Publications related to Randomization

SPEEDING UP KRYLOV SUBSPACE METHODS FOR COMPUTING f(A)b VIA RANDOMIZATION

This work is concerned with the computation of the action of a matrix function f(A), such as the matrix exponential or the matrix square root, on a vector b. For a general matrix A, this can be done by computing the compression of A onto a suitable Krylov ...

Siam Publications2024

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Rachid Guerraoui, Anne-Marie Kermarrec, Sadegh Farhadkhani, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos

We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

STREAMING TENSOR TRAIN APPROXIMATION

Daniel Kressner

Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the streaming tensor train approximation (STTA), a new class of algorithms for approximating a given tensor ' in the tensor train fo ...

Siam Publications2023

Revisiting the Indifferentiability of the Sum of Permutations

Ritam Bhaumik

The sum of two n-bit pseudorandom permutations is known to behave like a pseudorandom function with n bits of security. A recent line of research has investigated the security of two public n-bit permutations and its degree of indifferentiability. Mandal e ...

Springer2023

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

Emotional sounds in space: asymmetrical representation within early-stage auditory areas

Stephanie Clarke

Evidence from behavioral studies suggests that the spatial origin of sounds may influence the perception of emotional valence. Using 7T fMRI we have investigated the impact of the categories of sound (vocalizations; non-vocalizations), emotional valence (p ...

FRONTIERS MEDIA SA2023

Optimal Matching of Random Parts

Thomas Alois Weber

This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...

2022

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Fabio Nobile, Sundar Subramaniam Ganesh

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method ...

2023

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar, Seyedbehzad Bozorgtabar

As distribution shifts are inescapable in realistic clinical scenarios due to inconsistencies in imaging protocols, scanner vendors, and across different centers, well-trained deep models incur a domain generalization problem in unseen environments. Despit ...

PMLR2022

Conjugate heat transfer in isolated granular clusters with interstitial fluid using lattice Boltzmann method

Mehrdad Kiani Oshtorjani

The conjugate heat transfer in mixtures of a fluid and single granular clusters is studied in this paper using a novel lattice Boltzmann method (LBM) programmed for parallel computation on the graphics processing unit (GPU). The LBM is validated for heat c ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Watermarking-based Defense against Adversarial Attacks on Deep Neural Networks

James Richard Larus, Xiaoting Li, Dinghao Wu, Junjun Zhang

The vulnerability of deep neural networks to adversarial attacks has posed significant threats to real-world applications, especially security-critical ones. Given a well-trained model, slight modifications to the input samples can cause drastic changes in ...

IEEE2021

Neural Design Space Exploration

Pierre Dillenbourg, Richard Lee Davis, Kevin Gonyop Kim, Wei Jiang

We have developed a new tool that makes it possible for people with zero programming experience to intentionally and meaningfully explore the latent space of a GAN. This tool was developed to provide a means for designers to explore the "design space" of t ...

2021

Interventions for Softening Can Lead to Hardening of Opinions: Evidence from a Randomized Controlled Trial

Robert West, Andreas Oliver Spitz, Ahmad Abu-Akel

Motivated by the goal of designing interventions for softening polarized opinions on the Web, and building on results from psychology, we hypothesized that people would be moved more easily towards opposing opinions when the latter were voiced by a celebri ...

ASSOC COMPUTING MACHINERY2021

Mixed Nash Equilibria in the Adversarial Examples Game

Rafaël Benjamin Pinot

This paper tackles the problem of adversarial examples from a game theoretic point of view. We study the open question of the existence of mixed Nash equilibria in the zero-sum game formed by the attacker and the classifier. While previous works usually al ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Efficient random number generation techniques for CMOS single-photon avalanche diode array exploiting fast time tagging units

Edoardo Charbon, Samuel Burri

This work presents a technique to produce random bits by exploiting single-photon time of arrival. Two quantum random number generator (QRNG) devices based on the field programmable gate array (FPGA) technology are presented: Randy, which uses one discrete ...

AMER PHYSICAL SOC2020

Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist

Kenneth Younge

We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how ...

MIT Press2020

A Learning-Based Framework for Quantized Compressed Sensing

Volkan Cevher, Rabeeh Karimi Mahabadi, Junhong Lin

Sparse recovery from undersampled random quan- tization measurements is a recent active research topic. Previous work asserts that stable recovery can be guaranteed via the basis pursuit dequantizer (BPDQ) if the measurements number is large enough, consid ...

IEEE Institute of Electrical and Electronics Engineers2019

Protocol for the PreventIT feasibility randomised controlled trial of a lifestyle-integrated exercise intervention in young older adults

Kamiar Aminian, Anisoara Ionescu

Introduction The European population is rapidly ageing. In order to handle substantial future challenges in the healthcare system, we need to shift focus from treatment towards health promotion. The PreventIT project has adapted the Lifestyle-integrated Ex ...

BMJ2019