Search Results for author: Nina Deliu

Found 5 papers, 0 papers with code

Multi-disciplinary fairness considerations in machine learning for clinical trials

no code implementations18 May 2022 Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus

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.

BIG-bench Machine Learning Fairness

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions

no code implementations4 Mar 2022 Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty

Reinforcement learning (RL) is acquiring a key role in the space of adaptive interventions (AIs), attracting a substantial interest within methodological and theoretical literature and becoming increasingly popular within health sciences.

Causal Inference reinforcement-learning

Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization

no code implementations15 Dec 2021 Tong Li, Jacob Nogas, Haochen Song, Harsh Kumar, Audrey Durand, Anna Rafferty, Nina Deliu, Sofia S. Villar, Joseph J. Williams

TS-PostDiff takes a Bayesian approach to mixing TS and Uniform Random (UR): the probability a participant is assigned using UR allocation is the posterior probability that the difference between two arms is 'small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained.

Thompson Sampling

Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling

no code implementations30 Oct 2021 Nina Deliu, Joseph J. Williams, Sofia S. Villar

Increasing power in such small pilot experiments, without limiting the adaptive nature of the algorithm, can allow promising interventions to reach a larger experimental phase.

Thompson Sampling

Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments

no code implementations22 Mar 2021 Joseph Jay Williams, Jacob Nogas, Nina Deliu, Hammad Shaikh, Sofia S. Villar, Audrey Durand, Anna Rafferty

We therefore use our case study of the ubiquitous two-arm binary reward setting to empirically investigate the impact of using Thompson Sampling instead of uniform random assignment.

Thompson Sampling

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