14 citations · 50 across the 10 of their papers we have counts for
5 papers · 1 filter
Sequential Monte Carlo Bandits
Iñigo Urteaga, Chris H. Wiggins
We extend Bayesian multi-armed bandit (MAB) algorithms beyond their original setting by making use of sequential Monte Carlo (SMC) methods. A MAB is a sequential decision making pr…
Bayesian Nonparametric Thompson Sampling for Multi-Armed Bandits
Iñigo Urteaga, Chris H. Wiggins
In stochastic multi-armed bandits, rewards are generated by an unknown distribution; the world's reward uncertainty induces the exploration-exploitation trade-off. Thompson samplin…
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes
Iñigo Urteaga, David J. Albers, Marija Vlajic Wheeler +3
In this paper, we introduce a novel task for machine learning in healthcare, namely personalized modeling of the female hormonal cycle. The motivation for this work is to model the…
Variational inference for the multi-armed contextual bandit
Iñigo Urteaga, Chris H. Wiggins
In many biomedical, science, and engineering problems, one must sequentially decide which action to take next so as to maximize rewards. One general class of algorithms for optimiz…
Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling
Iñigo Urteaga, Chris H. Wiggins
Reinforcement learning studies how to balance exploration and exploitation in real-world systems, optimizing interactions with the world while simultaneously learning how the world…