Concept

Stability (learning theory)

Publications associées (59)

Fixing the problems of deep neural networks will require better training data and learning algorithms

Martin Schrimpf, Adrien Christophe Doerig, Matthias Bethge, Jianghao Liu, Kuntal Ghosh

Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs ar ...

Cambridge2023

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

Olga Fink, Mina Montazeri

This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment. In this problem, each unit aims to find a bidding strategy that maximizes its reve ...

ELSEVIER SCI LTD2023

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Meritxell Bach Cuadra, Erick Jorge Canales Rodriguez, Gabriel Girard, Hamza Kebiri

Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ a ...

FRONTIERS MEDIA SA2022

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Rachid Guerraoui, Sadegh Farhadkhani, Oscar Jean Olivier Villemaud, Le Nguyen Hoang

To study the resilience of distributed learning, the “Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes ...

PMLR2022

Practical Byzantine-resilient Stochastic Gradient Descent

Sébastien Louis Alexandre Rouault

Algorithms are everywhere.The recipe for the frangipane cake is an algorithm.If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven.Now, what ...

EPFL2022

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?

Rachid Guerraoui, Nirupam Gupta, John Stephan, Sébastien Louis Alexandre Rouault, Rafaël Benjamin Pinot

This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible with both diff ...

ASSOC COMPUTING MACHINERY2021

Social NCE: Contrastive Learning of Socially-aware Motion Representations

Alexandre Massoud Alahi, Yuejiang Liu

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

2021

Optimal Adversarial Policies in the Multiplicative Learning System With a Malicious Expert

Negar Kiyavash, Seyed Jalal Etesami

We consider a learning system based on the conventional multiplicative weight ( MW) rule that combines experts' advice to predict a sequence of true outcomes. It is assumed that one of the experts is malicious and aims to impose the maximum loss on the sys ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Predictive user-based relocation through incentives in one-way car-sharing systems

Nikolaos Geroliminis, Patrick Stefan Adriaan Stokkink

Car-sharing systems are an attractive alternative to private vehicles due to their benefits in terms of mobility and sustainability. However, the distribution of vehicles throughout the network in one-way systems is disturbed due to asymmetry and stochasti ...

2021

Disparity Between Batches as a Signal for Early Stopping

Patrick Thiran, Mahsa Forouzesh

We propose a metric for evaluating the generalization ability of deep neural networks trained with mini-batch gradient descent. Our metric, called gradient disparity, is the l2 norm distance between the gradient vectors of two mini-batches drawn from the t ...

Springer2021

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