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Michelangelo Conserva
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2020 – today
- 2023
- [c4]Remo Sasso, Michelangelo Conserva, Paulo E. Rauber:
Posterior Sampling for Deep Reinforcement Learning. ICML 2023: 30042-30061 - [i3]Remo Sasso, Michelangelo Conserva, Paulo E. Rauber:
Posterior Sampling for Deep Reinforcement Learning. CoRR abs/2305.00477 (2023) - 2022
- [j2]Aditya A. Ramesh, Paulo E. Rauber, Michelangelo Conserva, Jürgen Schmidhuber:
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits. Neural Comput. 34(11): 2232-2272 (2022) - [j1]Michelangelo Conserva, Marc Peter Deisenroth, K. S. Sesh Kumar:
The Graph Cut Kernel for Ranked Data. Trans. Mach. Learn. Res. 2022 (2022) - [c3]Marko Tot, Michelangelo Conserva, Diego Perez Liebana, Sam Devlin:
Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search. CoG 2022: 300-306 - [c2]Michelangelo Conserva, Paulo E. Rauber:
Hardness in Markov Decision Processes: Theory and Practice. NeurIPS 2022 - [i2]Michelangelo Conserva, Paulo E. Rauber:
Hardness in Markov Decision Processes: Theory and Practice. CoRR abs/2210.13075 (2022) - 2021
- [c1]Marko Tot, Michelangelo Conserva, Alan Pedrassoli Chitayat, Athanasios Vasileios Kokkinakis, Sagarika Patra, Simon Demediuk, Alvaro Caceres Munoz, Oluseyi Olarewaju, Marian Ursu, Ben Kirman, Jonathan Hook, Florian Block, Anders Drachen
, Diego Pérez-Liébana:
What Are You Looking At? Team Fight Prediction Through Player Camera. CoG 2021: 1-8 - [i1]Michelangelo Conserva, Marc Peter Deisenroth, K. S. Sesh Kumar:
Submodular Kernels for Efficient Rankings. CoRR abs/2105.12356 (2021)
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