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Talk by Iñigo Urteaga, Columbia University
February 15 @ 12:00 am - 1:00 pm
Place: AGORA – Espai Polivalent, mòdul B3, Campus Nord UPC
Bayesian models and inference for reinforcement learning: the multi-armed bandit case.
Speaker: Iñigo Urteaga, Columbia University
The most celebrated corners of machine learning over the past decades are those successful at predicting — e.g., spam classification, medical diagnoses, or cat faces. However, a wide variety of applied problems are prescriptive rather than predictive: those for which decisions must be made in order to maximize a reward. Such problems are common in health, commerce, and engineering. One particular setting for optimizing interactions with the unknown world is the multi-armed bandit, which describes sequential decision processes, a particular instance of reinforcement learning.
In this talk, I will show how Bayesian models and inference methods from the statistics and machine learning community — particularly variational and Monte Carlo methods — can be used to extend multi-armed bandit models, improve learning on complex scenarios, and make informed decisions.
Ricard Gavaldà, gavalda-at-cs.upc.edu
Talk partially sponsored by TIN2017-89244-R (MACDA project).