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ORCIDMatthia Sabatelli
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2020 – today
- 2025
[j3]Aleksandar Todorov, Juan Cardenas-Cartagena, Rafael Fernandes Cunha, Marco Zullich, Matthia Sabatelli:
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning. Trans. Mach. Learn. Res. 2025 (2025)- [c21]Tsegaye Misikir Tashu, Eduard-Raul Kontos, Matthia Sabatelli, Matias Valdenegro-Toro:
Cross-Lingual Document Recommendations with Transformer-Based Representations: Evaluating Multilingual Models and Mapping Techniques. COLING Workshops 2025: 39-47 - [c20]Josh Bruegger
, Diana Ioana Catana, Vanja Macovaz, Matias Valdenegro-Toro, Matthia Sabatelli, Marco Zullich:
Large-Image Object Detection for Fine-Grained Recognition of Punches Patterns in Medieval Panel Painting. EvoMUSART 2025: 34-50 - [c19]Juan Cardenas-Cartagena, Massimiliano Falzari, Marco Zullich, Matthia Sabatelli:
Upside-Down Reinforcement Learning for More Interpretable Optimal Control. ICAART (1) 2025: 859-869 - [c18]Rares Adrian Oancea, Stan van der Linde, Willem de Kok, Matthia Sabatelli, Sebastian Feld:
Optimizing Initial Qubit Mappings Under Fixed Gate Error Rates Using Deep Reinforcement Learning. I4CS 2025: 189-208 - [c17]Matthijs van der Lende, Matthia Sabatelli, Juan Cardenas-Cartagena:
Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning. NLDL 2025: 141-154 - [c16]Xenia Demetriou, Sophie Sananikone, Vojislav Tobias Westmoreland, Matthia Sabatelli, Marco Zullich:
A Study on the Faithfulness of Feature Attribution Explanations in Pruned Vision-Based Multi-Task Learning. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2025: 121-128 - [i25]Josh Bruegger, Diana Ioana Catana, Vanja Macovaz, Matias Valdenegro-Toro, Matthia Sabatelli, Marco Zullich:
Large-image Object Detection for Fine-grained Recognition of Punches Patterns in Medieval Panel Painting. CoRR abs/2501.12489 (2025) - [i24]Massimiliano Falzari, Matthia Sabatelli:
Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning. CoRR abs/2502.00802 (2025) - [i23]Aleksandar Todorov, Juan Cardenas-Cartagena, Rafael Fernandes Cunha, Marco Zullich, Matthia Sabatelli:
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning. CoRR abs/2508.06871 (2025) - [i22]Kyriakos Hjikakou, Juan Diego Cardenas Cartagena, Matthia Sabatelli:
On the Generalisation of Koopman Representations for Chaotic System Control. CoRR abs/2508.18954 (2025) - [i21]Viktor Veselý, Aleksandar Todorov, Matthia Sabatelli:
On The Presence of Double-Descent in Deep Reinforcement Learning. CoRR abs/2511.06895 (2025) - [i20]Erwan Escudie, Matthia Sabatelli, Olivier Buffet, Jilles Steeve Dibangoye:
ε-Optimally Solving Two-Player Zero-Sum POSGs. CoRR abs/2511.11282 (2025) - 2024
- [c15]Arthur Müller, Felix Grumbach, Matthia Sabatelli:
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling. ETFA 2024: 1-8 - [c14]Alexander Hill, Marc Groefsema, Matthia Sabatelli, Raffaella Carloni, Marco Grzegorczyk:
Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning. ICAART (3) 2024: 178-185 - [i19]Tsegaye Misikir Tashu, Eduard-Raul Kontos, Matthia Sabatelli, Matias Valdenegro-Toro:
Mapping Transformer Leveraged Embeddings for Cross-Lingual Document Representation. CoRR abs/2401.06583 (2024) - [i18]Marius Captari, Remo Sasso, Matthia Sabatelli:
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts. CoRR abs/2403.17542 (2024) - [i17]Erwan Escudie, Matthia Sabatelli, Jilles Dibangoye:
ε-Optimally Solving Zero-Sum POSGs. CoRR abs/2406.00054 (2024) - [i16]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 abs/2406.02294 (2024) - [i15]Franciszek Szewczyk, Gilles Louppe, Matthia Sabatelli:
Video-Driven Graph Network-Based Simulators. CoRR abs/2409.15344 (2024) - [i14]Juan Cardenas-Cartagena, Massimiliano Falzari, Marco Zullich, Matthia Sabatelli:
Upside-Down Reinforcement Learning for More Interpretable Optimal Control. CoRR abs/2411.11457 (2024) - 2023
- [j2]Remo Sasso, Matthia Sabatelli, Marco A. Wiering:
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c13]Arthur Müller, Matthia Sabatelli:
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning. ITSC 2023: 5271-5278 - [i13]Bart van Marum, Matthia Sabatelli, Hamidreza Kasaei:
Learning Perceptive Bipedal Locomotion over Irregular Terrain. CoRR abs/2304.07236 (2023) - [i12]Arthur Müller, Matthia Sabatelli:
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning. CoRR abs/2307.11357 (2023) - 2022
- [b1]Matthia Sabatelli:
Contributions to Deep Transfer Learning: from Supervised to Reinforcement Learning. University of Liège, Belgium, 2022 - [c12]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: 234-250 - [c11]Arthur Müller, Matthia Sabatelli:
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking. ITSC 2022: 951-958 - [c10]Matias Valdenegro-Toro, Matthia Sabatelli:
Machine Learning Students Overfit to Overfitting. Teaching ML 2022: 46-51 - [i11]Remo Sasso, Matthia Sabatelli, Marco A. Wiering:
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. CoRR abs/2205.14410 (2022) - [i10]Arthur Müller, Matthia Sabatelli:
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking. CoRR abs/2206.10122 (2022) - [i9]Vincent Tonkes, Matthia Sabatelli:
How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification. CoRR abs/2208.04693 (2022) - [i8]Matias Valdenegro-Toro, Matthia Sabatelli:
Machine Learning Students Overfit to Overfitting. CoRR abs/2209.03032 (2022) - [i7]Julius Wagenbach, Matthia Sabatelli:
Factors of Influence of the Overestimation Bias of Q-Learning. CoRR abs/2210.05262 (2022) - 2021
- [j1]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) - [c9]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: 1-9 - [c8]Matthia Sabatelli, Mike Kestemont, Pierre Geurts:
On the Transferability of Winning Tickets in Non-natural Image Datasets. VISIGRAPP (5: VISAPP) 2021: 59-69 - [i6]Remo Sasso, Matthia Sabatelli, Marco A. Wiering:
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning. CoRR abs/2108.06526 (2021) - [i5]Matthia Sabatelli, Pierre Geurts:
On The Transferability of Deep-Q Networks. CoRR abs/2110.02639 (2021) - 2020
- [c7]Travis Hammond, Dirk Jelle Schaap, Matthia Sabatelli, Marco A. Wiering:
Forest Fire Control with Learning from Demonstration and Reinforcement Learning. IJCNN 2020: 1-8 - [c6]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. IJCNN 2020: 1-8 - [i4]Matthia Sabatelli, Mike Kestemont, Pierre Geurts:
On the Transferability of Winning Tickets in Non-Natural Image Datasets. CoRR abs/2005.05232 (2020) - [i3]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 abs/2012.12062 (2020)
2010 – 2019
- 2019
- [c5]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Deep Quality-Value (DQV) Learning. BNAIC/BENELEARN 2019 - [i2]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 abs/1909.01779 (2019) - 2018
- [c4]Matthia Sabatelli, Mike Kestemont, Walter Daelemans, Pierre Geurts:
Deep Transfer Learning for Art Classification Problems. ECCV Workshops (2) 2018: 631-646 - [c3]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: 27-36 - [c2]Matthia Sabatelli, Francesco Bidoia, Valeriu Codreanu, Marco A. Wiering:
Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead. ICPRAM 2018: 276-283 - [i1]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Deep Quality-Value (DQV) Learning. CoRR abs/1810.00368 (2018) - 2014
- [c1]Matthia Sabatelli, Venet Osmani, Oscar Mayora, Agnes Grünerbl, Paul Lukowicz:
Correlation of significant places with self-reported state of bipolar disorder patients. MobiHealth 2014: 116-119
Coauthor Index
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last updated on 2026-01-11 00:58 CET by the dblp team
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