Design and implementation of a simulation environment for network virtualization. - Marc De Leenheer; Jens Buysse; Kevin Mets; Bart Dhoedt; Chris Develder (CAMAD, 2011)

Integrated simulation of power and communication networks for smart grid applications. - Kevin Mets; Tom Verschueren; Chris Develder; Tine L. Vandoorn; Lieven Vandevelde (CAMAD, 2011)

Assessment and mitigation of voltage violations by solar panels in a residential distribution grid. - Tom Verschueren; Kevin Mets; Bart Meersman; Matthias Strobbe; Chris Develder; Lieven Vandevelde (SmartGridComm, 2011)

Infrastructure for collaborating data-researchers in a Smart Grid pilot. - Wouter Labeeuw; Sven Claessens; Kevin Mets; Chris Develder; Geert Deconinck (ISGT Europe, 2012)

Distributed multi-agent algorithm for residential energy management in smart grids. - Kevin Mets; Matthias Strobbe; Tom Verschueren; Thomas Roelens; Filip De Turck; Chris Develder (NOMS, 2012)

Design and evaluation of an architecture for future smart grid service provisioning. - Matthias Strobbe; Tom Verschueren; Kevin Mets; Stijn Melis; Chris Develder; Filip De Turck; Thierry Pollet; Stijn Van de Veire (NOMS, 2012)

Distributed smart charging of electric vehicles for balancing wind energy. - Kevin Mets; Filip De Turck; Chris Develder (SmartGridComm, 2012)

Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid. - Kevin Mets; Reinhilde D'hulst; Chris Develder (J. Commun. Networks, 2012)

Design of a management infrastructure for smart grid pilot data processing and analysis. - Matthias Strobbe; Tom Verschueren; Stijn Melis; Dieter Verslype; Kevin Mets; Filip De Turck; Chris Develder (IM, 2013)

Combining Power and Communication Network Simulation for Cost-Effective Smart Grid Analysis. - Kevin Mets; Juan Aparicio; Chris Develder (IEEE Commun. Surv. Tutorials, 2014)

Two-Stage Load Pattern Clustering Using Fast Wavelet Transformation. - Kevin Mets; Frederick Depuydt; Chris Develder (IEEE Trans. Smart Grid, 2016)

HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory. - Jakob Struye; Kevin Mets; Steven Latré (BNAIC/BENELEARN, 2019)

Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning. - Matthias Hutsebaut-Buysse; Kevin Mets; Steven Latré (CoRR, 2019)

Language Grounded Task-Adaptation in Reinforcement Learning. - Matthias Hutsebaut-Buysse; Kevin Mets; Steven Latré (ESANN, 2020)

Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning. - Matthias Hutsebaut-Buysse; Kevin Mets; Steven Latré (CoRR, 2020)

Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System. - Simon Vanneste; Gauthier de Borrekens; Stig Bosmans; Astrid Vanneste; Kevin Mets; Siegfried Mercelis; Steven Latré; Peter Hellinckx (3PGCIC, 2021)

Mixed Cooperative-Competitive Communication Using Multi-agent Reinforcement Learning. - Astrid Vanneste; Wesley Van Wijnsberghe; Simon Vanneste; Kevin Mets; Siegfried Mercelis; Steven Latré; Peter Hellinckx (3PGCIC, 2021)

Object Detection with Semi-supervised Adversarial Domain Adaptation for Real-Time Edge Devices. - Mattias Billast; Tom De Schepper; Kevin Mets; Peter Hellinckx; José Oramas; Steven Latré (BNAIC/BENELEARN, 2021)

Task Independent Capsule-Based Agents for Deep Q-Learning. - Akash Singh; Tom De Schepper; Kevin Mets; Peter Hellinckx; José Oramas; Steven Latré (BNAIC/BENELEARN, 2021)

Disagreement Options: Task Adaptation Through Temporally Extended Actions. - Matthias Hutsebaut-Buysse; Tom De Schepper; Kevin Mets; Steven Latré (ECML/PKDD (1), 2021)

Low Cost Player Tracking in Field Hockey. - Henrique Duarte Moura; Leonid Kholkine; Laurens Van Damme; Kevin Mets; Christiaan Leysen; Tom De Schepper; Peter Hellinckx; Steven Latré (MLSA@PKDD/ECML, 2021)

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs. - Paola Soto; Miguel Camelo; Kevin Mets; Francesc Wilhelmi; David Góez; Luis Alejandro Fletscher; Natalia Gaviria; Peter Hellinckx; Juan Felipe Botero; Steven Latré (Sensors, 2021)

On-device Deep Learning Location Category Inference Model. - Gadzhi Musaev; Kevin Mets; Rokas Tamosiunas; Vadim Uvarov; Tom De Schepper; Peter Hellinckx (BNAIC/BENELEARN, 2022)

Object Detection To Enable Autonomous Vessels On European Inland Waterways. - Mattias Billast; Robin Janssens; Astrid Vanneste; Simon Vanneste; Olivier Vasseur; Ali Anwar 002; Kevin Mets; Tom De Schepper; José Oramas; Steven Latré; Peter Hellinckx (IECON, 2022)

Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways. - Astrid Vanneste; Simon Vanneste; Olivier Vasseur; Robin Janssens; Mattias Billast; Ali Anwar 002; Kevin Mets; Tom De Schepper; Siegfried Mercelis; Peter Hellinckx (IECON, 2022)

Deep Set Conditioned Latent Representations for Action Recognition. - Akash Singh; Tom De Schepper; Kevin Mets; Peter Hellinckx; José Oramas; Steven Latré (VISIGRAPP (5: VISAPP), 2022)

An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning. - Astrid Vanneste; Simon Vanneste; Kevin Mets; Tom De Schepper; Siegfried Mercelis; Steven Latré; Peter Hellinckx (CoRR, 2022)

Structured Exploration Through Instruction Enhancement for Object Navigation. - Matthias Hutsebaut-Buysse; Kevin Mets; Tom De Schepper; Steven Latré (CoRR, 2022)

Hierarchical Reinforcement Learning: A Survey and Open Research Challenges. - Matthias Hutsebaut-Buysse; Kevin Mets; Steven Latré (Mach. Learn. Knowl. Extr., 2022)

Multi-Agent Counterfactual Communication Using Difference Rewards Policy Gradients. - Simon Vanneste; Astrid Vanneste; Tom De Schepper; Siegfried Mercelis; Peter Hellinckx; Kevin Mets (BNAIC/BENELEARN, 2023)

Towards graph-based forecasting with attention for real-world restaurant sales. - Henrique Duarte Moura; Leonid Kholkine; Lynn D'eer; Kevin Mets; Tom De Schepper (ICDM (Workshops), 2023)

Directed Real-World Learned Exploration. - Matthias Hutsebaut-Buysse; Ferran Gebelli Guinjoan; Erwin Rademakers; Steven Latré; Abdellatif Bey-Temsamani; Kevin Mets; Erik Mannens; Tom De Schepper (IROS, 2023)

Human Motion Prediction on the IKEA-ASM Dataset. - Mattias Billast; Kevin Mets; Tom De Schepper; José Oramas; Steven Latré (VISIGRAPP (5: VISAPP), 2023)

Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning. - Astrid Vanneste; Thomas Somers 002; Simon Vanneste; Kevin Mets; Tom De Schepper; Siegfried Mercelis; Peter Hellinckx (CoRR, 2023)

An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning. - Astrid Vanneste; Simon Vanneste; Kevin Mets; Tom De Schepper; Siegfried Mercelis; Peter Hellinckx (CoRR, 2023)

Dataset condensation with latent quantile matching. - Wei Wei; Tom De Schepper; Kevin Mets (CVPR Workshops, 2024)

Online Adaptation of Compressed Models by Pre-Training and Task-Relevant Pruning. - Thomas Avé; Matthias Hutsebaut-Buysse; Wei Wei; Kevin Mets (ESANN, 2024)

Policy Compression for Low-Power Intelligent Scaling in Software-Based Network Architectures. - Thomas Avé; Paola Soto; Miguel Camelo; Tom De Schepper; Kevin Mets (NOMS, 2024)

Physical Ergonomics Anticipation with Human Motion Prediction. - Mattias Billast; Jonas De Bruyne; Klaas Bombeke; Tom De Schepper; Kevin Mets (VISIGRAPP (4): VISAPP, 2024)

Unsupervised Domain Adaptation for Human Pose Action Recognition. - Mattias Billast; Tom De Schepper; Kevin Mets (VISIGRAPP (2): VISAPP, 2024)

Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition. - Wei Wei; Tom De Schepper; Kevin Mets (VISIGRAPP (2): VISAPP, 2024)

Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning. - Amber Cassimon; Siegfried Mercelis; Kevin Mets (CoRR, 2024)

Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices. - Thomas Avé; Tom De Schepper; Kevin Mets (Sensors, 2024)

Action-Conditioned Hamiltonian Generative Networks (AC-HGN) for Supervised and Reinforcement Learning. - Arne Troch; Kevin Mets; Siegfried Mercelis (L4DC, 2025)

Designing a Classifier for Active Fire Detection From Multispectral Satellite Imagery Using Neural Architecture Search. - Amber Cassimon; Phil Reiter; Siegfried Mercelis; Kevin Mets (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2025)

Successor Clusters: A Behavior Basis for Unsupervised Zero-Shot Reinforcement Learning. - Louis Bagot; Lucas Nunes Alegre; Steven Latré; Kevin Mets; Bruno Castro da Silva (Trans. Mach. Learn. Res., 2025)

Scalable reinforcement learning-based neural architecture search. - Amber Cassimon; Siegfried Mercelis; Kevin Mets (Neural Comput. Appl., 2025)

Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review. - Bruno Deprez; Wei Wei; Wouter Verbeke; Bart Baesens; Kevin Mets; Tim Verdonck (CoRR, 2025)

GPI-tree search: algorithms for decision-time planning with the general policy improvement theorem. - Louis Bagot; Lynn D'eer; Steven Latré; Tom De Schepper; Kevin Mets (Neural Comput. Appl., 2025)