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Near-Optimal Noisy Group Testing via Separate Decoding of Items

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Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, John Stephan, Sadegh Farhadkhani, Le Nguyen Hoang, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Leaking Secrets Through Compressed Caches

Daniel Felipe Forero Sanchez

We offer the first security analysis of cache compression, a promising architectural technique that is likely to appear in future mainstream processors. We find that cache compression has novel security implications because the compressibility of a cache l ...

IEEE COMPUTER SOC2021

Extrapolation for Large-batch Training in Deep Learning

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Lingjing Kong

Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data. A major roadblock faced when increasing the batc ...

2020

Two-photon triggered photorelease of caged compounds from multifunctional harmonic nanoparticles

Sandrine Gerber, Laure Menin, Daniel Ortiz Trujillo, Jérémy Vuilleumier, Raphaël Jovita De Matos, Luigi Bonacina

The design of stimuli-responsive nanocarriers has raised much attention to achieve higher local concentration of therapeutics and mitigate the appearance of drug resistance. The combination of imaging properties and controlled photorelease of active molecu ...

2019

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and more. In this pa ...

2018

High-Dimensional Inference on Dense Graphs with Applications to Coding Theory and Machine Learning

Mohamad Baker Dia

We are living in the era of "Big Data", an era characterized by a voluminous amount of available data. Such amount is mainly due to the continuing advances in the computational capabilities for capturing, storing, transmitting and processing data. However, ...

EPFL2018

The small RNA RyhB is a regulator of cytochrome expression in Shewanella oneidensis

Rizlan Bernier-Latmani, Karin Lederballe Meibom

Shewanella oneidensis produces an extensive electron transfer network that results in metabolic flexibility. A large number of c-type cytochromes are expressed by S. oneidensis and these function as the fundamental electron transport chain proteins. Althou ...

2018

Rake, Peel, Sketch

Robin Scheibler

The prototypical signal processing pipeline can be divided into four blocks. Representation of the signal in a basis suitable for processing. Enhancement of the meaningful part of the signal and noise reduction. Estimation of important statistical properti ...

EPFL2017

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We study jump-penalized estimators based on least absolute deviations which are often referred to as Potts estimators. They are estimators for a parsimonious piecewise constant representation of noisy data having a noise distribution which has heavier tail ...

Oxford University Press2017

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Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available sta ...

Wiley-Blackwell2017