Matthia Sabatelli

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 32

Top Topics

Transfer Learning

Reinforcement Learning

Top Venues

Publications

Alexander Hill, Marc Groefsema, Matthia Sabatelli, Raffaella Carloni, Marco Grzegorczyk
Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning. ICAART (3) (2024)
Tsegaye Misikir Tashu, Eduard-Raul Kontos, Matthia Sabatelli, Matias Valdenegro-Toro
Mapping Transformer Leveraged Embeddings for Cross-Lingual Document Representation. CoRR (2024)
Erwan Escudie, Matthia Sabatelli, Jilles Dibangoye
ε-Optimally Solving Zero-Sum POSGs. CoRR (2024)
Arthur Müller, Felix Grumbach, Matthia Sabatelli
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling. CoRR (2024)
Marius Captari, Remo Sasso, Matthia Sabatelli
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts. CoRR (2024)
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Bart van Marum, Matthia Sabatelli, Hamidreza Kasaei
Learning Perceptive Bipedal Locomotion over Irregular Terrain. CoRR (2023)
Arthur Müller, Matthia Sabatelli
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning. ITSC (2023)
Arthur Müller, Matthia Sabatelli
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning. CoRR (2023)
Arthur Müller, Matthia Sabatelli
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking. ITSC (2022)
Julius Wagenbach, Matthia Sabatelli
Factors of Influence of the Overestimation Bias of Q-Learning. CoRR (2022)
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. CoRR (2022)
Matias Valdenegro-Toro, Matthia Sabatelli
Machine Learning Students Overfit to Overfitting. CoRR (2022)
Vincent Tonkes, Matthia Sabatelli
How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification. CoRR (2022)
Matias Valdenegro-Toro, Matthia Sabatelli
Machine Learning Students Overfit to Overfitting. Teaching ML (2022)
Vincent Tonkes, Matthia Sabatelli
How Well Do Vision Transformers (VTs) Transfer to the Non-natural Image Domain? An Empirical Study Involving Art Classification. ECCV Workshops (1) (2022)
Arthur Müller, Matthia Sabatelli
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking. CoRR (2022)
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning. CoRR (2021)
Matthia Sabatelli, Mike Kestemont, Pierre Geurts
On the Transferability of Winning Tickets in Non-natural Image Datasets. VISIGRAPP (5: VISAPP) (2021)
Matthia Sabatelli, Pierre Geurts
On The Transferability of Deep-Q Networks. CoRR (2021)
Nikolay Banar, Matthia Sabatelli, Pierre Geurts, Walter Daelemans, Mike Kestemont
Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain. Computer Vision and Image Analysis of Art (2021)
Matthia Sabatelli, Nikolay Banar, Marie Cocriamont, Eva Coudyzer, Karine Lasaracina, Walter Daelemans, Pierre Geurts, Mike Kestemont
Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. Digit. Humanit. Q. 15 (1) (2021)
Matthia Sabatelli, Mike Kestemont, Pierre Geurts
On the Transferability of Winning Tickets in Non-Natural Image Datasets. CoRR (2020)
Travis Hammond, Dirk Jelle Schaap, Matthia Sabatelli, Marco A. Wiering
Forest Fire Control with Learning from Demonstration and Reinforcement Learning. IJCNN (2020)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. IJCNN (2020)
Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. CoRR (2020)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Deep Quality-Value (DQV) Learning. BNAIC/BENELEARN (2019)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. CoRR (2019)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Deep Quality-Value (DQV) Learning. CoRR (2018)
Francesco Bidoia, Matthia Sabatelli, Amirhossein Shantia, Marco A. Wiering, Lambert Schomaker
A Deep Convolutional Neural Network for Location Recognition and Geometry based Information. ICPRAM (2018)
Matthia Sabatelli, Francesco Bidoia, Valeriu Codreanu, Marco A. Wiering
Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead. ICPRAM (2018)
Matthia Sabatelli, Mike Kestemont, Walter Daelemans, Pierre Geurts
Deep Transfer Learning for Art Classification Problems. ECCV Workshops (2) (2018)
Matthia Sabatelli, Venet Osmani, Oscar Mayora, Agnes Grünerbl, Paul Lukowicz
Correlation of significant places with self-reported state of bipolar disorder patients. MobiHealth (2014)