Pierre Dangauthier

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We extend the Bayesian skill rating system TrueSkill to infer entire time series of skills of players by smoothing through time instead of filtering. The skill of each participating player, say, every year is represented by a latent skill variable which is affected by the relevant game outcomes that year, and coupled with the skill variables of the previous(More)
Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and completely unsupervised learning, consist in relying on previous knowledge to acquire new skills. We propose here to realize auto-supervised learning by exploiting statistical(More)
This document presents some views on the semantic of the probability notion. It's mainly a summary of di erent readings, in particular of a Stanford Encyclopaedia of Philosophy article. Probabilities are daily used in real world application, often without a clear understanding of their mathematical foundation and of the meaning we attach to them. This lack(More)
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