Phebe Vayanos

Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment

  • Bill Tang
  • Çağıl Koçyiğit
  • Eric Rice
  • Phebe Vayanos
    • Categories Applications - OR and Management Sciences Tags , , , , ,

    We study the problem of allocating scarce societal resources of different types (e.g., permanent housing, deceased donor kidneys for transplantation, ventilators) to heterogeneous allocatees on a waitlist (e.g., people experiencing homelessness, individuals suffering from end-stage renal disease, Covid-19 patients) based on their observed covariates. We leverage administrative data collected in deployment to design an online … Read more

    Learning Optimal Classification Trees Robust to Distribution Shifts

  • Nathan Justin
  • Sina Aghaei
  • Andrés Gómez
  • Phebe Vayanos
    • Categories (Mixed) Integer Linear Programming, Robust Optimization Tags , , , ,

    We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data. This problem arises frequently in high stakes settings such as public health and social work where data is often collected using self-reported surveys which are highly sensitive to e.g., the framing of the questions, the time … Read more

    ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

  • Patrick Vossler
  • Sina Aghaei
  • Nathan Justin
  • Nathanael Jo
  • Andrés Gómez
  • Phebe Vayanos
    • Categories (Mixed) Integer Linear Programming, Optimization Software and Modeling Systems Tags , , , , , ,

    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. The current version of the package provides implementations for learning optimal classification trees, optimal fair classification trees, … Read more

    Learning Optimal Prescriptive Trees from Observational Data

  • Nathanael Jo
  • Sina Aghaei
  • Andrés Gómez
  • Phebe Vayanos
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences Tags , , , ,

    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. This problem arises in numerous socially important domains such as public health and personalized medicine, where interpretable and data-driven interventions are sought based on data gathered … Read more

    Strong Optimal Classification Trees

  • Sina Aghaei
  • Andrés Gómez
  • Phebe Vayanos
    • Categories (Mixed) Integer Linear Programming, Combinatorial Optimization, Statistics Tags , , ,

    Decision trees are among the most popular machine learning models and are used routinely in applications ranging from revenue management and medicine to bioinformatics. In this paper, we consider the problem of learning optimal binary classification trees with univariate splits. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality … Read more

    Cost-Sharing Mechanism Design for Ride-Sharing

  • Maged M. Dessouky
  • Nelson A. Uhan
  • Shichun Hu
  • Phebe Vayanos
    • Categories Game Theory, Transportation

    In this paper, we focus on the cost-sharing problem for ride-sharing that determines how to allocate the total ride cost between the driver and the passengers. We identify the properties that a desirable cost-sharing mechanism should have and develop a general framework which can be used to create specific cost-sharing mechanisms. We propose specific mechanisms … Read more

    ROC++: Robust Optimization in C++

  • Phebe Vayanos
  • Qing Jin
  • George Elissaios
    • Categories Optimization Software and Modeling Systems, Robust Optimization Tags , , , , , ,

    Over the last two decades, robust optimization has emerged as a popular means to address decision-making problems affected by uncertainty. This includes single- and multi-stage problems involving real-valued and/or binary decisions, and affected by exogenous (decision-independent) and/or endogenous (decision-dependent) uncertain parameters. Robust optimization techniques rely on duality theory potentially augmented with approximations to transform a … Read more

    Robust Active Preference Elicitation

  • Phebe Vayanos
  • Yingxiao Ye
  • Duncan McElfresh
  • John P. Dickerson
  • Eric Rice
    • Categories Applications - OR and Management Sciences, Robust Optimization

    We study the problem of strategically eliciting the preferences of a decision-maker through a moderate number of pairwise comparison queries with the goal of making them a high quality recommendation for a specific decision-making problem. We are particularly motivated by applications in high stakes domains, such as when choosing a policy for allocating scarce resources … Read more

    Learning Optimal Classification Trees: Strong Max-Flow Formulations

  • Sina Aghaei
  • Andrés Gómez
  • Phebe Vayanos
    • Categories (Mixed) Integer Linear Programming, Combinatorial Optimization, Statistics Tags , , ,

    We consider the problem of learning optimal binary classification trees. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality of heuristic approaches and the tremendous improvements in mixed-integer programming (MIP) technology. Yet, existing approaches from the literature do not leverage the power of MIP to its full extent. Indeed, … Read more

    Robust Optimization with Decision-Dependent Information Discovery

  • Phebe Vayanos
  • Angelos Georghiou
  • Han Yu
    • Categories Robust Optimization Tags , , ,

    Robust optimization (RO) is a popular paradigm for modeling and solving two- and multi-stage decision-making problems affected by uncertainty. In many real-world applications, such as R&D project selection, production planning, or preference elicitation for product or policy recommendations, the time of information discovery is decision-dependent and the uncertain parameters only become observable after an often costly … Read more