14 citations · 50 across the 10 of their papers we have counts for
10 papers
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic masking
Iñigo Urteaga, Moulay-Zaïdane Draïdia, Tomer Lancewicki +1
We design and evaluate a Bayesian optimization framework for resource efficient pre-training of Transformer-based language models (TLMs). TLM pre-training requires high computation…
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…
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics
Iñigo Urteaga, Tristan Bertin, Theresa M. Hardy +2
We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns. Reconstructing and forecasting…
Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data
Iñigo Urteaga, Mollie McKillop, Sharon Lipsky-Gorman +1
We investigate the use of self-tracking data and unsupervised mixed-membership models to phenotype endometriosis. Endometriosis is a systemic, chronic condition of women in reprodu…
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…