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Lavanya Marla

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AAAI Conference 2021 Conference Paper

PenDer: Incorporating Shape Constraints via Penalized Derivatives

Akhil Gupta
Lavanya Marla
Ruoyu Sun
Naman Shukla
Arinbjörn Kolbeinsson

When deploying machine learning models in the real-world, system designers may wish that models exhibit certain shape behavior, i. e. , model outputs follow a particular shape with respect to input features. Trends such as monotonicity, convexity, diminishing or accelerating returns are some of the desired shapes. Presence of these shapes makes the model more interpretable for the system designers, and adequately fair for the customers. We notice that many such common shapes are related to derivatives, and propose a new approach, Pen- Der (Penalizing Derivatives), which incorporates these shape constraints by penalizing the derivatives. We further present an Augmented Lagrangian Method (ALM) to solve this constrained optimization problem. Experiments on three realworld datasets illustrate that even though both PenDer and state-of-the-art Lattice models achieve similar conformance to shape, PenDer captures better sensitivity of prediction with respect to intended features. We also demonstrate that PenDer achieves better test performance than Lattice while enforcing more desirable shape behavior.

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AAAI Conference 2012 Conference Paper

An Efficient Simulation-Based Approach to Ambulance Fleet Allocation and Dynamic Redeployment

Yisong Yue
Lavanya Marla
Ramayya Krishnan

We present an efficient approach to ambulance fleet allocation and dynamic redeployment, where the goal is to position an entire fleet of ambulances to base locations to maximize the service level (or utility) of the Emergency Medical Services (EMS) system. We take a simulation-based approach, where the utility of an allocation is measured by directly simulating emergency requests. In both the static and dynamic settings, this modeling approach leads to an exponentially large action space (with respect to the number of ambulances). Futhermore, the utility of any particular allocation can only be measured via a seemingly “black box” simulator. Despite this complexity, we show that embedding our simulator within a simple and efficient greedy allocation algorithm produces good solutions. We derive data-driven performance guarantees which yield small optimality gap. Given its efficiency, we can repeatedly employ this approach in real-time for dynamic repositioning. We conduct simulation experiments based on real usage data of an EMS system from a large Asian city, and demonstrate significant improvement in the system’s service levels using static allocations and redeployment policies discovered by our approach.

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