output
20082025
most citedThe Future is Big Graphs! A Community View on Graph Processing Systems

157 citations

Showing 2024Show all

8 papers · 1 filter

cs.LG2024★ 4 cited

How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts

Clément Morand, Anne-Laure Ligozat, Aurélie Névéol

The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consu…

stat.ML2024

Learning conditional distributions on continuous spaces

Cyril Bénézet, Ziteng Cheng, Sebastian Jaimungal

We investigate sample-based learning of conditional distributions on multi-dimensional unit boxes, allowing for different dimensions of the feature and target spaces. Our approach…

physics.ao-ph2024

Leveraging deterministic weather forecasts for in-situ probabilistic temperature predictions via deep learning

David Landry, Anastase Charantonis, Claire Monteleoni

We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface te…

cs.LG2024

Automatically Adaptive Conformal Risk Control

Vincent Blot, Anastasios N Angelopoulos, Michael I Jordan +1

Science and technology have a growing need for effective mechanisms that ensure reliable, controlled performance from black-box machine learning algorithms. These performance guara…

cs.CL2024

Towards Universal and Black-Box Query-Response Only Attack on LLMs with QROA

Hussein Jawad, Yassine Chenik, Nicolas J. -B. Brunel

The rapid adoption of Large Language Models (LLMs) has exposed critical security and ethical vulnerabilities, particularly their susceptibility to adversarial manipulations. This p…

cs.DS2024

Cascading-Tree Algorithm for the 0-1 Knapsack Problem (In Memory of Heiner M{ü}ller-Merbach, a Former President of IFORS)

Mahdi Moeini, Daniel Schermer, Oliver Wendt

In operations research, the Knapsack Problem (KP) is one of the classical optimization problems that has been widely studied. The KP has several variants and, in this paper, we add…

cs.LG2024★ 10 cited

Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials

Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +2

Physics-Informed Neural Networks (PINNs) have gained considerable interest in diverse engineering domains thanks to their capacity to integrate physical laws into deep learning mod…

stat.ML2024★ 1 cited

Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees

Edgar Jaber, Vincent Blot, Nicolas Brunel +6

Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in indus…