Statistics research seminar

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Spring Semester 2023

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Speaker

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17 March 2023
15:15-16:15

Sebastian Lerch
Karlsruhe Institute of Technology

Event Details

Research Seminar in Statistics

Title	Generative machine learning methods for multivariate ensemble post-processing
Speaker, Affiliation	Sebastian Lerch, Karlsruhe Institute of Technology
Date, Time	17 March 2023, 15:15-16:15
Location	HG G 19.1
Abstract	Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts. Accurately modeling multivariate dependencies is crucial in many practical applications, and various approaches to multivariate post-processing have been proposed where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are then restored via copulas. These two-step methods share common key limitations, in particular the difficulty to include additional predictors in modeling the dependencies. We propose a novel multivariate post-processing method based on generative machine learning to address these challenges. In this new class of nonparametric data-driven distributional regression models, samples from the multivariate forecast distribution are directly obtained as output of a generative neural network. The generative model is trained by optimizing a proper scoring rule which measures the discrepancy between the generated and observed data, conditional on exogenous input variables. Our method does not require parametric assumptions on univariate distributions or multivariate dependencies and allows for incorporating arbitrary predictors. In two case studies on multivariate temperature and wind speed forecasting at weather stations over Germany, our generative model shows significant improvements over state-of-the-art methods and particularly improves the representation of spatial dependencies. A preprint is available at https://arxiv.org/abs/2211.01345.

Generative machine learning methods for multivariate ensemble post-processingread_more

HG G 19.1

28 March 2023
13:15-14:15

Boaz Nadler
The Weizmann Institute of Science, Israel

Event Details

Research Seminar in Statistics

Title	The Trimmed Lasso: Sparse Recovery Guarantees And Practical Optimization
Speaker, Affiliation	Boaz Nadler, The Weizmann Institute of Science, Israel
Date, Time	28 March 2023, 13:15-14:15
Location	HG G 19.2
Abstract	Consider the sparse approximation or best subset selection problem: Given a vector y and a matrix A, find a k-sparse vector x that minimizes the residual \|\|Ax-y\|\|. This sparse linear regression problem, and related variants, plays a key role in high dimensional statistics, compressed sensing, and more. In this talk we focus on the trimmed lasso penalty, defined as the L_1 norm of x minus the L_1 norm of its top k entries in absolute value. We advocate using this penalty by deriving sparse recovery guarantees for it, and by presenting a practical approach to optimize it. Our computational approach is based on the generalized soft-min penalty, a smooth surrogate that takes into account all possible k-sparse patterns. We derive a polynomial time algorithm to compute it, which in turn yields a novel method for the best subset selection problem. Numerical simulations illustrate its competitive performance compared to current state of the art.

The Trimmed Lasso: Sparse Recovery Guarantees And Practical Optimizationread_more

HG G 19.2

4 April 2023
15:15-16:15

Boaz Nadler
The Weizmann Institute of Science, Israel

Event Details

Research Seminar in Statistics

Title	Spectral Methods for Reconstructing Trees
Speaker, Affiliation	Boaz Nadler, The Weizmann Institute of Science, Israel
Date, Time	4 April 2023, 15:15-16:15
Location	HG G 19.2
Abstract	Tree graphical models are common statistical models for data in a wide variety of applications. Tree models are particularly popular in phylogenetics, where an important task is to infer the evolutionary history of current species. Given observations at the leaves of the tree, a common problem is to reconstruct the tree's latent structure. We present two simple spectral-based methods for tree recovery: (i) A bottom up spectral neighbor joining method (SNJ); and (ii) STDR - a spectral based top down method. We prove that under suitable assumptions, both methods are consistent and derive finite sample recovery guarantees. We illustrate the competitive performance of our algorithms in comparison with popular tree recovery methods.

Spectral Methods for Reconstructing Treesread_more

HG G 19.2

31 May 2023
17:15-18:15

Sara van de Geer
ETH Zürich

Event Details

Research Seminar in Statistics

Title	Farewell Lecture: Data dust
Speaker, Affiliation	Sara van de Geer, ETH Zürich
Date, Time	31 May 2023, 17:15-18:15
Location	HG F 30
Abstract	tba

Farewell Lecture: Data dust read_more

HG F 30

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