Daniel de Marchi — accepted AI-conference papers

2023

Revisiting Bellman Errors for Offline Model Selection

Joshua P Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael Rene Kosorok

ICML 2023poster

Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings. One idea that has been extensively explored is to select policies based on the mean squared Bellman error (MSBE) of the asso…

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2020

Augmenting Molecular Images with Vector Representations as a Featurization Technique for Drug Classification

Daniel de Marchi, Amarjit Budhiraja

ICASSP 2020accepted

One of the key steps in building deep learning systems for drug classification and generation is the choice of featurization for the molecules. Previous featurization methods have included molecular images, binary strings, graphs, and SMILES strings. This paper proposes the creation of molecular ima…

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