REDFLAG: A Run-timE, Distributed, Flexible, Lightweight, And Generic fault detection service for data-driven wireless sensor applications. - Iñigo Urteaga; Kevin Barnhart; Qi Han 001 (Pervasive Mob. Comput., 2009)

On the Design of a Scalable Multimedia Streaming System based on Receiver-driven Flow and Congestion Awareness. - Iñigo Urteaga; Iraide Unanue; Javier Del Ser; Pedro J. Sánchez; Aitor Rodriguez (SIGMAP, 2010)

AWARE: Activity AWARE network clustering for wireless sensor networks. - Iñigo Urteaga; Na Yu; Nicholas Hubbell; Qi Han 001 (LCN, 2011)

Replication and optimization of hedge fund risk factor exposures. - Douglas E. Johnston; Iñigo Urteaga; Petar M. Djuric (ICASSP, 2013)

Estimation of ARMA state processes by particle filtering. - Iñigo Urteaga; Petar M. Djuric (ICASSP, 2014)

Acquisition of human traces with Bluetooth technology: Challenges and proposals. - José María Cabero; Virginia Molina; Iñigo Urteaga; Fidel Liberal; José Luis Martín 001 (Ad Hoc Networks, 2014)

AWARE: Activity aware maintenance of communication structures for wireless sensor networks. - Iñigo Urteaga; Na Yu; Nicholas Hubbell; Qi Han 001 (Pervasive Mob. Comput., 2014)

Filtering of nonlinear time-series coupled by fractional Gaussian processes. - Iñigo Urteaga; Mónica F. Bugallo; Petar M. Djuric (CAMSAP, 2015)

Sequential Monte Carlo sampling for systems with fractional Gaussian processes. - Iñigo Urteaga; Mónica F. Bugallo; Petar M. Djuric (EUSIPCO, 2015)

Particle filtering of ARMA processes of unknown order and parameters. - Iñigo Urteaga; Petar M. Djuric (ICASSP, 2015)

DTN Routing Optimised by Human Routines: The HURRy Protocol. - Susana Pérez; José María Cabero; Iñigo Urteaga (WWIC, 2015)

Reliability of Bluetooth-based connectivity traces for the characterization of human interaction. - José María Cabero; Iñigo Urteaga; Virginia Molina; Fidel Liberal; José Luis Martín 001 (Ad Hoc Networks, 2015)

Sequential Monte Carlo sampling for correlated latent long-memory time-series. - Iñigo Urteaga; Mónica F. Bugallo; Petar M. Djuric (ICASSP, 2016)

Sequential Monte Carlo methods under model uncertainty. - Iñigo Urteaga; Mónica F. Bugallo; Petar M. Djuric (SSP, 2016)

Multiple particle filtering for inference in the presence of state correlation of unknown mixing parameters. - Iñigo Urteaga; Mónica F. Bugallo; Petar M. Djuric (ICASSP, 2017)

Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling. - Iñigo Urteaga; Chris H. Wiggins 001 (CoRR, 2017)

Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes. - Iñigo Urteaga; David J. Albers; Marija Vlajic Wheeler; Anna Druet; Hans Raffauf; Noémie Elhadad (CoRR, 2017)

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time. - Iñigo Urteaga; Mónica F. Bugallo; Petar M. Djuric (EURASIP J. Adv. Signal Process., 2017)

Sequential Estimation of Hidden ARMA Processes by Particle Filtering - Part I. - Iñigo Urteaga; Petar M. Djuric (IEEE Trans. Signal Process., 2017)

Sequential Estimation of Hidden ARMA Processes by Particle Filtering - Part II. - Iñigo Urteaga; Petar M. Djuric (IEEE Trans. Signal Process., 2017)

Variational inference for the multi-armed contextual bandit. - Iñigo Urteaga; Chris Wiggins 001 (AISTATS, 2018)

Nonparametric Gaussian mixture models for the multi-armed contextual bandit. - Iñigo Urteaga; Chris H. Wiggins 001 (CoRR, 2018)

(Sequential) Importance Sampling Bandits. - Iñigo Urteaga; Chris H. Wiggins 001 (CoRR, 2018)

Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data. - Iñigo Urteaga; Mollie McKillop; Sharon Lipsky Gorman; Noémie Elhadad (CoRR, 2018)

Learning Across a Healthcare Data Network to Improve Model Robustness and Evidence Reliability. - Noémie Elhadad; Iñigo Urteaga; Alison Callahan; Jenna Reps; Patrick B. Ryan (AMIA, 2019)

Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics. - Iñigo Urteaga; Tristan Bertin; Theresa M. Hardy; David J. Albers; Noémie Elhadad (MLHC, 2019)

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. - Kathy Li; Iñigo Urteaga; Chris H. Wiggins 001; Anna Druet; Amanda Shea; Virginia J. Vitzthum; Noémie Elhadad (npj Digit. Medicine, 2020)

Learning endometriosis phenotypes from patient-generated data. - Iñigo Urteaga; Mollie McKillop; Noémie Elhadad (npj Digit. Medicine, 2020)

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction. - Iñigo Urteaga; Kathy Li; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noemie Elhadad (MLHC, 2021)

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; Virginia J. Vitzthum; Chris H. Wiggins 001; Noémie Elhadad (CoRR, 2021)

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking. - Kathy Li; Iñigo Urteaga; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noémie Elhadad (J. Am. Medical Informatics Assoc., 2021)

Human, Data, and Algorithmic Perspectives to Inform the Design of RL-enabled Self-management Regimens. - Adrienne Pichon; Iñigo Urteaga; Lena Mamykina; Noemie Elhadad (AMIA, 2022)

Multi-armed bandits for online optimization of language model pre-training: the use case of dynamic masking. - Iñigo Urteaga; Moulay-Zaïdane Draïdia; Tomer Lancewicki; Shahram Khadivi (CoRR, 2022)

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; Shahram Khadivi (ACL (Findings), 2023)

A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference. - Mert Ketenci; Adler J. Perotte; Noémie Elhadad; Iñigo Urteaga (CoRR, 2023)

Minimax Risk Classifiers for Mislabeled Data: a Study on Patient Outcome Prediction Tasks. - Lucia Filippozzi; Santiago Mazuelas; Iñigo Urteaga (MLHC, 2024)

Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification. - Mert Ketenci; Iñigo Urteaga; Victor Alfonso Rodriguez; Noémie Elhadad; Adler J. Perotte (CoRR, 2024)

Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference. - Mert Ketenci; Adler J. Perotte; Noémie Elhadad; Iñigo Urteaga (UAI, 2025)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - Shalmali Joshi; Iñigo Urteaga; Wouter A. C. van Amsterdam; George Hripcsak; Pierre A. Elias; Benjamin R. C. Amor; Noémie Elhadad; James C. Fackler; Mark P. Sendak; Jenna Wiens; Kaivalya Deshpande; Yoav Wald; et al. (J. Am. Medical Informatics Assoc., 2025)

Canonical Bayesian Linear System Identification. - Andrey Bryutkin; Matthew E. Levine; Iñigo Urteaga; Youssef Marzouk 001 (CoRR, 2025)

Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives. - Adrienne Pichon; Iñigo Urteaga; Lena Mamykina; Noémie Elhadad (ACM Trans. Comput. Hum. Interact., 2025)