Publications related to Inference on the Angular Distribution of Extremes

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

Efficient ensemble summaries are inversely related to visual crowding

Michael Herzog, David Pascucci, Yury Markov, Natalia Tiurina

Visual crowding is the inability to perceive properly peripheral stimuli within clutter. Previous work has shown that crowding is affected by perceptual grouping: when the flankers do not group with the target, crowding decreases, leading to uncrowding. Ty ...

2021

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Amit Sharma

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

Fundamental bounds on the precision of iSCAT, COBRI and dark-field microscopy for 3D localization and mass photometry

Jonathan Yuelin Dong

Interferometric imaging is an emerging technique for particle tracking and mass photometry. Mass or position are estimated from weak signals, coherently scattered from nanoparticles or single molecules, and interfered with a co-propagating reference. In th ...

IOP PUBLISHING LTD2021

Estimation of separable direct and indirect effects in continuous time

Mats Julius Stensrud

Many research questions involve time-to-event outcomes that can be prevented from occurring due to competing events. In these settings, we must be careful about the causal interpretation of classical statistical estimands. In particular, estimands on the h ...

WILEY2021

Multivariate extremes over a random number of observations

Simone Padoan, Stefano Rizzelli

The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as max ...

WILEY2020

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Daniel Kuhn, Viet Anh Nguyen, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...

2020

MATHICSE Technical Report : Drift Estimation of Multiscale Diffusions Based on Filtered Data

Assyr Abdulle, Giacomo Garegnani, Andrea Zanoni, Grigorios A. Pavliotis

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coeffic ...

EPFL2020

The probability of intransitivity in dice and close elections

Jan Hazla

We study the phenomenon of intransitivity in models of dice and voting. First, we follow a recent thread of research for n-sided dice with pairwise ordering induced by the probability, relative to 1/2, that a throw from one die is higher than the other. We ...

2020

Inference on the Angular Distribution of Extremes

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Unbiased Monte Carlo cluster updates with autoregressive neural networks

Multiple testing with test statistics following heavy-tailed distributions

Efficient ensemble summaries are inversely related to visual crowding

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Fundamental bounds on the precision of iSCAT, COBRI and dark-field microscopy for 3D localization and mass photometry

Estimation of separable direct and indirect effects in continuous time

Multivariate extremes over a random number of observations

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

MATHICSE Technical Report : Drift Estimation of Multiscale Diffusions Based on Filtered Data

The probability of intransitivity in dice and close elections

Multiple testing with test statistics following heavy-tailed distributions

MATHICSE Technical Report : Drift Estimation of Multiscale Diffusions Based on Filtered Data

Efficient ensemble summaries are inversely related to visual crowding

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Fundamental bounds on the precision of iSCAT, COBRI and dark-field microscopy for 3D localization and mass photometry

The probability of intransitivity in dice and close elections

Estimation of separable direct and indirect effects in continuous time

Multivariate extremes over a random number of observations

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective