Publications related to Adaptive Filters with Error Nonlinearities: Mean-Square Analysis and Optimum Design

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

Shape constrained splines as transparent black-box models for bioprocess modeling

Dominique Bonvin, Julien Léo Billeter, Sriniketh Srinivasan

Empirical model identification for biological systems is a challenging task due to the combined effects of complex interactions, nonlinear effects, and lack of specific measurements. In this context, several researchers have provided tools for experimental ...

Elsevier2017

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

Timm Faulwasser

The present contribution demonstrates the applicability of polynomial chaos expansion to stochastic (optimal) AC power flow problems that arise in the operation of power grids. For rectangular power flow, polynomial chaos expansion together with Galerkin p ...

IEEE2016

Learning Search Strategies from Human Demonstrations

Guillaume Pierre Luc De Chambrier

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

EPFL2016

Interactive Design of Probability Density Functions for Shape Grammars

Mark Pauly, Duygu Ceylan, Boris Neubert, Stefan Lienhard

A shape grammar defines a procedural shape space containing a variety of models of the same class, e.g. buildings, trees, furniture, airplanes, bikes, etc. We present a framework that enables a user to interactively design a probability density function (p ...

Assoc Computing Machinery2015

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function u in multivariate polynomial spaces

P_\Lambda := span\{y \mapsto y^\nu | \nu \in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma := [-1,1]^d

, based on the sa ...

MATHICSE2015

Adaptive polynomial approximation by means of random discrete least squares

Giovanni Migliorati

We address adaptive multivariate polynomial approximation by means of the discrete least-squares method with random evaluations, to approximate in the L2 probability sense a smooth function depending on a random variable distributed according to a given pr ...

Springer2015

A Geometric View on Constrained M-Estimators

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

We study the estimation error of constrained M-estimators, and derive explicit upper bounds on the expected estimation error determined by the Gaussian width of the constraint set. Both of the cases where the true parameter is on the boundary of the constr ...

2015

Exploring Variability in Reflood Simulation Results: an Application of Functional Data Analysis

Andreas Pautz, Damar Canggih Wicaksono, Omar Zerkak

In this paper we followed the functional data analysis (FDA) paradigm to summarize the output of computer code simulating a reflood transient. The paradigm stands on the assumption that the data (code output) is generated by a smooth, albeit complex, proce ...

2014

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Michaël Unser, Arash Amini, Emrah Bostan, Ulugbek Kamilov

We consider continuous-time sparse stochastic processes from which we have only a finite number of noisy/noiseless samples. Our goal is to estimate the noiseless samples (denoising) and the signal in-between (interpolation problem). By relying on tools fro ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Adaptive Filters with Error Nonlinearities: Mean-Square Analysis and Optimum Design

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Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Shape constrained splines as transparent black-box models for bioprocess modeling

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

Learning Search Strategies from Human Demonstrations

Interactive Design of Probability Density Functions for Shape Grammars

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Adaptive polynomial approximation by means of random discrete least squares

A Geometric View on Constrained M-Estimators

Exploring Variability in Reflood Simulation Results: an Application of Functional Data Analysis

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Interactive Design of Probability Density Functions for Shape Grammars

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Exploring Variability in Reflood Simulation Results: an Application of Functional Data Analysis

Adaptive polynomial approximation by means of random discrete least squares

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

A Geometric View on Constrained M-Estimators

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Shape constrained splines as transparent black-box models for bioprocess modeling

Learning Search Strategies from Human Demonstrations