Publications associées à Linear Bayesian Reinforcement Learning

Bayesian adaptation of chaos representations using variational inference and sampling on geodesics

A novel approach is presented for constructing polynomial chaos representations of scalar quantities of interest (QoI) that extends previously developed methods for adaptation in Homogeneous Chaos spaces. In this work, we develop a Bayesian formulation of ...

ROYAL SOC2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

2018

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

Aude Billard, Nadia Barbara Figueroa Fernandez

We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) on trajectory data. Physical-consistency of the GMM is ensured by imposing a prior on the component assignments biased by a novel similarity metri ...

2018

On the performance of random reshuffling in stochastic learning

Ali H. Sayed, Stefan Vlaski, Bicheng Ying, Kun Yuan

In empirical risk optimization, it has been observed that gradient descent implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data randomly and independently of each other. ...

2017

How does one compute the noise power to simulate real and complex GNSS signals?

Cyril Botteron, Jérôme Leclère

Simulation of GNSS signals is very important to test and validate algorithms. With some algorithms, such as acquisition or tracking, we do not need to simulate a realistic constellation with actual ranges, Doppler or navigation data. We can simply simulate ...

2016

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Stochastic Spectral Descent for Restricted Boltzmann Machines.

Volkan Cevher

Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Learning typically proceeds by using stochastic gradient descent, and the gradients are estimated with sampling methods. However, the gradient estimation is a ...

2015

Linear Bayesian Reinforcement Learning

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Bayesian adaptation of chaos representations using variational inference and sampling on geodesics

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

On the performance of random reshuffling in stochastic learning

How does one compute the noise power to simulate real and complex GNSS signals?

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Shapes from Pixels

Random sampling of bandlimited signals on graphs

Stochastic Spectral Descent for Restricted Boltzmann Machines.

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Random sampling of bandlimited signals on graphs

Bayesian adaptation of chaos representations using variational inference and sampling on geodesics

How does one compute the noise power to simulate real and complex GNSS signals?

Shapes from Pixels

Mirrored Langevin Dynamics

Stochastic Spectral Descent for Restricted Boltzmann Machines.

On the performance of random reshuffling in stochastic learning

Mirrored Langevin Dynamics