Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Silander, Tomi"'
Publikováno v:
PMLR 84:948-957, 2018
We introduce an information theoretic criterion for Bayesian network structure learning which we call quotient normalized maximum likelihood (qNML). In contrast to the closely related factorized normalized maximum likelihood criterion, qNML satisfies
Externí odkaz:
http://arxiv.org/abs/2408.14935
Autor:
Lehoux-Lebacque, Vassilissa, Silander, Tomi, Loiodice, Christelle, Lee, Seungjoon, Wang, Albert, Michel, Sofia
Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the envi
Externí odkaz:
http://arxiv.org/abs/2408.14527
Autor:
Aractingi, Michel, Léziart, Pierre-Alexandre, Flayols, Thomas, Perez, Julien, Silander, Tomi, Souères, Philippe
Publikováno v:
Scientific Reports, 2023, 13 (11945), pp.12
Quadruped robots require robust and general locomotion skills to exploit their mobility potential in complex and challenging environments. In this work, we present the first implementation of a robust end-to-end learning-based controller on the Solo1
Externí odkaz:
http://arxiv.org/abs/2309.16683
Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of com
Externí odkaz:
http://arxiv.org/abs/2011.10274
Autor:
Perez, Julien, Silander, Tomi
Partially observable environments present an important open challenge in the domain of sequential control learning with delayed rewards. Despite numerous attempts during the two last decades, the majority of reinforcement learning algorithms and asso
Externí odkaz:
http://arxiv.org/abs/1705.10993
Autor:
Dance, Christopher R., Silander, Tomi
The trade-off between the cost of acquiring and processing data, and uncertainty due to a lack of data is fundamental in machine learning. A basic instance of this trade-off is the problem of deciding when to make noisy and costly observations of a d
Externí odkaz:
http://arxiv.org/abs/1703.10010
Autor:
Dance, Christopher R., Silander, Tomi
We study the restless bandit associated with an extremely simple scalar Kalman filter model in discrete time. Under certain assumptions, we prove that the problem is indexable in the sense that the Whittle index is a non-decreasing function of the re
Externí odkaz:
http://arxiv.org/abs/1509.04541
Publikováno v:
In Journal of Behavioral and Experimental Economics April 2020 85
We analyze differences between two information-theoretically motivated approaches to statistical inference and model selection: the Minimum Description Length (MDL) principle, and the Minimum Message Length (MML) principle. Based on this analysis, we
Externí odkaz:
http://arxiv.org/abs/1301.7378
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to
Externí odkaz:
http://arxiv.org/abs/1301.6710