Publications related to Conditional probability

A General Framework for Optimal Data-Driven Optimization

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

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

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Christian Vincent Mouchet

We present a bootstrapping procedure for the full-RNS variant of the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared to the previously proposed procedures (Eurocrypt 18 & 19, CT-RSA 20), our bootstrapping pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

Stochastic Optimal Control for Autonomous Driving Applications via Polynomial Chaos Expansions

Colin Neil Jones, Petr Listov, Johannes Christian Schwarz

Model-based methods in autonomous driving and advanced driving assistance gain importance in research and development due to their potential to contribute to higher road safety. Parameters of vehicle models, however, are hard to identify precisely or they ...

2021

Shrinking Scale Equidistribution for Monochromatic Random Waves on Compact Manifolds

Matthew De Courcy-Ireland

We prove equidistribution at shrinking scales for the monochromatic ensemble on a compact Riemannian manifold of any dimension. This ensemble on an arbitrary manifold takes a slowly growing spectral window in order to synthesize a random function. With hig ...

OXFORD UNIV PRESS2021

The Surface Quasi-geostrophic Equation With Random Diffusion

Klaus Martin Widmayer

Consider the surface quasi-geostrophic equation with random diffusion, white in time. We show global existence and uniqueness in high probability for the associated Cauchy problem satisfying a Gevrey type bound. This article is inspired by a recent work of ...

OXFORD UNIV PRESS2020

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

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

Conditional probability

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A General Framework for Optimal Data-Driven Optimization

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

Learning in Volatile Environments With the Bayes Factor Surprise

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Stochastic Optimal Control for Autonomous Driving Applications via Polynomial Chaos Expansions

Shrinking Scale Equidistribution for Monochromatic Random Waves on Compact Manifolds

The Surface Quasi-geostrophic Equation With Random Diffusion

Normalized Gaussian path integrals

Robust Generalization via $\alpha$-Mutual Information

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

Normalized Gaussian path integrals

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

Learning in Volatile Environments With the Bayes Factor Surprise

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Robust Generalization via $\alpha$-Mutual Information

A General Framework for Optimal Data-Driven Optimization

Stochastic Optimal Control for Autonomous Driving Applications via Polynomial Chaos Expansions

Shrinking Scale Equidistribution for Monochromatic Random Waves on Compact Manifolds

The Surface Quasi-geostrophic Equation With Random Diffusion

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