1 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
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…