Publications related to Safe Adaptive Importance Sampling

Stochastic gradient descent with finite samples sizes

Ali H. Sayed, Stefan Vlaski, Bicheng Ying, Kun Yuan

The minimization of empirical risks over finite sample sizes is an important problem in large-scale machine learning. A variety of algorithms has been proposed in the literature to alleviate the computational burden per iteration at the expense of converge ...

IEEE2016

On Selection of Samples in Algebraic Attacks and a New Technique to Find Hidden Low Degree Equations

Serge Vaudenay, Pouyan Sepehrdad, Petr Susil

The best way of selecting samples in algebraic attacks against block ciphers is not well explored and understood. We introduce a simple strategy for selecting the plaintexts and demonstrate its strength by breaking reduced-round KATAN, LBLOCK and SIMON. Fo ...

Springer Verlag2016

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

Association of PCK1 with Body Mass Index and Other Metabolic Features in Patients With Psychotropic Treatments

Pierre Magistretti, Aurélie Delacrétaz

Weight gain is a major health problem among psychiatric populations. It implicates several receptors and hormones involved in energy balance and metabolism. Phosphoenolpyruvate carboxykinase 1 is a rate-controlling enzyme involved in gluconeogenesis, glyce ...

Lippincott, Williams & Wilkins2015

MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets

Agnieszka Katarzyna Miedlar

The first algorithms for computing the minimal Geršgorn set were developed by Varga et all. in [17] for the use on small and medium size (dense) matrices. Here, we first discuss the existing methods and present a new approach based on the modified Newton’s ...

MATHICSE2015

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

Full-wavefield modeling and reverse time migration of laser ultrasound data: A feasibility study

Thomas Emmanuel Blum

Laser ultrasound (LU) data acquired on cylindrical core samples effectively probe the physical properties of geologic materials. Although most LU analyses focus on estimating and inverting traveltimes of direct arrivals, it is important to recognize that L ...

2015

Too much success for recent groundbreaking epigenetic experiments

Gregory Francis

An article reporting statistical evidence for epigenetic transfer of learned behavior has important implications, if true. With random sampling, real effects do not always result in rejection of the null hypothesis, but the reported experiments were unifor ...

Genetics Society of America2014

Sampling based on timing: Time encoding machines on shift-invariant subspaces

Martin Vetterli, David Gontier

Sampling information using timing is an approach that has received renewed attention in sampling theory. The question is how to map amplitude information into the timing domain. One such encoder, called time encoding machine, was introduced by Lazar and To ...

Elsevier2014

Measuring the relative effect of factors affecting species distribution model predictions

Anthony Christopher Davison, Emeric Rolland Georges Thibaud

Species distribution models are increasingly used to address conservation questions, so their predictive capacity requires careful evaluation. Previous studies have shown how individual factors used in model construction can affect prediction. Although som ...

Wiley-Blackwell2014

Safe Adaptive Importance Sampling

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Stochastic gradient descent with finite samples sizes

On Selection of Samples in Algebraic Attacks and a New Technique to Find Hidden Low Degree Equations

Random sampling of bandlimited signals on graphs

Association of PCK1 with Body Mass Index and Other Metabolic Features in Patients With Psychotropic Treatments

MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets

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

Full-wavefield modeling and reverse time migration of laser ultrasound data: A feasibility study

Too much success for recent groundbreaking epigenetic experiments

Sampling based on timing: Time encoding machines on shift-invariant subspaces

Measuring the relative effect of factors affecting species distribution model predictions

On Selection of Samples in Algebraic Attacks and a New Technique to Find Hidden Low Degree Equations

Full-wavefield modeling and reverse time migration of laser ultrasound data: A feasibility study

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

Measuring the relative effect of factors affecting species distribution model predictions

Stochastic gradient descent with finite samples sizes

Association of PCK1 with Body Mass Index and Other Metabolic Features in Patients With Psychotropic Treatments

Too much success for recent groundbreaking epigenetic experiments

Sampling based on timing: Time encoding machines on shift-invariant subspaces

Random sampling of bandlimited signals on graphs

MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets