Publications related to Divergence theorem

A structured prediction approach for robot imitation learning

Aude Billard, Iason Batzianoulis, Anqing Duan

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

London2023

QFT without infinities and hierarchy problem

Sander Johannes Nicolaas Mooij

The standard way to do computations in Quantum Field Theory (QFT) often results in the requirement of dramatic cancellations between contributions induced by a "heavy" sector into the physical observables of the "light" (or low energy) sector - the phenome ...

ELSEVIER2023

Quadrature-free immersed isogeometric analysis

Pablo Antolin Sanchez, Thibaut Hirschler

This paper presents a novel method for solving partial differential equations on three-dimensional CAD geometries by means of immersed isogeometric discretizations that do not require quadrature schemes. It relies on a newly developed technique for the eva ...

SPRINGER2022

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Michael Christoph Gastpar, Adrien Vandenbroucque, Amedeo Roberto Esposito

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results i ...

2022

On the commutativity of flows of rough vector fields

Maria Colombo, Riccardo Tione

In the class of Sobolev vector fields in R-n of bounded divergence, for which the theory of DiPerna and Lions provides a well defined notion of flow, we characterize the vector fields whose flow commutes in terms of the Lie bracket and of a regularity cond ...

ELSEVIER2022

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

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

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

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Divergence theorem

Anomalous dissipation and other non-smooth phenomena in fluids

On Speed and Advantage : Results in Information Velocity and Monitoring Problems

A structured prediction approach for robot imitation learning

QFT without infinities and hierarchy problem

Quadrature-free immersed isogeometric analysis

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

On the commutativity of flows of rough vector fields

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

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

Robust Generalization via f−Mutual Information

Graph Chatbot

Chat with Graph Search

On Speed and Advantage : Results in Information Velocity and Monitoring Problems

Anomalous dissipation and other non-smooth phenomena in fluids

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

QFT without infinities and hierarchy problem

A structured prediction approach for robot imitation learning

Quadrature-free immersed isogeometric analysis

Robust Generalization via f−Mutual Information

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

On the commutativity of flows of rough vector fields

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences