2.5k citations · 4.8k across the 39 of their papers we have counts for
41 papers
A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data
Kathy Li, Iñigo Urteaga, Amanda Shea +3
Mobile health (mHealth) apps such as menstrual trackers provide a rich source of self-tracked health observations that can be leveraged for health-relevant research. However, such…
Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile health data
Kathy Li, Iñigo Urteaga, Chris H. Wiggins +4
The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual…
Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach
Wesley Tansey, Kathy Li, Haoran Zhang +4
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology. As genomic tumor profiling is becom…
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…
Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit
Iñigo Urteaga, Chris H. Wiggins
We here adopt Bayesian nonparametric mixture models to extend multi-armed bandits in general, and Thompson sampling in particular, to scenarios where there is reward model uncertai…
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…