Pascal Bouvry
359 papers 109 citations h-index 5 1995–2026
FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences
Federated Learning (FL) is a distributed machine learning strategy, developed for settings where training data is owned by distributed devices and cannot be shared. FL circumvents this constraint...
Training Green AI Models Using Elite Samples
The substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices. On the one hand, data-centric approaches...
Strategic Decisions Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective
Strategic Decision-Making is always challenging because it is inherently uncertain, ambiguous, risky, and complex. It is the art of possibility. We develop a systematic taxonomy of decision-making...
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
Transformer-based models for time series forecasting (TSF) have attracted significant attention in recent years due to their effectiveness and versatility. However, these models often require...
Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
Alongside the continuous process of improving AI performance through the development of more sophisticated models, researchers have also focused their attention to the emerging concept of...
Trustworthiness of Stochastic Gradient Descent in Distributed Learning
Distributed learning (DL) uses multiple nodes to accelerate training, enabling efficient optimization of large-scale models. Stochastic Gradient Descent (SGD), a key optimization algorithm, plays...
Transformer Multivariate Forecasting: Less is More?
In the domain of multivariate forecasting, transformer models stand out as powerful apparatus, displaying exceptional capabilities in handling messy datasets from real-world contexts. However, the...
Lightweight Trustworthy Distributed Clustering
Ensuring data trustworthiness within individual edge nodes while facilitating collaborative data processing poses a critical challenge in edge computing systems (ECS), particularly in...
Constraint Model for the Satellite Image Mosaic Selection Problem
Satellite imagery solutions are widely used to study and monitor different regions of the Earth. However, a single satellite image can cover only a limited area. In cases where a larger area of...
Survey of Trustworthy AI: A Meta Decision of AI
When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other...
A Variant of Concurrent Constraint Programming on GPU
The number of cores on graphical computing units (GPUs) is reaching thousands nowadays, whereas the clock speed of processors stagnates. Unfortunately, constraint programming solvers do not take...
Challenges in Automatic Software Optimization: the Energy Efficiency Case
With the advent of the Exascale capability allowing supercomputers to perform at least $10^{18}$ IEEE 754 Double Precision (64 bits) operations per second, many concerns have been raised regarding...
Detecting Target-Area Link-Flooding DDoS Attacks using Traffic Analysis and Supervised Learning
A novel class of extreme link-flooding DDoS (Distributed Denial of Service) attacks is designed to cut off entire geographical areas such as cities and even countries from the Internet by...
A Bayesian Optimization Approach for Attenuation Correction in SPECT Brain Imaging
Photon attenuation and scatter are the two main physical factors affecting the diagnostic quality of SPECT in its applications in brain imaging. In this work, we present a novel Bayesian...
Transforming Collaboration Data into Network Layers for Enhanced Analytics
We consider the problem of automatically generating networks from data of collaborating researchers. The objective is to apply network analysis on the resulting network layers to reveal...
Challenges on Probabilistic Modeling for Evolving Networks
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity...
Design and classification of dynamic multi-objective optimization problems
In this work we provide a formal model for the different time-dependent components that can appear in dynamic multi-objective optimization problems, along with a classification of these...
Optimal Design of Ad Hoc Injection Networks by Using Genetic Algorithms
This work aims at optimizing injection networks, which consist in adding a set of long-range links (called bypass links) in mobile multi-hop ad hoc networks so as to improve connectivity and...