Publications related to Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Surprise-based model estimation in reinforcement learning: algorithms and brain signatures

Learning how to act and adapting to unexpected changes are remarkable capabilities of humans and other animals. In the absence of a direct recipe to follow in life, behaviour is often guided by rewarding and by surprising events. A positive or a negative o ...

EPFL2021

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

MATHICSE Technical Report : A probabilistic finite element method based on random meshes: Error estimators and Bayesian inverse problems

Assyr Abdulle, Giacomo Garegnani

We present a novel probabilistic finite element method (FEM) for the solution and uncertainty quantification of elliptic partial differential equations based on random meshes, which we call random mesh FEM (RM-FEM). Our methodology allows to introduce a pr ...

EPFL2021

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Wolfram Wiesemann

The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood ...

2019

Statistical Aspects of Wasserstein Distances

Victor Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

ANNUAL REVIEWS2019

Quantifying Commitment in Nash Equilibria

Thomas Alois Weber

To quantify a player's commitment in a given Nash equilibrium of a finite dynamic game, we map the corresponding normal-form game to a "canonical extension," which allows each player to adjust his or her move with a certain probability. The commitment meas ...

2019

Efficient Learning from Comparisons

Lucas Maystre

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

EPFL2018

A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

Rudolf Haraksim

This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes' inference model. In the context of the inference of identity of source, the Likeliho ...

Elsevier Ireland Ltd2017

Great Balls of Fire: A probabilistic approach to quantify the hazard related to ballistics – A case study at La Fossa volcano, Vulcano Island, Italy

Pierino Lestuzzi

We present a probabilistic approach to quantify the hazard posed by volcanic ballistic projectiles (VBP) and their potential impact on the built environment. A model named Great Balls of Fire (GBF) is introduced to describe ballistic trajectories of VBPs a ...

Elsevier Science Bv2016

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

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Surprise-based model estimation in reinforcement learning: algorithms and brain signatures

Learning in Volatile Environments With the Bayes Factor Surprise

MATHICSE Technical Report : A probabilistic finite element method based on random meshes: Error estimators and Bayesian inverse problems

Robust Generalization via $\alpha$-Mutual Information

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Statistical Aspects of Wasserstein Distances

Quantifying Commitment in Nash Equilibria

Efficient Learning from Comparisons

A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

Great Balls of Fire: A probabilistic approach to quantify the hazard related to ballistics – A case study at La Fossa volcano, Vulcano Island, Italy

Surprise-based model estimation in reinforcement learning: algorithms and brain signatures

Learning in Volatile Environments With the Bayes Factor Surprise

MATHICSE Technical Report : A probabilistic finite element method based on random meshes: Error estimators and Bayesian inverse problems

Robust Generalization via $\alpha$-Mutual Information

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Efficient Learning from Comparisons

Statistical Aspects of Wasserstein Distances

Great Balls of Fire: A probabilistic approach to quantify the hazard related to ballistics – A case study at La Fossa volcano, Vulcano Island, Italy

A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

Quantifying Commitment in Nash Equilibria