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DTSTART;TZID=America/New_York:20190927T110000
DTEND;TZID=America/New_York:20190927T120000
DTSTAMP:20260517T051559
CREATED:20190923T172454Z
LAST-MODIFIED:20191016T163112Z
UID:10858-1569582000-1569585600@idss-stage.mit.edu
SUMMARY:Frontiers of Efficient Neural-Network Learnability
DESCRIPTION:Abstract:  \nWhat are the most expressive classes of neural networks that can be learned\, provably\, in polynomial-time in a distribution-free setting? In this talk we give the first efficient algorithm for learning neural networks with two nonlinear layers using tools for solving isotonic regression\, a nonconvex (but tractable) optimization problem. If we further assume the distribution is symmetric\, we obtain the first efficient algorithm for recovering the parameters of a one-layer convolutional network. These results implicitly make use of a convex surrogate loss for generalized linear models and go beyond the kernel-method/overparameterization regime used in recent works.\n\nBiography:  \nAdam Klivans is a professor of computer science at the University of Texas at Austin who works in theoretical computer science and machine learning. He completed his doctorate in mathematics from MIT\, where he was awarded the Charles W. and Jennifer C. Johnson Prize. \nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/frontiers/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190920T110000
DTEND;TZID=America/New_York:20190920T120000
DTSTAMP:20260517T051559
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:20190906T110000
DTEND;TZID=America/New_York:20190906T120000
DTSTAMP:20260517T051559
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;TZID=America/New_York:20190510T080000
DTEND;TZID=America/New_York:20190510T170000
DTSTAMP:20260517T051559
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:20190503T110000
DTEND;TZID=America/New_York:20190503T120000
DTSTAMP:20260517T051559
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:20190426T110000
DTEND;TZID=America/New_York:20190426T120000
DTSTAMP:20260517T051559
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:20190419T110000
DTEND;TZID=America/New_York:20190419T120000
DTSTAMP:20260517T051559
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:20260517T051559
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:20190322T110000
DTEND;TZID=America/New_York:20190322T120000
DTSTAMP:20260517T051559
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:20260517T051559
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:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20260517T051559
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:20190301T110000
DTEND;TZID=America/New_York:20190301T120000
DTSTAMP:20260517T051559
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:20190222T110000
DTEND;TZID=America/New_York:20190222T120000
DTSTAMP:20260517T051559
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:20190215T110000
DTEND;TZID=America/New_York:20190215T120000
DTSTAMP:20260517T051559
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:20260517T051559
CREATED:20190128T195456Z
LAST-MODIFIED:20190128T195657Z
UID:8782-1549623600-1549627200@idss-stage.mit.edu
SUMMARY:Medical Image Imputation
DESCRIPTION:Abstract: \nWe present an algorithm for creating high resolution anatomically\nplausible images that are consistent with acquired clinical brain MRI\nscans with large inter-slice spacing. Although large databases of\nclinical images contain a wealth of information\, medical acquisition\nconstraints result in sparse scans that miss much of the\nanatomy. These characteristics often render computational analysis\nimpractical as standard processing algorithms tend to fail when\napplied to such images. Our goal is to enable application of existing\nalgorithms that were originally developed for high resolution research\nscans to severely undersampled images. We illustrate the applications\nof the method in the context of neurodegeneration and white matter\ndisease studies in stroke patients. \nBiography:\nPolina Golland is a Henry Ellis Warren (1894) professor of Electrical\nEngineering and Computer Science at MIT and a principal investigator\nin the MIT Computer Science and Artificial Intelligence Laboratory\n(CSAIL). She received her PhD in 2001 from MIT and her Bachelor and\nMasters degrees in 1993 and 1995 from Technion\, Israel. Polina’s\nprimary research interest is in developing novel techniques for\nmedical image analysis and understanding. With her students\, Polina\nhas demonstrated novel approaches to image segmentation\, shape\nanalysis\, functional image analysis and population studies. She has\nserved as an associate editor of the IEEE Transactions on Medical\nImaging and of the IEEE Transactions on Pattern Analysis. Polina is\ncurrently on the editorial board of the Journal of Medical Image\nAnalysis. She is a Fellow of the International Society for Medical\nImage Computing and Computer Assisted Interventions. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/medical-image-imputation/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190201T110000
DTEND;TZID=America/New_York:20190201T120000
DTSTAMP:20260517T051559
CREATED:20190128T171951Z
LAST-MODIFIED:20190128T192955Z
UID:8777-1549018800-1549022400@idss-stage.mit.edu
SUMMARY:Optimization of the Sherrington-Kirkpatrick Hamiltonian
DESCRIPTION:Andrea Montanari\nProfessor\, Department of Electrical Engineering\, Department of Statistics Stanford University \nThis lecture is in conjunction with the LIDS Student Conference. \nAbstract: Let A be n × n symmetric random matrix with independent and identically distributed Gaussian entries above the diagonal. We consider the problem of maximizing xT Ax over binary vectors with ±1 entries. In the language of statistical physics\, this amounts to finding the ground state of the Sherrington-Kirkpatrick model of spin glasses. The asymptotic value of this optimization problem was characterized by Parisi via a celebrated variational principle\, subsequently proved by Talagrand. We give an algorithm that\, for any > 0\, outputs a feasible solution that is at least 1 − of the optimum value\, with probability converging to one as n goes to infinity. The algorithm’s time complexity is 0(n2). It is a message-passing algorithm\, but the specific structure of its update rules is new. As a side result\, we prove that\, at (low) non-zero temperature\, the algorithm constructs approximate solutions of the celebrated Thouless-Anderson-Palmer equations. \nBiography: \nAndrea Montanari received a Laurea degree in Physics in 1997\, and a Ph. D. in Theoretical Physics in 2001 (both from Scuola Normale Superiore in Pisa\, Italy). He has been post-doctoral fellow at Laboratoire de Physique Théorique de l’Ecole Normale Supérieure (LPTENS)\, Paris\, France\, and the Mathematical Sciences Research Institute\, Berkeley\, USA. Since 2002 he is Chargé de Recherche (with Centre National de la Recherche Scientifique\, CNRS) at LPTENS. In September 2006 he joined Stanford University as a faculty\, and since 2015 he is Full Professor in the Departments of Electrical Engineering and Statistics. \nHe was co-awarded the ACM SIGMETRICS best paper award in 2008. He received the CNRS bronze medal for theoretical physics in 2006\, the National Science Foundation CAREER award in 2008\, the Okawa Foundation Research Grant in 2013\, and the Applied Probability Society Best Publication Award in 2015. He is an Information Theory Society distinguished lecturer for 2015-2016. In 2016 he received the James L. Massey Research & Teaching Award of the Information Theory Society for young scholars. In 2018 he was an invited sectional speaker at the International Congress of Mathematicians. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/andrea-montanari/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181214T110000
DTEND;TZID=America/New_York:20181214T120000
DTSTAMP:20260517T051559
CREATED:20180621T193833Z
LAST-MODIFIED:20181204T175526Z
UID:7926-1544785200-1544788800@idss-stage.mit.edu
SUMMARY:Large girth approximate Steiner triple systems
DESCRIPTION:Abstract:  In 1973 Erdos asked whether there are n-vertex partial Steiner triple systems with arbitrary high girth and quadratically many triples. (Here girth is defined as the smallest integer g \ge 4 for which some g-element vertex-set contains at least g-2 triples.) \nWe answer this question\, by showing existence of approximate Steiner triple systems with arbitrary high girth. More concretely\, for any fixed \ell \ge 4 we show that a natural constrained random process typically produces a partial Steiner triple system with (1/6-o(1))n^2 triples and girth larger than \ell. The process iteratively adds random triples subject to the constraint that the girth remains larger than \ell. Our result is best possible up to the o(1)-term\, which is a negative power of n. \nJoint work with Tom Bohman.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-22/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181207T110000
DTEND;TZID=America/New_York:20181207T120000
DTSTAMP:20260517T051559
CREATED:20180621T193538Z
LAST-MODIFIED:20181130T173240Z
UID:7924-1544180400-1544184000@idss-stage.mit.edu
SUMMARY:Reducibility and Computational Lower Bounds for Some High-dimensional Statistics Problems
DESCRIPTION:Abstract: The prototypical high-dimensional statistics problem entails finding a structured signal in noise. Many of these problems exhibit an intriguing phenomenon: the amount of data needed by all known computationally efficient algorithms far exceeds what is needed for inefficient algorithms that search over all possible structures. A line of work initiated by Berthet and Rigollet in 2013 has aimed to explain these gaps by reducing from conjecturally hard problems in computer science. However\, the delicate nature of average-case reductions has limited the applicability of this approach. In this work we introduce several new techniques to give a web of average-case reductions showing strong computational lower bounds based on the planted clique conjecture. These include tight lower bounds for Planted Independent Set\, Planted Dense Subgraph\, Biclustering\, Sparse Spiked Wigner\, Sparse PCA\, as well as for new models we introduce. Joint work with Matthew Brennan and Wasim Huleihel. \n Bio:  Guy Bresler is an assistant professor in the Department of Electrical Engineering and Computer Science at MIT\, and a member of LIDS and IDSS.\nPreviously\, he was a postdoc at MIT and before that received his PhD from the Department of EECS at UC Berkeley.\nHe seeks to obtain engineering insight into practically relevant problems by formulating and solving mathematical models. Concretely\, he wants to understand the relationship between combinatorial structure and computational tractability of high-dimensional inference in the context of graphical models and other statistical models\, recommendation systems\, and biology.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-21/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181130T110000
DTEND;TZID=America/New_York:20181130T120000
DTSTAMP:20260517T051559
CREATED:20180621T193346Z
LAST-MODIFIED:20181121T171911Z
UID:7922-1543575600-1543579200@idss-stage.mit.edu
SUMMARY:Bias Reduction and Asymptotic Efficiency in Estimation of Smooth Functionals of High-Dimensional Covariance
DESCRIPTION:Abstract:  We discuss a recent approach to bias reduction in a problem of estimation of smooth functionals of high-dimensional parameters of statistical models. In particular\, this approach has been developed in the case of estimation of functionals of covariance operator Σ : Rd → Rd of the form f(Σ)\, B based on n i.i.d. observations X1\, . . . \, Xn sampled from the normal distribution with mean zero and covariance Σ\, f : R → R being a sufficiently smooth\nfunction and B being an operator with nuclear norm bounded by a constant. This includes such problems as estimation of bilinear forms (for instance\, matrix entries in a given basis) of spectral projections of unknown covari-ance that are of importance in principal component analysis. A “bootstrap chain” bias reduction method\, based on an approximate solution of a certain integral equation (the Wishart equation) on the cone of self-adjoint positive semidefinite operators\, yields asymptotically efficient estimators of the func-tional f(Σ)\, B under proper assumptions on the growth of dimension d and smoothness of function f. In particular\, this holds under the assumption that d ≤ nα for some α ∈ (0\, 1) and that f belongs to a Besov space Bs∞\,1(R) for s > 1 . The proof of asymptotic efficiency relies on a number of probabilistic and analytic tools (operator differentiability; Gaussian concentration; properties of Wishart operators and orthogonally invariant functions on the cone of positive semidefinite operators; information-theoretic lower bounds).\n Biography:  Vladimir Koltchinskii is a professor in Mathematics at Georgia Tech. His current research is primarily in high-dimensional statistics and probability.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-20/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181116T110000
DTEND;TZID=America/New_York:20181116T120000
DTSTAMP:20260517T051559
CREATED:20180621T193041Z
LAST-MODIFIED:20181108T190207Z
UID:7920-1542366000-1542369600@idss-stage.mit.edu
SUMMARY:Model-X knockoffs for controlled variable selection in high dimensional nonlinear regression
DESCRIPTION:Abstract:  Many contemporary large-scale applications\, from genomics to advertising\, involve linking a response of interest to a large set of potential explanatory variables in a nonlinear fashion\, such as when the response is binary. Although this modeling problem has been extensively studied\, it remains unclear how to effectively select important variables while controlling the fraction of false discoveries\, even in high-dimensional logistic regression\, not to mention general high-dimensional nonlinear models. To address such a practical problem\, we propose a new framework of model-X knockoffs\, which reads from a different perspective the knockoff procedure (Barber and Candès\, 2015) originally designed for controlling the false discovery rate in low-dimensional linear models. Model-X knockoffs can deal with arbitrary (and unknown) conditional models and any dimensions\, including when the number of explanatory variables p exceeds the sample size n. Our approach requires the design matrix be random (independent and identically distributed rows) with a known distribution for the explanatory variables\, although we show preliminary evidence that our procedure is robust to unknown/estimated distributions. As we require no knowledge/assumptions about the conditional distribution of the response\, we effectively shift the burden of knowledge from the response to the explanatory variables\, in contrast to the canonical model-based approach which assumes a parametric model for the response but very little about the explanatory variables. To our knowledge\, no other procedure solves the controlled variable selection problem in such generality\, but in the restricted settings where competitors exist\, we demonstrate the superior power of knockoffs through simulations. We also apply our procedure to data from a case-control study of Crohn’s disease in the United Kingdom\, making twice as many discoveries as the original analysis of the same data. \n Biography:  Lucas Janson is an Assistant Professor in the Department of Statistics at Harvard University\, where he works on high-dimensional inference\, autonomous robotic motion planning\, and statistical machine learning. Prior to Harvard\, he was a PhD student in Statistics at Stanford University advised by Professor Emmanuel Candès.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-19/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181109T110000
DTEND;TZID=America/New_York:20181109T120000
DTSTAMP:20260517T051559
CREATED:20180621T192716Z
LAST-MODIFIED:20181107T151946Z
UID:7918-1541761200-1541764800@idss-stage.mit.edu
SUMMARY:Optimal hypothesis testing for stochastic block models with growing degrees
DESCRIPTION:Abstract:  In this talk\, we discuss optimal hypothesis testing for distinguishing a stochastic block model from an Erdos–Renyi random graph when the average degree grows to infinity with the graph size. We show that linear spectral statistics based on Chebyshev polynomials of the adjacency matrix can approximate signed cycles of growing lengths when the graph is sufficiently dense. The signed cycles have been shown by Banerjee (2018) to determine the likelihood ratio statistic asymptotically. In this way one achieves sharp asymptotic optimal power of the testing problem within polynomial time complexity. Time permitting\, we will also discuss how linear spectral statistics of a weighted non-backtracking matrix can be used to approximate the likelihood ratio. The talk is based on joint work with Debapratim Banerjee. \n Biography:  Dr.Zongming Ma is an Associate Professor of Statistics of the Wharton School at the University of Pennsylvania. He received his PhD in Statistics from Stanford University in 2010 and has since then been on the faculty of the Wharton Statistics Department. Dr.Ma’s research interests include high-dimensional statistical inference\, non-parametric statistics\, network data analysis\, and their applications in biomedical data analysis. He is a recipient of a Sloan Research Fellowship and an NSF CAREER Award.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-18/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181102T110000
DTEND;TZID=America/New_York:20181102T120000
DTSTAMP:20260517T051559
CREATED:20180621T192450Z
LAST-MODIFIED:20181029T150448Z
UID:7916-1541156400-1541160000@idss-stage.mit.edu
SUMMARY:Joint estimation of parameters in Ising Model
DESCRIPTION:Abstract:  Inference in the framework of Ising models has received significant attention in Statistics and Machine Learning in recent years. In this talk we study joint estimation of the inverse temperature parameter β\, and the magnetization parameter B\, given one realization from the Ising model\, under the assumption that the underlying graph of the Ising model is completely specified. We show that if the graph is either irregular or sparse\, then both the parameters can be estimated at rate n−1/2  using Besag’s pseudo-likelihood. Conversely\, if the underlying graph is dense and regular\, we show that no consistent estimates exist for (β\, B).\nThis is joint work with Promit Ghosal from Columbia University. \n Biography:  Sumit is currently an Assistant Professor in the Statistics Department at Columbia. Prior to this\, he received his PhD in Statistics at Stanford\, under the guidance of Persi Diaconis.\nHis research interests lie in the intersection of Theoretical Statistics and Applied Probability. In Statistics\, his main focus is developing inferential procedures on probability distributions on combinatorial spaces\, such as permutations\, graphs\, and spin configurations. On the probability side\, his main focus is studying persistence of stochastic processes\, and graph coloring problems.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-17/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181026T110000
DTEND;TZID=America/New_York:20181026T120000
DTSTAMP:20260517T051559
CREATED:20180621T192221Z
LAST-MODIFIED:20180626T142057Z
UID:7914-1540551600-1540555200@idss-stage.mit.edu
SUMMARY:Alan Frieze
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-16/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181019T110000
DTEND;TZID=America/New_York:20181019T120000
DTSTAMP:20260517T051559
CREATED:20180621T191820Z
LAST-MODIFIED:20181010T202034Z
UID:7912-1539946800-1539950400@idss-stage.mit.edu
SUMMARY:Algorithmic thresholds for tensor principle component analysis
DESCRIPTION:Abstract:  Consider the problem of recovering a rank 1 tensor of order k that has been subject to Gaussian noise. The log-likelihood for this problem is highly non-convex. It is information theoretically possible to recover the tensor with a finite number of samples via maximum likelihood estimation\, however\, it is expected that one needs a polynomially diverging number of samples to efficiently recover it. What is the cause of this large statistical–to–algorithmic gap? To study this question\, we investigate the thresholds for efficient recovery for a simple family of algorithms\, Langevin dynamics and gradient descent. We view this problem as a member of a broader class of problems which correspond to recovering a signal from a non-linear observation that has been perturbed by isotropic Gaussian noise. We propose a mechanism for success/failure of recovery of such algorithms in terms of the strength of the signal on the high entropy region of the initialization. Joint work with G. Ben Arous (NYU) and R. Gheissari (NYU). \n Biography:  Aukosh Jagannath is a Benjamin Pierce Fellow and NSF Postdoctoral fellow at Harvard University with undergraduate and graduate degree from NYU. He works in probability at the interface of statistical physics\, data science\, combinatorics\, and statistics.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-15/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181012T110000
DTEND;TZID=America/New_York:20181012T120000
DTSTAMP:20260517T051559
CREATED:20180621T191525Z
LAST-MODIFIED:20181004T134355Z
UID:7910-1539342000-1539345600@idss-stage.mit.edu
SUMMARY:Locally private estimation\, learning\, inference\, and optimality
DESCRIPTION:Abstract: In this talk\, we investigate statistical learning and estimation under local privacy constraints\, where data providers do not trust the collector of the data and so privatize their data before it is even collected. We identify fundamental tradeoffs between statistical utility and privacy in such local models of privacy\, providing instance-specific bounds for private estimation and learning problems by developing local minimax risks. In contrast to approaches based on worst-case (minimax) error\, which are conservative\, this allows us to evaluate the difficulty of individual problem instances and delineate the possibilities for adaptation in private estimation and inference. As part of this\, we identify an alternative to the Fisher information for private estimation\, giving a more nuanced understanding of the challenges of adaptivity and optimality. We also provide optimal procedures for private inference\, highlighting the importance of a more careful development of optimal tradeoffs between estimation and privacy. One consequence of our results is to identify settings where standard local privacy restrictions may be too strong for practice; time permitting\, I will then discuss a few new directions that maintain limited amounts of privacy while simultaneously allowing the development of high-performance statistical and learning procedures.\nBased on joint work with Feng Ruan.\n\nBiography: John Duchi is an assistant professor of Statistics and Electrical Engineering and (by courtesy) Computer Science at Stanford University\, with graduate degrees from UC Berkeley and undergraduate degrees from Stanford. His work focuses on large scale optimization problems arising out of statistical and machine learning problems\, robustness and uncertain data problems\, and information theoretic aspects of statistical learning. He has won a number of awards and fellowships\, including best paper awards at the Neural Information Processing Systems conference\, the International Conference on Machine Learning\, an NSF CAREER award\, a Sloan Fellowship in Mathematics\, the Okawa Foundation Award\, and the Association for Computing Machinery (ACM) Doctoral Dissertation Award (honorable mention).
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-14/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181005T110000
DTEND;TZID=America/New_York:20181005T120000
DTSTAMP:20260517T051559
CREATED:20180621T191105Z
LAST-MODIFIED:20181004T152601Z
UID:7908-1538737200-1538740800@idss-stage.mit.edu
SUMMARY:Efficient Algorithms for the Graph Matching Problem in Correlated Random Graphs
DESCRIPTION:Abstract:  The Graph Matching problem is a robust version of the Graph Isomorphism problem: given two not-necessarily-isomorphic graphs\, the goal is to find a permutation of the vertices which maximizes the number of common edges. We study a popular average-case variant; we deviate from the common heuristic strategy and give the first quasi-polynomial time algorithm\, where previously only sub-exponential time algorithms were known.\nBased on joint work with Boaz Barak\, Chi-Ning Chou\, Zhixian Lei\, and Yueqi Sheng.\n\n\nBiography: Tselil Schramm is a postdoc in theoretical computer science at Harvard and MIT\, hosted by Boaz Barak\, Jon Kelner\, Ankur Moitra\, and Pablo Parrilo. She obtained her PhD in computer science from U.C. Berkeley under the advisement of Prasad Raghavendra and Satish Rao. Her research interests include inference and average-case problems\, optimization via convex programs (especially the sum-of-squares hierarchy)\, spectral algorithms\, spectral graph theory\, and more.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-13/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180928T110000
DTEND;TZID=America/New_York:20180928T120000
DTSTAMP:20260517T051559
CREATED:20180621T194142Z
LAST-MODIFIED:20180919T020411Z
UID:7928-1538132400-1538136000@idss-stage.mit.edu
SUMMARY:Jingbo Liu
DESCRIPTION:Abstract  Concentration of measure refers to a collection of tools and results from analysis and probability theory that have been used in many areas of pure and applied mathematics. Arguably\, the first data science application of measure concentration (under the name ‘‘blowing-up lemma’’) is the proof of strong converses in multiuser information theory by Ahlswede\, G’acs and K”orner in 1976. Since then\, measure concentration has found applications in many other information theoretic problems\, most notably the converse (impossibility) results in information theory. Motivated by this\, information theorists (e.g. Marton) have also contributed to the mathematical foundations of measure concentration using their information-theoretic techniques. \nNow\, after all the past 40 years of such progress\, we found that\, amusingly\, measure concentration is not the right hammer for many of these information theoretic applications. We introduce a new machinery based on functional inequalities and reverse hypercontractivity which yields strict improvements in terms of sharpness of the bounds\, generality of the source/channel distributions\, and simplicity of the proofs. Examples covered in the talk include: 1. optimal second-order converses to distributed source-type problems (hypothesis testing\, common randomness generation\, and source coding); 2. sharpening the recent relay channel converse bounds by Wu and Ozgur with much simpler proofs. \nThe work benefited from collaborations with Thomas Courtade\, Paul Cuff\, Ayfer Ozgur\, Ramon van Handel\, and Sergio Verd’u \n Biography:  jingbo Liu received the B.E. degree from Tsinghua University\, Beijing\, China in 2012\, and the M.A. and Ph.D. degrees from Princeton University\, Princeton\, NJ\, USA\, in 2014 and 2018\, all in electrical engineering. His research interests include signal processing\, information theory\, coding theory\, high dimensional statistics\, and the related fields. His undergraduate thesis received the best undergraduate thesis award at Tsinghua University (2012). He gave a semi-plenary presentation at the 2015 IEEE Int. Symposium on Information Theory\, Hong-Kong\, China. He was a recipient of the Princeton University Wallace Memorial Honorific Fellowship in 2016.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-12/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180921T110000
DTEND;TZID=America/New_York:20180921T120000
DTSTAMP:20260517T051559
CREATED:20180621T184900Z
LAST-MODIFIED:20180626T141436Z
UID:7904-1537527600-1537531200@idss-stage.mit.edu
SUMMARY:Boaz Nadler
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-11/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180914T110000
DTEND;TZID=America/New_York:20180914T120000
DTSTAMP:20260517T051559
CREATED:20180621T184405Z
LAST-MODIFIED:20180914T154952Z
UID:7902-1536922800-1536926400@idss-stage.mit.edu
SUMMARY:An Information-Geometric View of Learning in High Dimensions
DESCRIPTION:Abstract: We consider the problem of data feature selection prior to inference task specification\, which is central to high-dimensional learning. Introducing natural notions of universality for such problems\, we show a local equivalence among them. Our analysis is naturally expressed via information geometry\, and represents a conceptually and practically useful learning methodology. The development reveals the key roles of the singular value decomposition\, Hirschfeld-Gebelein-Renyi maximal correlation\, canonical correlation and principle component analyses\, Tishby’s information bottleneck\, Wyner’s common information\, Ky Fan k-norms\, and Brieman and Friedman’s alternating conditional expectation algorithm. As we’ll discuss\, this framework provides a basis for understanding and optimizing aspects of learning systems\, including neural network architectures\, matrix factorization methods for collaborative filtering\, rank-constrained multivariate linear regression\, and semi-supervised learning\, among others.\nJoint work with Shao-Lun Huang\, Anuran Makur\, and Lizhong Zheng\n\n Biography: Gregory W. Wornell received the B.A.Sc. degree (with honors) from the University of British Columbia\, Canada\, and the S.M. and Ph.D. degrees from the Massachusetts Institute of Technology\, all in Electrical Engineering and Computer Science\, in 1985\, 1987 and 1991\, respectively.\nHis research interests and publications span the areas of signal processing\, information theory\, statistical inference\, digital communication\, and information security\, and include architectures for sensing\, learning\, computing\, communication\, and storage; systems for computational imaging\, vision\, and perception; aspects of computational biology and neuroscience; and the design of wireless networks. He has been involved in the Information Theory and Signal Processing societies of the IEEE in a variety of capacities\, and maintains a number of close industrial relationships and activities. He has won a number of awards for both his research and teaching\, including the IEEE Leon K. Kirchmayer Graduate Teaching Award\, and is a Fellow of the IEEE.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-10/
LOCATION:32-155\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180907T110000
DTEND;TZID=America/New_York:20180907T120000
DTSTAMP:20260517T051559
CREATED:20180621T183631Z
LAST-MODIFIED:20180626T140733Z
UID:7874-1536318000-1536321600@idss-stage.mit.edu
SUMMARY:Dejan Slepcev
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-9/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
END:VCALENDAR