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Approximate Steepest Coordinate Descent

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Modelling and design of CMOS SPAD sensors for quantum random number generation

Pouyan Keshavarzian

Modern digital connectivity has necessitated the creation of robust methods for securely storing and transferring data. At the heart of all security infrastructure is the random number generator (RNG). While random numbers find use in a variety of applicat ...

EPFL2023

On the Double Descent of Random Features Models Trained with SGD

Volkan Cevher, Fanghui Liu

We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...

2022

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

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 ...

2020

Multivariate extremes over a random number of observations

Simone Padoan, Stefano Rizzelli

The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as max ...

WILEY2020

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019

Topology classification with deep learning to improve real-time event selection at the LHC

Roberto Castello

We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real-time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class ...

2019

Some Proofs of Conditional and Joint Biases in RC4 Stream Cipher

Subhadeep Banik

In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifi- cations and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 ...

2018

Theoretical Understanding of Some Conditional and Joint Biases in RC4 Stream Cipher

Subhadeep Banik

In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifications and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 in ...

IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG2018

Asynchronous adaptive networks

Ali H. Sayed

In a recent article [1] we surveyed advances related to adaptation, learning, and optimization over synchronous networks. Various distributed strategies were discussed that enable a collection of networked agents to interact locally in response to streamin ...

Elsevier2018