25 citations · 50 across the 16 of their papers we have counts for
16 papers
Conformal Prediction Intervals with Tail-Specific Guarantees
Simone Cuonzo, Nina Deliu
This paper extends classical conformal frameworks for constructing prediction intervals with global marginal coverage to intervals that provide explicitly calibrated guarante…
Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability
Simone Cuonzo, Nina Deliu
We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty. T…
A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial
Amy Armento Lee, Narayan Hegde, Nina Deliu +16
Consistent physical inactivity poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a prom…
Scalable Generalised Accuracy Estimation for Multisource Register-based Official Statistics
Nina Deliu, Piero Demetrio Falorsi, Stefano Falorsi +2
Official statistics are undergoing a significant transformation, as national statistical institutes transition from traditional single-source data production systems to integrated…
Implementing Response-Adaptive Randomisation in Stratified Rare-disease Trials: Design Challenges and Practical Solutions
Rajenki Das, Nina Deliu, Mark Toshner +1
Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implement…
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health
Nina Deliu, Bibhas Chakraborty
Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This visi…
A Multivariate Copula-based Bayesian Framework for Doping Detection
Nina Deliu, Brunero Liseo
Doping control is an essential component of anti-doping organizations for protecting clean sports competitions. Since 2009, this mission has been complemented worldwide by the Athl…
Alternative Approaches for Estimating Highest-Density Regions
Nina Deliu, Brunero Liseo
Among the variety of statistical intervals, highest-density regions (HDRs) stand out for their ability to effectively summarize a distribution or sample, unveiling its distinctive…
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study
Xueqing Liu, Nina Deliu, Tanujit Chakraborty +2
Mobile health (mHealth) interventions often aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions…
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Harsh Kumar, Tong Li, Jiakai Shi +11
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these intervention…
Incorporating Participants' Welfare into Sequential Multiple Assignment Randomized Trials
Xinru Wang, Nina Deliu, Yusuke Narita +1
Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients' time-varying clinical conditions. The sequential multiple assignment r…
Multi-disciplinary fairness considerations in machine learning for clinical trials
Isabel Chien, Nina Deliu, Richard E. Turner +3
While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A n…
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
In recent years, reinforcement learning (RL) has acquired a prominent position in health-related sequential decision-making problems, gaining traction as a valuable tool for delive…
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization
Tong Li, Jacob Nogas, Haochen Song +8
Traditional randomized A/B experiments assign arms with uniform random (UR) probability, such as 50/50 assignment to two versions of a website to discover whether one version engag…
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling
Nina Deliu, Joseph J. Williams, Sofia S. Villar
Using bandit algorithms to conduct adaptive randomised experiments can minimise regret, but it poses major challenges for statistical inference (e.g., biased estimators, inflated t…
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
Joseph Jay Williams, Jacob Nogas, Nina Deliu +4
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative in…