Publications related to High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomi ...

Wiley-Blackwell2016

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Matthieu Martin Jean-André Simeoni

Estimating intensity fields of stochastic phenomenons is of crucial interest in many scientific applications. Typical experimental setups involve an acquisition system, that subsequently filters and samples the probed intensity field. This equivalently def ...

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

PCA using graph total variation

Pierre Vandergheynst, Nathanaël Perraudin, Benjamin Ricaud, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received sig- nificant attention of the signal processing and machine learning com- munity in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome th ...

Ieee2016

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Ting Zhu

The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of ...

EPFL2015

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

Generalized Poisson Summation Formula for Tempered Distributions

Michaël Unser, Quy Ha Nguyen

The Poisson summation formula (PSF), which relates the sampling of an analog signal with the periodization of its Fourier transform, plays a key role in the classical sampling theory. In its current forms, the formula is only applicable to a limited class ...

IEEE2015

Analyte quantification with Comprehensive Two-Dimensional Gas Chromatography: Assessment of methods for baseline correction, peak delineation, and matrix effect elimination for real samples

Jeremy Samuel Arey, Jonas Gros, Saer Samanipour, Petros Dimitriou Christidis, Auréline Grange

Comprehensive two-dimensional gas chromatography (GC x GC) is used widely to separate and measure organic chemicals in complex mixtures. However, approaches to quantify analytes in real, complex samples have not been critically assessed. We quantified 7 PA ...

Elsevier2015

UAVs using Bayesian Optimization to LocateWiFi Devices

Bixio Rimoldi, Stefano Rosati, Mohammad Emtiyaz Khan, Mattia Carpin

We address the problem of localizing non-collaborative WiFi devices in a large region. Our main motive is to localize humans by localizing their WiFi devices, e.g. during search-and-rescue operations after a natural disaster. We use an active sensing appro ...

2015

Bilinear Generalized Approximate Message Passing—Part II: Applications

Volkan Cevher

In this paper, we extend the generalized approximate message passing (G-AMP) approach, originally proposed for high-dimensional generalized-linear regression in the context of compressive sensing, to the generalized-bilinear case. In Part I of this two-par ...

Ieee-Inst Electrical Electronics Engineers Inc2014

High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

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Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Shapes from Pixels

PCA using graph total variation

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Random sampling of bandlimited signals on graphs

Generalized Poisson Summation Formula for Tempered Distributions

Analyte quantification with Comprehensive Two-Dimensional Gas Chromatography: Assessment of methods for baseline correction, peak delineation, and matrix effect elimination for real samples

UAVs using Bayesian Optimization to LocateWiFi Devices

Bilinear Generalized Approximate Message Passing—Part II: Applications

Shapes from Pixels

Generalized Poisson Summation Formula for Tempered Distributions

Random sampling of bandlimited signals on graphs

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

PCA using graph total variation

Bilinear Generalized Approximate Message Passing—Part II: Applications

Analyte quantification with Comprehensive Two-Dimensional Gas Chromatography: Assessment of methods for baseline correction, peak delineation, and matrix effect elimination for real samples

UAVs using Bayesian Optimization to LocateWiFi Devices

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies