We show how decision-focused learning can improve the fairness-accuracy tradeoff in algorithmic decision-making using a case study on a real-world domain.
We propose an efficient way to implement decision-focused learning for the kinds of RMABs used for intervention planning in public health.
A blog post about the predict-then-optimize paradigm for algorithmic decision-making, and how we can view it as inducing a task-specific loss function in supervised machine learning.
We propose two new innovations to help improve the learning of task-specific loss functions.
We propose a way to differentiate through MDP Planning for Restless Multi-Armed Bandits. We use this approach to better learn the Transition Matrices from "features" associated with different arms using Decision-Focused Learning.
We learn task-specific loss functions that, when trained on, allow a predictive model to make better predictions for the given task.
We propose a way to optimally differentiate through Reinforcement Learning. Specifically, we propose two optimality conditions that hold at convergence and show how to (approximately) calculate gradients using them.