Neyman Seminar

The Neyman seminar is the statistics seminar in the Department. Historically, it has been focused on applications of Statistics to other fields. Nowadays, it has a very broad scope, with topics ranging from applications of statistics to theory.

The seminar is held on Wednesdays from 4 to 5 in the Jerzy Neyman room, 1011 Evans.

Details of individual seminar events are published in the campus' event system.

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Recent & Upcoming Neyman Seminars

Non-linear feature selection in high-dimensional genomic data sets
Chloé-Agathe Azencott, Mines ParisTech
Oct 30, 2019 4:00pm
1011 Evans Hall
Abstract:
Many problems in genomics require the ability to identify relevant features in data sets containing many more orders of magnitude than samples. One such example is genome-wide association studies (GWAS), in which hundreds of thousands of single nucleotide polymorphisms are measured for orders of magnitude fewer samples. This setup poses statistical and computational challenges, and for...
Neyman Seminar
Mathematics of deep learning?
Roman Vershynin, University of California, Irvine
Nov 5, 2019 4:00pm
1011 Evans Hall
Abstract:
Deep learning is a rapidly developing area of machine learning, which uses artificial neural networks to perform learning tasks. Although mathematical description of neural networks is simple, theoretical explanation of spectacular performance of deep learning remains elusive. Even the most basic questions about remain open. For example, how many different functions can a neural network compute?...
Neyman Seminar
Existence and uniqueness of the Liouville quantum gravity metric for $\gamma \in (0,2)$
Jason Miller, University of Cambridge
Nov 13, 2019 4:00pm
1011 Evans Hall
Abstract:
Liouville quantum gravity (LQG) is in some sense the canonical model of a two-dimensional Riemannian manifold and is defined using the (formal) metric tensor \[ e^{\gamma h(z)} (dx^2 + dy^2)\] where $h$ is an instance of some form of the Gaussian free field and $\gamma \in (0,2)$ is a parameter. This expression does not make literal sense since $h$ is a distribution and not a function, so...
Neyman Seminar
Ensembles of Trees and CLT's: Inference and Machine Learning
Giles Hooker, Cornell University
Nov 20, 2019 4:00pm
1011 Evans Hall
Abstract:
This talk develops methods of statistical inference based around ensembles of decision trees: bagging, random forests, and boosting. Recent results have shown that when the bootstrap procedure in bagging methods is replaced by sub-sampling, predictions from these methods can be analyzed using the theory of U-statistics which have a limiting normal distribution. Moreover, the limiting variance...
Neyman Seminar
Statistical Treatment of inverse problems constrained by differential equations-based models with stochastic terms
Noemi Petra, UC Merced
Dec 4, 2019 4:00pm
1011 Evans Hall
Abstract:
In this talk, we introduce a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution represented numerically by an ensemble of simulations. The goal is to formulate the inverse problem, in particular the objective function, to find the closest forward distribution (i.e., the output of the...
Neyman Seminar
Optimal adaptive design of experiments for stochastic dynamic systems
Giles Hooker, Cornell University
Jan 22, 2020 4:00pm
1011 Evans Hall
Abstract:
This talk examines the design of stochastic experimental systems so as to best able to estimate parameters of the underlying dynamics. In systems ranging from ecology, neurobiology and economics, models of system dynamics can be paired with laboratory experiments to estimate parameters and gain insight into their underlying dynamics. When this is done, several experimental parameters can be...
Neyman Seminar
Bayesian Probabilistic Numerical Methods
Tim Sullivan, Freie Universität Berlin and Zuse Institute Berlin
Jan 29, 2020 4:00pm
1011 Evans Hall
Abstract:
Numerical computation --- such as numerical solution of a PDE, or quadrature --- can modelled as a statistical inverse problem in its own right. In particular, we can apply the Bayesian approach to inversion, so that a posterior distribution is induced over the object of interest (e.g. the PDE's solution) by conditioning a prior distribution on the same finite information that would be used in a...
Neyman Seminar
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