Publications related to Sampling-Based AQP in Modern Analytical Engines

A systematic review and meta-analysis of the efficacy of aortic anastomotic devices

Nikolaos Stergiopoulos, Dimitrios Schizas

Objective: One of the factors contributing to complications related to open repair of the aorta is the construction of a hand-sewn anastomosis. Aortic anastomotic devices (AADs), such as the intraluminal ringed graft (IRG), and the anastomotic stenting tec ...

MOSBY-ELSEVIER2019

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

On the performance of random reshuffling in stochastic learning

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

In empirical risk optimization, it has been observed that gradient descent implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data randomly and independently of each other. ...

2017

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

MATHICSE2017

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Matthias Grossglauser, Lucas Maystre

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal sol ...

2017

Electroencephalogram (EEG) Frontal Asymmetry and Error-Related Signals Are Potential Markers of Internalizing and Externalizing Behaviors in Children: A Meta-Analysis

Michael Herzog, Ophélie Gladys Favrod

Objectives: The frontal hemispheric asymmetry and error-related components, as measured by EEG, are often considered to be biological correlates or “indicators of risk” for psychopathology. Here, we performed a meta-analysis to define specific patterns of ...

2017

Compressive Spectral Clustering

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a fea ...

2016

Locally differentially-private distribution estimation

Michael Christoph Gastpar, Adriano Pastore

We consider a setup in which confidential i.i.d. samples X1, ..., Xn from an unknown discrete distribution PX are passed through a discrete memoryless privatization channel (a.k.a. mechanism) which guarantees an epsilon-level of local differential privacy. ...

Ieee2016

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

Sampling-Based AQP in Modern Analytical Engines

Graph Chatbot

Chat with Graph Search

A systematic review and meta-analysis of the efficacy of aortic anastomotic devices

Safe Adaptive Importance Sampling

Sampling at unknown locations, with an application in surface retrieval

On the performance of random reshuffling in stochastic learning

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Electroencephalogram (EEG) Frontal Asymmetry and Error-Related Signals Are Potential Markers of Internalizing and Externalizing Behaviors in Children: A Meta-Analysis

Compressive Spectral Clustering

Locally differentially-private distribution estimation

Random sampling of bandlimited signals on graphs

A systematic review and meta-analysis of the efficacy of aortic anastomotic devices

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Safe Adaptive Importance Sampling

Compressive Spectral Clustering

Locally differentially-private distribution estimation

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Electroencephalogram (EEG) Frontal Asymmetry and Error-Related Signals Are Potential Markers of Internalizing and Externalizing Behaviors in Children: A Meta-Analysis

Random sampling of bandlimited signals on graphs

On the performance of random reshuffling in stochastic learning

Sampling at unknown locations, with an application in surface retrieval