Explanipedia

Catch Me If You Can: Combatting Fraud in Artificial Currency Based Government Benefits Programs Open

Devansh Jalota, Matthew Tsao, Marco Pavone · 2024

Artificial currencies have grown in popularity in many real-world resource allocation settings, gaining traction in government benefits programs like food assistance and transit benefits programs. However, such programs are susceptible to …

Differentially Private Stochastic Convex Optimization for Network Routing Applications Open

Matthew Tsao, Karthik Gopalakrishnan, Kaidi Yang, Marco Pavone · 2022

Network routing problems are common across many engineering applications. Computing optimal routing policies requires knowledge about network demand, i.e., the origin and destination (OD) of all requests in the network. However, privacy co…

Private Location Sharing for Decentralized Routing services Open

Matthew Tsao, Kaidi Yang, Karthik Gopalakrishnan, Marco Pavone · 2022

Data-driven methodologies offer many exciting upsides, but they also introduce new challenges, particularly in the realm of user privacy. Specifically, the way data is collected can pose privacy risks to end users. In many routing services…

Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment Open

Devansh Jalota, Kiril Solovey, Matthew Tsao, Stephen Zoepf, Marco Pavone · 2021

System optimum (SO) routing, wherein the total travel time of all users is minimized, is a holy grail for transportation authorities. However, SO routing may discriminate against users who incur much larger travel times than others to achi…

Online Hypergraph Matching with Delays Open

Marco Pavone, Amin Saberi, Maximilian Schiffer, Matthew Tsao · 2020

We study an online hypergraph matching problem with delays, motivated by ridesharing applications. In this model, users enter a marketplace sequentially, and are willing to wait up to $d$ timesteps to be matched, after which they will leav…

Planning and Operations of Mixed Fleets in Mobility-on-Demand Systems Open

Kaidi Yang, Matthew Tsao, Xin Xu, Marco Pavone · 2020

Automated vehicles (AVs) are expected to be beneficial for Mobility-on-Demand (MoD), thanks to their ability of being globally coordinated. To facilitate the steady transition towards full autonomy, we consider the transition period of AV …

Sample Complexity of Probabilistic Roadmaps via $\epsilon$-nets. Open

Matthew Tsao, Kiril Solovey, Marco Pavone · 2019

We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in\nthe finite time (non-asymptotic) regime. In particular, we investigate how\ncompleteness and optimality guarantees of the approach are influenced by the\nunderlyi…

Sample Complexity of Probabilistic Roadmaps via $ε$-nets Open

Matthew Tsao, Kiril Solovey, Marco Pavone · 2019

We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in the finite time (non-asymptotic) regime. In particular, we investigate how completeness and optimality guarantees of the approach are influenced by the underlying …

Robust and Adaptive Planning under Model Uncertainty Open

Apoorva Sharma, J. Michael Harrison, Matthew Tsao, Marco Pavone · 2019

Planning under model uncertainty is a fundamental problem across many applications of decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo Planning (RAMCP) algorithm, which allows computation of risk-sens…

Stochastic Model Predictive Control for Autonomous Mobility on Demand Open

Matthew Tsao, Ramón Iglesias, Marco Pavone · 2018

This paper presents a stochastic, model predictive control (MPC) algorithm that leverages short-term probabilistic forecasts for dispatching and rebalancing Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles)…

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