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Euclid : Effects of sample covariance on the number counts of galaxy clusters

Publications associées (87)

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Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

2013

Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

IEEE2013

Distributed bearing estimation via matrix completion

Volkan Cevher

We consider bearing estimation of multiple narrow-band plane waves impinging on an array of sensors. For this problem, bearing estimation algorithms such as minimum variance distortionless response (MVDR), multiple signal classification, and maximum likeli ...

2010

Cosmological parameters from large scale structure - geometric versus shape information

Julien Lesgourgues, Jan Hamann

The matter power spectrum as derived from large scale structure (LSS) surveys contains two important and distinct pieces of information: an overall smooth shape and the imprint of baryon acoustic oscillations (BAO). We investigate the separate impact of th ...

2010

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

Single-field inflation constraints from CMB and SDSS data

Julien Lesgourgues, Fabio Finelli, Jan Hamann

We present constraints on canonical single-field inflation derived from WMAP five year, ACBAR, QUAD, BICEP data combined with the halo power spectrum from SDSS LRG7. Models with a non-scale-invariant spectrum and a red tilt n(S) < 1 are now preferred over ...

2010

Gaussian Covariance and Scalable Variational Inference

Matthias Seeger

We analyze computational aspects of variational approximate inference techniques for sparse linear models, which have to be understood to allow for large scale applications. Gaussian covariances play a key role, whose approximation is computationally hard. ...

Omnipress2010

Likelihood-Based Inference for Max-Stable Processes

Simone Padoan

The last decade has seen max-stable processes emerge as a common tool for the statistical modeling of spatial extremes However, their application is complicated due to the unavailability of the multivariate density function and so likehhood-based methods r ...

American Statistical Association2010