Search Results for author:
Found 15 papers, 4 papers with code
Learning Fair Policies for Multi-stage Selection Problems from Observational Data
We consider the problem of learning fair policies for multi-stage selection problems from observational data.
Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment
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.
Learning Optimal Classification Trees Robust to Distribution Shifts
We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data.
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
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.
Deploying a Robust Active Preference Elicitation Algorithm on MTurk: Experiment Design, Interface, and Evaluation for COVID-19 Patient Prioritization
Preference elicitation leverages AI or optimization to learn stakeholder preferences in settings ranging from marketing to public policy.
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing.
Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery
The resources are assigned in a first come first served (FCFS) fashion according to an eligibility structure that encodes the resource types that serve each queue.
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
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.
Optimal Robust Classification Trees
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).
Learning Optimal Prescriptive Trees from Observational Data
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.
Strong Optimal Classification Trees
To fill this gap in the literature, we propose an intuitive flow-based MIO formulation for learning optimal binary classification trees.
Fair Influence Maximization: A Welfare Optimization Approach
Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter.
Robust Active Preference Elicitation
For the offline case, where active preference elicitation takes the form of a two and half stage robust optimization problem with decision-dependent information discovery, we provide an equivalent reformulation in the form of a mixed-binary linear program which we solve via column-and-constraint generation.
Learning Optimal Classification Trees: Strong Max-Flow Formulations
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.
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
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).
