no code implementations • 26 Oct 2023 • Nathan Justin, Sina Aghaei, Andrés Gómez, Phebe Vayanos
We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data.
1 code implementation • 28 Jul 2023 • Patrick Vossler, Sina Aghaei, Nathan Justin, Nathanael Jo, Andrés Gómez, Phebe Vayanos
ODTLearn is an open-source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2019) and several of its extensions.
no code implementations • 4 Dec 2022 • Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos
We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing.
1 code implementation • 24 Jan 2022 • Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos
The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algorithms.
no code implementations • AAAI Workshop AdvML 2022 • Nathan Justin, Sina Aghaei, Andres Gomez, Phebe Vayanos
In many high-stakes domains, the data used to drive machine learning algorithms is noisy (due to e. g., the sensitive nature of the data being collected, limited resources available to validate the data, etc).
1 code implementation • 31 Aug 2021 • Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos
We consider the problem of learning an optimal prescriptive tree (i. e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observational data.
3 code implementations • 29 Mar 2021 • Sina Aghaei, Andrés Gómez, Phebe Vayanos
To fill this gap in the literature, we propose an intuitive flow-based MIO formulation for learning optimal binary classification trees.
no code implementations • 21 Feb 2020 • Sina Aghaei, Andres Gomez, Phebe Vayanos
To fill this gap in the literature, we propose a flow-based MIP formulation for optimal binary classification trees that has a stronger linear programming relaxation.
no code implementations • 25 Mar 2019 • Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos
In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e. g., to make product recommendations, or to guide the production of entertainment).