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DTSTART;TZID=America/New_York:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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:20260517T061410
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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180525T110000
DTEND;TZID=America/New_York:20180525T120000
DTSTAMP:20260517T061410
CREATED:20180510T154321Z
LAST-MODIFIED:20180801T190714Z
UID:7554-1527246000-1527249600@idss-stage.mit.edu
SUMMARY:Fitting a putative manifold to noisy data
DESCRIPTION:Abstract: We give a solution to the following question from manifold learning.\nSuppose data belonging to a high dimensional Euclidean space is drawn independently\, identically distributed from a measure supported on a low dimensional twice differentiable embedded compact manifold M\, and is corrupted by a small amount of i.i.d gaussian noise. How can we produce a manifold $M_o$ whose Hausdorff distance to M is small and whose reach (normal injectivity radius) is not much smaller than the reach of M?\nThis is joint work with Charles Fefferman\, Sergei Ivanov\, Yaroslav Kurylev\, and Matti Lassas.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-8/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180511T110000
DTEND;TZID=America/New_York:20180511T120000
DTSTAMP:20260517T061410
CREATED:20171215T163823Z
LAST-MODIFIED:20180801T190611Z
UID:7150-1526036400-1526040000@idss-stage.mit.edu
SUMMARY:Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
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-3/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180504T110000
DTEND;TZID=America/New_York:20180504T120000
DTSTAMP:20260517T061410
CREATED:20171215T163500Z
LAST-MODIFIED:20180801T190448Z
UID:7148-1525431600-1525435200@idss-stage.mit.edu
SUMMARY:Size-Independent Sample Complexity of Neural Networks
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-2/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180427T110000
DTEND;TZID=America/New_York:20180427T120000
DTSTAMP:20260517T061410
CREATED:20171215T163016Z
LAST-MODIFIED:20180426T181058Z
UID:7146-1524826800-1524830400@idss-stage.mit.edu
SUMMARY:Inference\, Computation\, and Visualization for Convex Clustering and Biclustering
DESCRIPTION:Abstract:  Hierarchical clustering enjoys wide popularity because of its fast computation\, ease of interpretation\, and appealing visualizations via the dendogram and cluster heatmap. Recently\, several have proposed and studied convex clustering and biclustering which\, similar in spirit to hierarchical clustering\, achieve cluster merges via convex fusion penalties. While these techniques enjoy superior statistical performance\, they suffer from slower computation and are not generally conducive to representation as a dendogram. In the first part of the talk\, we present new convex (bi)clustering methods and fast algorithms that inherit all of the advantages of hierarchical clustering. Specifically\, we develop a new fast approximation and variation of the convex (bi)clustering solution path that can be represented as a dendogram or cluster heatmap. Also\, as one tuning parameter indexes the sequence of convex (bi)clustering solutions\, we can use these to develop interactive and dynamic visualization strategies that allow one to watch data form groups as the tuning parameter varies. In the second part of this talk\, we consider how to conduct inference for convex clustering solutions that addresses questions like: Are there clusters in my data set? Or\, should two clusters be merged into one? To achieve this\, we develop a new geometric representation of Hotelling’s T^2-test that allows us to use the selective inference paradigm to test multivariate hypotheses for the first time. We can use this approach to test hypotheses and calculate confidence ellipsoids on the cluster means resulting from convex clustering. We apply these techniques to examples from text mining and cancer genomics. This is joint work with John Nagorski\, Michael Weylandt\, and Frederick Campbell. \nBiography:  Genevera Allen is an Associate Professor of Statistics\, Computer Science\, and Electrical and Computer Engineering at Rice University. She is also a member of the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital and Baylor College of Medicine where she holds a joint appointment. Dr. Allen received her PhD in statistics from Stanford University (2010)\, under the mentorship of Prof. Robert Tibshirani\, and her bachelors\, also in statistics\, from Rice University (2006).\nDr. Allen’s research focuses on developing statistical methods to help scientists make sense of their ‘Big Data’ in applications such as high-throughput genomics and neuroimaging. Her work lies in the areas of modern multivariate analysis\, graphical models\, statistical machine learning\, and data integration or data fusion. She is the recipient of several honors including a National Science Foundation CAREER award\, the International Biometric Society’s Young Statistician Showcase award\, and the George R. Brown School of Engineering’s Research and Teaching Excellence Award at Rice University. In 2013 and 2014\, she represented the American Statistical Association (ASA) at the Coalition for National Science Funding on Capitol Hill and has had her research highlighted on the House floor in a speech by Congressman McNerney (D-CA). In 2014\, Dr. Allen was named to the “Forbes ’30 under 30′: Science and Healthcare” list. Dr. Allen currently serves as an Associated Editor for Biometrics\, the Secretary / Treasurer for the ASA Section on Statistical Computing\, and the Program Chair for the ASA Section on Statistical Learning and Data Science.\nOutside of work\, Dr. Allen is a patron of the Houston Symphony and Houston Grand Opera and is involved with several arts organizations throughout Houston. She also enjoys traveling\, Texas craft beers\, and playing viola.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180413T110000
DTEND;TZID=America/New_York:20180413T120000
DTSTAMP:20260517T061410
CREATED:20171215T161019Z
LAST-MODIFIED:20180801T190032Z
UID:7143-1523617200-1523620800@idss-stage.mit.edu
SUMMARY:Testing degree corrections in Stochastic Block Models
DESCRIPTION:Abstract:  The community detection problem has attracted signicant attention in re- cent years\, and it has been studied extensively under the framework of a Stochas- tic Block Model (SBM). However\, it is well-known that SBMs fit real data very poorly\, and various extensions have been suggested to replicate characteristics of real data. The recovered community assignments are often sensitive to the model used\, and this naturally begs the following question:  Given a network with community structure\, how to decide whether to fit a vanilla SBM\, or a more complicated model?  In this talk\, we will formulate this problem as a classical goodness of fit question\, and try to provide some principled answers in this direction. \nThis is based on joint work with Rajarshi Mukherjee. \nBio:  Subhabrata Sen is Schramm Postdoctoral Fellow at Microsoft Re- search NE and MIT Mathematics. He graduated from the Stanford Statistics Department in 2017\, where he was advised by Amir Dembo and Andrea Mon- tanari. He was awarded the “Probability Dissertation Award” for his thesis on “Random graphs\, optimization\, and spin glasses”.  His research interests include hypothesis testing and non-parametric inference on one hand\, and combinatorial optimization and random graphs on the other.
URL:https://idss-stage.mit.edu/calendar/stochastic-and-statistics-seminar/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180406T110000
DTEND;TZID=America/New_York:20180406T120000
DTSTAMP:20260517T061410
CREATED:20180311T182217Z
LAST-MODIFIED:20180311T183822Z
UID:7473-1523012400-1523016000@idss-stage.mit.edu
SUMMARY:Optimality of Spectral Methods for Ranking\, Community Detections and Beyond
DESCRIPTION:Abstract:  Spectral methods have been widely used for a large class of challenging problems\, ranging from top-K ranking via pairwise comparisons\, community detection\, factor analysis\, among others.\nAnalyses of these spectral methods require super-norm perturbation analysis of top eigenvectors. This allows us to UNIFORMLY approximate elements in eigenvectors by linear functions of the observed random matrix that can be analyzed further. We first establish such an infinity-norm pertubation bound for top eigenvectors and apply the idea to several challenging problems such as top-K ranking\, community detections\, Z_2-syncronization and matrix completion. We show that the spectral methods are indeed optimal for these problems. We illustrate these methods via simulations.\n(Based on joint work with Emmanuel Abbe\, Kaizheng Wang\, Yiqiao Zhong and that of Yixin Chen\, Cong Ma and Kaizheng Wang) \n Biography: Jianqing Fan is Frederick L. Moore Professor at Princeton University. After receiving his Ph.D. from the University of California at Berkeley\, he has been appointed as assistant\, associate\, and full professor at the University of North Carolina at Chapel Hill (1989-2003)\, professor at the University of California at Los Angeles (1997-2000)\, and professor at the Princeton University (2003–). He was the past president of the Institute of Mathematical Statistics and International Chinese Statistical Association. He is co-editing Journal of Econometrics and was the co-editor of The Annals of Statistics\, Probability Theory and Related Fields and Econometrics Journal. His published work on statistics\, economics\, finance\, and computational biology has been recognized by The 2000 COPSS Presidents’ Award\, The 2007 Morningside Gold Medal of Applied Mathematics\, Guggenheim Fellow\, P.L. Hsu Prize\, Royal Statistical Society Guy medal in silver\, and election to Academician of Academia Sinica and follow of American Associations for Advancement of Science.
URL:https://idss-stage.mit.edu/calendar/optimality-of-spectral-methods-for-ranking-community-detections-and-beyond/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180323T110000
DTEND;TZID=America/New_York:20180323T120000
DTSTAMP:20260517T061410
CREATED:20180205T145624Z
LAST-MODIFIED:20180205T145624Z
UID:7346-1521802800-1521806400@idss-stage.mit.edu
SUMMARY:Statistical theory for deep neural networks with ReLU activation function
DESCRIPTION:Abstract: The universal approximation theorem states that neural networks are capable of approximating any continuous function up to a small error that depends on the size of the network. The expressive power of a network does\, however\, not guarantee that deep networks perform well on data. For that\, control of the statistical estimation risk is needed. In the talk\, we derive statistical theory for fitting deep neural networks to data generated from the multivariate nonparametric regression model. It is shown that estimators based on sparsely connected deep neural networks with ReLU activation function and properly chosen network architecture achieve the minimax rates of convergence (up to logarithmic factors) under a general composition assumption on the regression function. The framework includes many well-studied structural constraints such as (generalized) additive models. While there is a lot of flexibility in the network architecture\, the tuning parameter is the sparsity of the network. Specifically\, we consider large networks with number of potential parameters being much bigger than the sample size. Interestingly\, the depth (number of layers) of the neural network architectures plays an important role and our theory suggests that scaling the network depth with the logarithm of the sample size is natural.
URL:https://idss-stage.mit.edu/calendar/statistical-theory-for-deep-neural-networks-with-relu-activation-function/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180316T110000
DTEND;TZID=America/New_York:20180316T120000
DTSTAMP:20260517T061410
CREATED:20180302T201932Z
LAST-MODIFIED:20180302T201932Z
UID:7461-1521198000-1521201600@idss-stage.mit.edu
SUMMARY:When Inference is Tractable
DESCRIPTION:Abstract:\nA key capability of artificial intelligence will be the ability to\nreason about abstract concepts and draw inferences. Where data is\nlimited\, probabilistic inference in graphical models provides a\npowerful framework for performing such reasoning\, and can even be used\nas modules within deep architectures. But\, when is probabilistic\ninference computationally tractable? I will present recent theoretical\nresults that substantially broaden the class of provably tractable\nmodels by exploiting model stability (Lang\, Sontag\, Vijayaraghavan\, AI\nStats ’18)\, structure in model parameters (Weller\, Rowland\, Sontag\, AI\nStats ’16)\, and reinterpreting inference as ground truth recovery\n(Globerson\, Roughgarden\, Sontag\, Yildirim\, ICML ’15). \nBio:\nDavid Sontag is an Assistant Professor in the Department of Electrical\nEngineering and Computer Science (EECS) at MIT\, and member of the\nInstitute for Medical Engineering and Science and the Computer Science\nand Artificial Intelligence Laboratory (CSAIL). Prior to joining MIT\,\nDr. Sontag was an Assistant Professor in Computer Science and Data\nScience at New York University from 2011 to 2016\, and a postdoctoral\nresearcher at Microsoft Research New England. Dr. Sontag received the\nSprowls award for outstanding doctoral thesis in Computer Science at\nMIT in 2010\, best paper awards at the conferences Empirical Methods in\nNatural Language Processing (EMNLP)\, Uncertainty in Artificial\nIntelligence (UAI)\, and Neural Information Processing Systems (NIPS)\,\nfaculty awards from Google\, Facebook\, and Adobe\, and a National\nScience Foundation Early Career Award. Dr. Sontag received a B.A. from\nthe University of California\, Berkeley.
URL:https://idss-stage.mit.edu/calendar/when-inference-is-tractable/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180309T110000
DTEND;TZID=America/New_York:20180309T120000
DTSTAMP:20260517T061410
CREATED:20171215T165643Z
LAST-MODIFIED:20180305T132412Z
UID:7157-1520593200-1520596800@idss-stage.mit.edu
SUMMARY:Statistical estimation under group actions: The Sample Complexity of Multi-Reference Alignment
DESCRIPTION:Abstract: \nMany problems in signal/image processing\, and computer vision amount to estimating a signal\, image\, or tri-dimensional structure/scene from corrupted measurements. A particularly challenging form of measurement corruption are latent transformations of the underlying signal to be recovered. Many such transformations can be described as a group acting on the object to be recovered. Examples include the Simulatenous Localization and Mapping (SLaM) problem in Robotics and Computer Vision\, where pictures of a scene are obtained from different positions andorientations; Cryo-Electron Microscopy (Cryo-EM) imaging where projections of a molecule density are taken from unknown rotations\, andseveral others. \nOne fundamental example of this type of problems is Multi-Reference Alignment: Given a group acting in a space\, the goal is to estimate an orbit of the group action from noisy samples. For example\, in one of its simplest forms\, one is tasked with estimating a signal from noisy cyclically shifted copies. We will show that the number of observations needed by any method has a surprising dependency on the signal-to-noise ratio (SNR)\, and algebraic properties of the underlying group action. Remarkably\, in some important cases\, this sample complexity is achieved with computationally efficient methods based on computing invariants under the group of transformations.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-6/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180302T110000
DTEND;TZID=America/New_York:20180302T120000
DTSTAMP:20260517T061410
CREATED:20171215T165516Z
LAST-MODIFIED:20180214T152856Z
UID:7155-1519988400-1519992000@idss-stage.mit.edu
SUMMARY:One and two sided composite-composite tests in Gaussian mixture models
DESCRIPTION:Abstract: Finding an efficient test for a testing problem is often linked to the problem of estimating a given function of the data. When this function is not smooth\, it is necessary to approximate it cleverly in order to build good tests.\nIn this talk\, we will discuss two specific testing problems in Gaussian mixtures models. In both\, the aim is to test the proportion of null means. The aforementioned link between sharp approximation rates of non-smooth objects and minimax testing rates is particularly well illustrated by these problems. \n(based on joint works with Nicolas Verzelen\, Etienne Roquain and Sylvain Delattre) \nBiography:  Alexandra Carpenter is since October 2017 chair of Mathematical Statistics and Machine Learning in the Institut für Mathematische Stochastik (IMST)\, Fakultät für Mathematik (FMA)\, in the Otto-von-Guericke-Universität Magdeburg. Prior to that\, she was between 2015 and 2017 the group leader of the DFG Emmy Noether group MuSyAD on theoretical anomaly detection in the Universitaet Potsdam\, and between 2012 and 2015 in the StatsLab in the University of Cambridge as a research associate\, working with Richard Nickl. She finished her PhD in 2012 in INRIA Lille Nord-Europe under the supervision of Remi Munos and on the topic of bandit theory. Her research interests are in machine learning and mathematical statistics with an emphasis on composite testing problems\, adaptive inference in high and infinite dimension and sequential learning (e.g. bandit theory).
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-5/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
END:VCALENDAR