Publications related to Probability theory

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...

CAMBRIDGE UNIV PRESS2021

A General Framework for Optimal Data-Driven Optimization

Daniel Kuhn, Tobias Sutter

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

2021

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Sylvain Calinon, Emmanuel Pignat

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

SAGE PUBLICATIONS LTD2021

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

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

COVID-19: data-driven dynamic asset allocation in times of pandemic

Anna Timonina-Farkas

The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the ...

AMER INST MATHEMATICAL SCIENCES-AIMS2021

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

Giacomo Garegnani

In this thesis we explore uncertainty quantification of forward and inverse problems involving differential equations. Differential equations are widely employed for modeling natural and social phenomena, with applications in engineering, chemistry, meteor ...

EPFL2021

Tighter thermodynamic bound on the speed limit in systems with unidirectional transitions

Daniel Maria Busiello, Deepak Gupta

We consider a general discrete state-space system with both unidirectional and bidirectional links. In contrast to bidirectional links, there is no reverse transition along the unidirectional links Herein, we first compute the statistical length and the th ...

2020

Normalized Gaussian path integrals

Giulio Corazza

Path integrals play a crucial role in describing the dynamics of physical systems subject to classical or quantum noise. In fact, when correctly normalized, they express the probability of transition between two states of the system. In this work, we show ...

AMER PHYSICAL SOC2020

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

Probability theory

Graph Chatbot

Chat with Graph Search

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

A General Framework for Optimal Data-Driven Optimization

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Learning in Volatile Environments With the Bayes Factor Surprise

COVID-19: data-driven dynamic asset allocation in times of pandemic

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

Tighter thermodynamic bound on the speed limit in systems with unidirectional transitions

Normalized Gaussian path integrals

Robust Generalization via $\alpha$-Mutual Information

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

Tighter thermodynamic bound on the speed limit in systems with unidirectional transitions

A General Framework for Optimal Data-Driven Optimization

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Learning in Volatile Environments With the Bayes Factor Surprise

COVID-19: data-driven dynamic asset allocation in times of pandemic

Normalized Gaussian path integrals

Robust Generalization via $\alpha$-Mutual Information