Publications related to Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures

Learning Robot Manipulation Tasks with Task-Parameterized Semi-Tied Hidden Semi-Markov Model

In this paper, we investigate the semi-tied Gaussian mixture models for robust learning and adaptation of robot manipulation tasks. We make use of the spatial and temporal correlation in the data by tying the covariance matrices of the mixture model with c ...

2016

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

An Inner-Product Calculus for Periodic Functions and Curves

Michaël Unser, Daniel Andreas Schmitter, Anaïs Laure Marie-Thérèse Badoual

Our motivation is the design of efficient algorithms to process closed curves represented by basis functions or wavelets. To that end, we introduce an inner-product calculus to evaluate correlations and

L _{ 2 }

distances between such curves. In partic ...

2016

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Michaël Unser, John Paul Ward, Aurélien Bourquard, Hagay Kirshner

We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and sto ...

Ieee-Inst Electrical Electronics Engineers Inc2014

On the probability distribution of distributed optimization strategies

Ali H. Sayed

We study the steady-state probability distribution of diffusion and consensus strategies that employ constant step-sizes to enable continuous adaptation and learning. We show that, in the small step-size regime, the estimation error at each agent approache ...

IEEE2013

Investigating Reliability on Fuel Cell Model Identification. Part III: Behavior of Assessment Criteria and Limits in Identification

Daniel Favrat, Jan Van Herle, Leonidas Tsikonis

The present paper is the third and last part of an investigation on what determines reliability in fuel cell model identification. In continuation to the effect of experimental design (Part I) and a process method for stochastic calculation of a model's pa ...

Wiley-V C H Verlag Gmbh2013

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

Andreas Krause, Matthias Seeger

Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low norm in a reproducing ...

Institute of Electrical and Electronics Engineers2012

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Andreas Krause, Matthias Seeger

Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve ...

Omnipress2010

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Andreas Krause, Matthias Seeger

Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve ...

2010

Exponential Natural Evolution Strategies

Daniël Pieter Wierstra, Tom Schaul

The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization by following the natural gradient of the expected fitness. Like the well-known CMA-ES, the most competitive algorithm in the field, NES c ...

2010

Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures

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Learning Robot Manipulation Tasks with Task-Parameterized Semi-Tied Hidden Semi-Markov Model

Dimensionality Reduction in Dynamic Optimization under Uncertainty

An Inner-Product Calculus for Periodic Functions and Curves

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

On the probability distribution of distributed optimization strategies

Investigating Reliability on Fuel Cell Model Identification. Part III: Behavior of Assessment Criteria and Limits in Identification

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Exponential Natural Evolution Strategies

Investigating Reliability on Fuel Cell Model Identification. Part III: Behavior of Assessment Criteria and Limits in Identification

On the probability distribution of distributed optimization strategies

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

An Inner-Product Calculus for Periodic Functions and Curves

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Learning Robot Manipulation Tasks with Task-Parameterized Semi-Tied Hidden Semi-Markov Model

Exponential Natural Evolution Strategies