the head of Principles Of Effective Machine learning (POEM) research group in Google Brain and a professor of Computer Science at Princeton.  He was a member of the technical staff at AT&T Research from 1995 through 1999 and an associate professor at the Hebrew University from 1999 through 2007. He is a fellow of AAAI. His research on machine learning algorithms received several awards. \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-yoram-singer
LOCATION:32-G449 (KIva/Patel)
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190419T110000
DTEND;TZID=America/New_York:20190419T120000
DTSTAMP:20260408T112347
CREATED:20190204T202923Z
LAST-MODIFIED:20190430T195704Z
UID:8822-1555671600-1555675200@idss-stage.mit.edu
SUMMARY:Stochastics and Statistics Seminar Series
DESCRIPTION:Logistic regression is a fundamental task in machine learning and statistics. For the simple case of linear models\, Hazan et al. (2014) showed that any logistic regression algorithm that estimates model weights from samples must exhibit exponential dependence on the weight magnitude. As an alternative\, we explore a counterintuitive technique called improper learning\, whereby one estimates a linear model by fitting a non-linear model. Past success stories for improper learning have focused on cases where it can improve computational complexity. Surprisingly\, we show that for sample complexity (number of examples needed to achieve a desired accuracy level)\, improper learning leads to a doubly-exponential improvement in dependence on weight magnitude over estimation of model weights\, and more broadly over any so-called “proper” learning algorithm. This provides a positive resolution to a COLT 2012 open problem of McMahan and Streeter. As a consequence of this improvement\, we also resolve two open problems on the sample complexity of boosting and bandit multi-class classification. \nDylan Foster is a postdoctoral researcher at the MIT Institute for Foundations of Data Science. In 2018 he received his PhD in computer science at Cornell University\, advised by Karthik Sridharan. His research focuses on theory for machine learning in real-world settings. He is particularly interested in all aspects of generalization theory\, particularly as it applies to deep learning\, non-convex optimization\, and interactive learning problems including online and bandit learning. Dylan previously received his BS and MS in Electrical Engineering from USC in 2014. He has received awards including the NDSEG PhD fellowship\, Facebook PhD fellowship\, and best student paper award at COLT. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/dylanfoster/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190412T110000
DTEND;TZID=America/New_York:20190412T120000
DTSTAMP:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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:20260408T112347
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190205T160000
DTEND;TZID=America/New_York:20190205T170000
DTSTAMP:20260408T112347
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
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