Publications associées à Natural exponential family

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Sadegh Farhadkhani, Oscar Jean Olivier Villemaud, Julien René Pierre Fageot, Le Nguyen Hoang

Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

AAAI Press2024

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Soumaya Elkantassi

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

EPFL2023

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Michael Christoph Gastpar, Alper Köse, Ahmet Arda Atalik

The conditional mean is a fundamental and important quantity whose applications include the theories of estimation and rate-distortion. It is also notoriously difficult to work with. This paper establishes novel bounds on the differential entropy of the co ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Andrea Zanoni

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

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

Certified And Fast Computations With Shallow Covariance Kernels

Daniel Kressner, Stefano Massei

Many techniques for data science and uncertainty quantification demand efficient tools to handle Gaussian random fields, which are defined in terms of their mean functions and covariance operators. Recently, parameterized Gaussian random fields have gained ...

AMER INST MATHEMATICAL SCIENCES-AIMS2020

Sequential Outlier Detection Based on Incremental Decision Trees

Hakan Gökcesu, Kaan Gökcesu

We introduce an online outlier detection algorithm to detect outliers in a sequentially observed data stream. For this purpose, we use a two-stage filtering and hedging approach. In the first stage, we construct a multimodal probability density function to ...

2019

Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems

Vincenzo Savona, Alexandra Nagy

The possibility to simulate the properties of many-body open quantum systems with a large number of degrees of freedom (d.o.f.) is the premise to the solution of several outstanding problems in quantum science and quantum information. The challenge posed b ...

AMER PHYSICAL SOC2019

Bayesian adaptation of chaos representations using variational inference and sampling on geodesics

Panagiotis Tsilifis

A novel approach is presented for constructing polynomial chaos representations of scalar quantities of interest (QoI) that extends previously developed methods for adaptation in Homogeneous Chaos spaces. In this work, we develop a Bayesian formulation of ...

ROYAL SOC2018

Accurate Directional Inference for Vector Parameters in Linear Exponential Families

Anthony Christopher Davison

We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value i ...

American Statistical Association2014