Views Navigation

Event Views Navigation

Calendar of Events

S Sun

M Mon

T Tue

W Wed

T Thu

F Fri

S Sat

0 events,

0 events,

0 events,

0 events,

0 events,

1 event,

Stochastics and Statistics Seminar Series Marco Mondelli

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

1 event,

Stochastics and Statistics Seminar Series Julia Palacios

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

1 event,

Stochastics and Statistics Seminar Series Jaouad Mourtada

0 events,

Inference in High Dimensions for (Mixed) Generalized Linear Models: the Linear, the Spectral and the Approximate

Marco Mondelli (Institute of Science and Technology Austria)
E18-304

Abstract: In a generalized linear model (GLM), the goal is to estimate a d-dimensional signal x from an n-dimensional observation of the form f(Ax, w), where A is a design matrix and w is a noise vector. Well-known examples of GLMs include linear regression, phase retrieval, 1-bit compressed sensing, and logistic regression. We focus on…

Find out more »

Distance-based summaries and modeling of evolutionary trees.

Julia Palacios (Stanford University)
E18-304

Abstract:  Phylogenetic trees are mathematical objects of great importance used to model hierarchical data and evolutionary relationships with applications in many fields including evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explore the posterior distribution of trees via Markov Chain Monte Carlo methods, however assessing uncertainty and summarizing distributions remains challenging for these types…

Find out more »

Coding convex bodies under Gaussian noise, and the Wills functional

Jaouad Mourtada (ENSAE Paris)
E18-304

Abstract: We consider the problem of sequential probability assignment in the Gaussian setting, where one aims to predict (or equivalently compress) a sequence of real-valued observations almost as well as the best Gaussian distribution with mean constrained to a general domain. First, in the case of a convex constraint set K, we express the hardness…

Find out more »


MIT Institute for Data, Systems, and Society
Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139-4307
617-253-1764