Publications related to Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

Negar Kiyavash, Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Seyed Jalal Etesami

Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correc ...

2022

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

Structure of multicorrelation sequences with integer part polynomial iterates along primes

Florian Karl Richter

Let T be a measure-preserving Zℓ-action on the probability space (X,B,μ), let q1,…,qm:R→Rℓ be vector polynomials, and let f0,…,fm∈L∞⁡(X). For any ϵ>0 and multicorrelation sequences of the form α⁡(n)=∫Xf0⋅T⌊q1⁡(n)⌋⁢f1⋯T⌊qm⁡(n)⌋⁢fmd⁢μ we show that there exis ...

2021

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

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Mehdi Gholam

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

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

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Nicolas Henri Bernard Flammarion, Xiang Cheng

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional. We show that an under-damped form of the Langevin algorithm perfor ...

INT STATISTICAL INST2021

Bayesian Methods for the Identification of Distribution Networks

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line param ...

IEEE2021

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

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

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Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

A General Framework for Optimal Data-Driven Optimization

Structure of multicorrelation sequences with integer part polynomial iterates along primes

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

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

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

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Bayesian Methods for the Identification of Distribution Networks

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

Robust Generalization via $\alpha$-Mutual Information

A General Framework for Optimal Data-Driven Optimization

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

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

Bayesian Methods for the Identification of Distribution Networks

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

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Structure of multicorrelation sequences with integer part polynomial iterates along primes

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

Robust Generalization via $\alpha$-Mutual Information