Aldo Gael Carranza — accepted AI-conference papers

2025

Robust Offline Policy Learning with Observational Data from Multiple Sources

AISTATS 2025poster

We consider the problem of using observational bandit feedback data from multiple heterogeneous data sources to learn a personalized decision policy that robustly generalizes across diverse target settings. To achieve this, we propose a minimax regret optimization objective to ensure uniformly low r…

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2023

Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles

Aldo Gael Carranza, Sanath Kumar Krishnamurthy, Susan Athey

AISTATS 2023poster

Contextual bandit algorithms often estimate reward models to inform decision-making. However, true rewards can contain action-independent redundancies that are not relevant for decision-making. We show it is more data-efficient to estimate any function that explains the reward differences between ac…

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