Search Results for author:
Found 6 papers, 1 papers with code
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT).
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
Our approach is interpretable, allowing a practitioner to visualize which subgroups in the population lead to falsification of an observational study.
Federated Multi-Armed Bandits Under Byzantine Attacks
We demonstrate Fed-MoM-UCB's effectiveness against the baselines in the presence of Byzantine attacks via experiments.
Safe Linear Leveling Bandits
In this work, we tackle a different critical task through the lens of \textit{linear stochastic bandits}, where the aim is to keep the actions' outcomes close to a target level while respecting a \textit{two-sided} safety constraint, which we call \textit{leveling}.
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
We propose ESCADA, a novel and generic multi-armed bandit (MAB) algorithm tailored for the leveling task, to make safe, personalized, and context-aware dose recommendations.
