Publications related to Exponential family

Learning in Volatile Environments With the Bayes Factor Surprise

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...

MIT PRESS2021

Learning, compression, and leakage: Minimising classification error via meta-universal compression principles

Michael Christoph Gastpar, Fernando Rosas de Andraca

Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas. A promising group of compression techniques for learning scenarios is ...

IEEE2021

Revisiting the Calculation of the Early Time HEMP Conducted Environment

Carlos Alberto Romero Romero, Nicolas Mora Parra

The early time high altitude electromagnetic pulse conducted environment calculation is revisited by using the transmission line (TL) theory and including high-frequency corrections that were not present in the earlier studies. Waveform salient parameters, ...

2021

Strong convergence of multivariate maxima

Simone Padoan, Stefano Rizzelli

It is well known and readily seen that the maximum of n independent and uniformly on [0, 1] distributed random variables, suitably standardised, converges in total variation distance, as n increases, to the standard negative exponential distribution. We ex ...

CAMBRIDGE UNIV PRESS2020

Modeling Network Populations via Graph Distances

Sofia Charlotta Olhede

This article introduces a new class of models for multiple networks. The core idea is to parameterize a distribution on labeled graphs in terms of a Frechet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter t ...

AMER STATISTICAL ASSOC2020

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Michel Bierlaire, Prateek Bansal

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substan ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Content Based Status Updates

Emre Telatar, Elie Najm, Rajai Nasser

Consider a stream of status updates generated by a source, where each update is of one of two types: high priority or ordinary (low priority). These updates are to be transmitted through a network to a monitor. However, the transmission policy of each pack ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Small-variance asymptotics for non-parametric online robot learning

Sylvain Calinon, Ajay Kumar Tanwani

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-va ...

SAGE PUBLICATIONS LTD2019

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Exponential family

Learning in Volatile Environments With the Bayes Factor Surprise

Learning, compression, and leakage: Minimising classification error via meta-universal compression principles

Revisiting the Calculation of the Early Time HEMP Conducted Environment

Strong convergence of multivariate maxima

Modeling Network Populations via Graph Distances

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Content Based Status Updates

Fast Mean Estimation with Sub-Gaussian Rates

Sequential Outlier Detection Based on Incremental Decision Trees

Small-variance asymptotics for non-parametric online robot learning

Graph Chatbot

Chat with Graph Search

Learning, compression, and leakage: Minimising classification error via meta-universal compression principles

Modeling Network Populations via Graph Distances

Revisiting the Calculation of the Early Time HEMP Conducted Environment

Strong convergence of multivariate maxima

Content Based Status Updates

Learning in Volatile Environments With the Bayes Factor Surprise

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Fast Mean Estimation with Sub-Gaussian Rates

Sequential Outlier Detection Based on Incremental Decision Trees

Small-variance asymptotics for non-parametric online robot learning