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Quasi-random numbers for copula models

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Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

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-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

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

MATHICSE Technical Report : Riemannian optimization for high-dimensional tensor completion

Michael Maximilian Steinlechner

Tensor completion aims to reconstruct a high-dimensional data set with a large fraction of missing entries. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem restricte ...

MATHICSE2015

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

We consider the previously unsolved problem of sampling paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all paths and he ...

2013

Masking vs. Multiparty Computation: How Large Is the Gap for AES?

Sebastian Faust

In this paper, we evaluate the performances of state-of-the art higher-order masking schemes for the AES. Doing so, we pay a particular attention to the comparison between specialized solutions introduced exclusively as countermeasures against side-channel ...

2013

Towards a High-Speed Quantum Random Number Generator

Edoardo Charbon, Francesco Regazzoni, Samuel Burri

Randomness is of fundamental importance in various fields, such as cryptography, numerical simulations, or the gaming industry. Quantum physics, which is fundamentally probabilistic, is the best option for a physical random number generator. In this articl ...

2013

Learning Ridge Functions With Randomized Sampling In High Dimensions

Volkan Cevher, Hemant Tyagi

We study the problem of learning ridge functions of the form f(x) = g(aT x), x ∈ ℝd, from random samples. Assuming g to be a twice continuously differentiable function, we leverage techniques from low rank matrix recovery literature to derive a uniform app ...

IEEE2012

Effective Enrichment of Gene Expression Data Sets

Utku Sirin

The ever-growing need for gene-expression data analysis motivates studies in sample generation due to the lack of enough gene-expression data. It is common that there are thousands of genes but only tens or rarely hundreds of samples available. In this pap ...

2012

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2015