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DTSTART;TZID=America/New_York:20190923T140000
DTEND;TZID=America/New_York:20190923T150000
DTSTAMP:20260408T131344
CREATED:20190920T150517Z
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UID:10840-1569247200-1569250800@idss-stage.mit.edu
SUMMARY:Power of Experimental Design and Active Learning
DESCRIPTION:Classical supervised machine learning algorithms focus on the setting where the algorithm has access to a fixed labeled dataset obtained prior to any analysis. In most applications\, however\, we have control over the data collection process such as which image labels to obtain\, which drug-gene interactions to record\, which network routes to probe\, which movies to rate\, etc. Furthermore\, most applications face budget limitations on the amount of labels that can be collected. Experimental design and active learning are two paradigms that involve careful selection of data points to label from a large unlabeled pool. This talk will discuss and contrast the power of experimental design and active learning\, starting with some recent advances in these paradigms and then posing open questions involving their integration and application to deep models. \nBio: Aarti Singh is an Associate Professor in the Machine Learning Department at Carnegie Mellon University. Her research lies at the intersection of machine learning\, statistics and signal processing\, and focuses on designing statistically and computationally efficient algorithms for learning from direct\, compressive and interactive queries. Her work is recognized by an NSF Career Award\, the United States Air Force Young Investigator Award\, A. Nico Habermann Junior Faculty Chair Award\, Harold A. Peterson Best Dissertation Award\, and three best student paper awards. Her service honors include serving as Program Chair for the International Conference on Machine Learning (ICML) 2020\, Program Chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference\, member of the National Academy of Sciences (NAS) Committee on Applied and Theoretical Statistics\, guest editor for Electronic Journal of Statistics\, and Associate Editor of the IEEE Transactions on Information Theory and IEEE Transactions on Signal and Information Processing over Networks. \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/power-experimental-design-and-active-learning
LOCATION:E18-304\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190920T110000
DTEND;TZID=America/New_York:20190920T120000
DTSTAMP:20260408T131344
CREATED:20190910T191447Z
LAST-MODIFIED:20191016T163208Z
UID:10670-1568977200-1568980800@idss-stage.mit.edu
SUMMARY:Some New Insights On Transfer Learning
DESCRIPTION:Abstract:  \nThe problem of transfer and domain adaptation is ubiquitous in machine learning and concerns situations where predictive technologies\, trained on a given source dataset\, have to be transferred to a new target domain that is somewhat related. For example\, transferring voice recognition trained on American English accents to apply to Scottish accents\, with minimal retraining. A first challenge is to understand how to properly model the ‘distance’ between source and target domains\, viewed as probability distributions over a feature space.\n\nIn this talk we will argue that various existing notions of distance between distributions turn out to be pessimistic\, i.e.\, these distances might appear high in many situations where transfer is possible\, even at fast rates. Instead we show that some new notions of distance tightly capture a continuum from easy to hard transfer\, and furthermore can be adapted to\, i.e.\, do not need to be estimated in order to perform near-optimal transfer. Finally we will discuss near-optimal approaches to minimizing sampling of target data (e.g. sampling Scottish speech)\, when one already has access to a given amount of source data (e.g. American speech).\n\nThis talk is based on some joint work with G. Martinet\, and ongoing work with S. Hanneke.\n\nBiography:  \nSamory Kpotufe is an Associate Professor in Statistics at Columbia University. He works in machine learning\, with an emphasis on nonparametric methods and high dimensional statistics. Generally\, his interests are in understanding basic learning scenarios under practical constraints from modern application domains. He has previously held positions at the Max Planck Institute in Germany\, the Toyota Technological Institute at Chicago\, and Princeton University. \nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/some-new-insights-on-transfer-learning/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190918T160000
DTEND;TZID=America/New_York:20190918T170000
DTSTAMP:20260408T131344
CREATED:20190916T194901Z
LAST-MODIFIED:20190916T194901Z
UID:10702-1568822400-1568826000@idss-stage.mit.edu
SUMMARY:Probabilistic Modeling meets Deep Learning using TensorFlow Probability
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \nSpeaker: \nBrian Patton (Google AI) \nAbstract: \nTensorFlow Probability provides a toolkit to enable\nresearchers and practitioners to integrate uncertainty with\ngradient-based deep learning on modern accelerators. In this talk\nwe’ll walk through some practical problems addressed using TFP;\ndiscuss the high-level interfaces\, goals\, and principles of the\nlibrary; and touch on some recent innovations in describing\nprobabilistic graphical models. Time-permitting\, we may touch on a\ncouple areas of research interest for the team.\n\n–\n\n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes.  For more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/\n \n**Meetings are open to any interested researcher.
URL:https://stat.mit.edu/calendar/probabilistic-modeling-meets-deep-learning-using-tensorflow-probability/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190916T160000
DTEND;TZID=America/New_York:20190916T170000
DTSTAMP:20260408T131344
CREATED:20190920T150317Z
LAST-MODIFIED:20190920T150317Z
UID:10838-1568649600-1568653200@idss-stage.mit.edu
SUMMARY:Dynamic Monitoring and Decision Systems (DyMonDS) Framework for Data-Enabled Integration in Complex Electric Energy Systems
DESCRIPTION:In this talk\, we introduce a unifying Dynamic Monitoring and Decision Systems (DyMonDS) framework that is based on multi-layered modeling for aggregation and minimal coordination of interactions between the layers of complex electric energy systems. Using this approach\, distributed control and optimization problems are formulated so that: (1) the low-level decision-makers optimize cost of local interactions while accounting for their heterogeneous technologies\, as well as for their social and risk preferences; and\, (2) the higher layer aggregators and coordinators optimize the cost of all interactions at their levels to enable cooperative control. The interactions of each layer are abstracted by using unifying energy state space and the Lagrange coefficients associated with the general physical laws. This sets the bases for both nonlinear control of power electronically-switched automation and for market design formulation. Potential benefits (such as enhanced reliability\, resiliency\, and efficiency) from integrating flexible technologies\, storage\, and control\, in particular\, are illustrated on simple IEEE test systems. \nBio: Marija Ilić has retired as a Professor Emerita at Carnegie Mellon University. She is currently a Senior Staff in the Energy Systems Group 73 at the MIT Lincoln Laboratory. She is also a Senior Research Scientist at MIT in LIDS and IDSS. She is an IEEE Life Fellow. She was the first recipient of the NSF Presidential Young Investigator Award for Power Systems. In addition to her academic work\, she has gained considerable industry experience as the founder of New Electricity Transmission Software Solutions\, Inc. (NETSS\, Inc.). She has co-authored several books on the subject of large-scale electric power systems and has co-organized an annual multidisciplinary Electricity Industry conference series at Carnegie Mellon with participants from academia\, government\, and industry. She was the founder and co-director of the Electric Energy Systems Group (EESG) at Carnegie Mellon University. Currently\, she is building EESG@MIT\, in the same spirit as EESG@CMU. \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/dynamic-monitoring-and-decision-systems-dymonds-framework-data-enabled
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190911T160000
DTEND;TZID=America/New_York:20190911T170000
DTSTAMP:20260408T131344
CREATED:20190910T184518Z
LAST-MODIFIED:20190910T190807Z
UID:10666-1568217600-1568221200@idss-stage.mit.edu
SUMMARY:Automated Data Summarization for Scalability in Bayesian Inference
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \nAbstract: \nMany algorithms take prohibitively long to run on modern\, large datasets. But even in complex data sets\, many data points may be at least partially redundant for some task of interest. So one might instead construct and use a weighted subset of the data (called a “coreset”) that is much smaller than the original dataset. Typically running algorithms on a much smaller data set will take much less computing time\, but it remains to understand whether the output can be widely useful. (1) In particular\, can running an analysis on a smaller coreset yield answers close to those from running on the full data set? (2) And can useful coresets be constructed automatically for new analyses\, with minimal extra work from the user? We answer in the affirmative for a wide variety of problems in Bayesian inference. We demonstrate how to construct “Bayesian coresets” as an automatic\, practical pre-processing step. We prove that our method provides geometric decay in relevant approximation error as a function of coreset size. Empirical analysis shows that our method reduces approximation error by orders of magnitude relative to uniform random subsampling of data. Though we focus on Bayesian methods here\, we also show that our construction can be applied in other domains. \nBiography: \nTamara Broderick is an Associate Professor in EECS at MIT. \n**Meetings are open to any interested researcher.  \n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes.  For more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \n 
URL:https://stat.mit.edu/calendar/automated-data-summarization-for-scalability-in-bayesian-inference/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190906T110000
DTEND;TZID=America/New_York:20190906T120000
DTSTAMP:20260408T131344
CREATED:20190903T150512Z
LAST-MODIFIED:20190903T152812Z
UID:10580-1567767600-1567771200@idss-stage.mit.edu
SUMMARY:GANs\, Optimal Transport\, and Implicit Density Estimation
DESCRIPTION:Abstract:  \nWe first study the rate of convergence for learning distributions with the adversarial framework and Generative Adversarial Networks (GANs)\, which subsumes Wasserstein\, Sobolev\, and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions\, under a collection of objective evaluation metrics. On the nonparametric end\, we investigate the minimax optimal rates and fundamental difficulty of the implicit density estimation under the adversarial framework. On the parametric end\, we establish a theory for general neural network classes\, that characterizes the interplay on the choice of generator and discriminator. We investigate how to obtain a good statistical guarantee for GANs through the lens of regularization. We discover and isolate a new notion of regularization\, called the generator/discriminator pair regularization\, that sheds light on the advantage of GANs compared to classical approaches for density estimation. We develop novel oracle inequalities as the main tools for analyzing GANs\, which is of independent theoretical interest. \nLater\, we proceed to discuss optimal transport\, estimating under the Wasserstein metric\, and how to use them for implicit density estimation. We will point out an interesting connection between pair regularization and optimal transport.\n\n\nBiography: \nDr. Liang is an assistant professor at Chicago Booth. He is also the George C. Tiao faculty fellow in data science research. His current research interests include computational and algorithmic aspects of statistical inference\, machine learning and statistical learning theory\, stochastic methods in non-convex optimization. \nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/liang/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190530
DTEND;VALUE=DATE:20190601
DTSTAMP:20260408T131344
CREATED:20190502T161055Z
LAST-MODIFIED:20190502T161155Z
UID:9592-1559174400-1559347199@idss-stage.mit.edu
SUMMARY:Learning for Dynamics and Control (L4DC)
DESCRIPTION:Over the next decade\, the biggest generator of data is expected to be devices which sense and control the physical world. This explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning\, control theory\, and optimization. While control theory has been firmly rooted in tradition of model-based design\, the availability and scale of data (both temporal and spatial) will require rethinking of the foundations of our discipline. From a machine learning perspective\, one of the main challenges going forward is to go beyond pattern recognition and address problems in data driven control and optimization of dynamical processes. Our overall goal is to create a new community of people that think rigorously across the disciplines\, asks new questions\, and develops the foundations of this new scientific area.
URL:https://l4dc.mit.edu/
LOCATION:32-123\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190520
DTEND;VALUE=DATE:20190522
DTSTAMP:20260408T131344
CREATED:20190417T144745Z
LAST-MODIFIED:20190417T145145Z
UID:9389-1558310400-1558483199@idss-stage.mit.edu
SUMMARY:Conference on Synthetic Controls and Related Methods
DESCRIPTION:Organizers are Alberto Abadie (MIT)\, Victor Chernozhukov (MIT)\, and Guido Imbens (Stanford University). The program is posted here. \nParticipation by invitation only.
URL:https://idss-stage.mit.edu/calendar/conference-on-synthetic-controls-and-related-methods/
LOCATION:E18-304\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190514T160000
DTEND;TZID=America/New_York:20190514T170000
DTSTAMP:20260408T131344
CREATED:20190301T172026Z
LAST-MODIFIED:20190501T142034Z
UID:8991-1557849600-1557853200@idss-stage.mit.edu
SUMMARY:Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery
DESCRIPTION:The overarching goal of my research is to develop cutting-edge machine learning\, AI and operations research theory\, methods\, algorithms\, and systems to understand the basis of health and disease; develop methodology to catalyze clinical research; support clinical decisions through individualized medicine; inform clinical pathways\, better utilize resources & reduce costs; and inform public health. \nTo do this\, Prof. van der Schaar is creating what she calls Learning Engines for Healthcare (LEH’s). An LEH is an integrated ecosystem that uses machine learning\, AI and operations research to provide clinical insights and healthcare intelligence to all the stakeholders (patients\, clinicians\, hospitals\, administrators). In contrast to an Electronic Health Record\, which provides a static\, passive\, isolated display of information\, an LEH provides a dynamic\, active\, holistic & individualized display of information including alerts. \nIn this talk Prof. van der Schaar will focus on 3 steps in the development of LEH’s: \n\nBuilding a comprehensive model that accommodates irregularly sampled\, temporally correlated\, informatively censored and non-stationary processes in order to understand and predict the longitudinal trajectories of diseases.\nEstablishing the theoretical limits of causal inference and using what has been established to create a new approach that makes it possible to better estimate individualized treatment effects.\nUsing Machine Learning itself to automate the design and construction of entire pipelines of Machine Learning algorithms for risk prediction\, screening\, diagnosis\, and prognosis.\n\nBio: Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning\, Artificial Intelligence\, and Medicine at the University of Cambridge\, a Turing Faculty Fellow at The Alan Turing Institute in London\, where she leads the effort on data science and machine learning for personalized medicine. Prior to this\, she was a Chancellor’s Professor at UCLA and MAN Professor of Quantitative Finance at the University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018).  She has also been the recipient of an NSF Career Award\, 3 IBM Faculty Awards\, the IBM Exploratory Stream Analytics Innovation Award\, the Philips Make a Difference Award and several best paper awards\, including the IEEE Darlington Award. She holds 35 granted USA patents. Her current research focus is on data science\, machine learning\, AI and operations research for medicine. \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-mihaela-van-der-schaar
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190513
DTEND;VALUE=DATE:20190514
DTSTAMP:20260408T131344
CREATED:20190328T163321Z
LAST-MODIFIED:20190328T163321Z
UID:9178-1557705600-1557791999@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data-Driven Decisions
DESCRIPTION:Developed by 11 MIT faculty members at IDSS\, this seven-week course is specially designed for data scientists\, business analysts\, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins May\, 13\, 2019.
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+2T2019/about?utm_medium=website&utm_source=idss&utm_campaign=ds-su19&utm_content=event-calendar
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190510T080000
DTEND;TZID=America/New_York:20190510T170000
DTSTAMP:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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:20260408T131344
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20260408T131344
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
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