David Hallac — accepted AI-conference papers

2018

Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems

Ramon Iglesias, Federico Rossi, Kevin Wang, David Hallac, Jure Leskovec, Marco Pavone

ICRA 2018poster

The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles). We first model the AMoD system using a time-expanded network, and present a formulation that computes the optimal rebalancing strat…

Cited by 196 Source ↗Scholar ↗

2017

Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields

Youngsuk Park, David Hallac, Stephen Boyd, Jure Leskovec

AISTATS 2017poster

Markov random fields (MRFs) are a useful tool for modeling relationships present in large and high-dimensional data. Often, this data comes from various sources and can have diverse distributions, for example a combination of numerical, binary, and categorical variables. Here, we define the pairwise…

Cited by 26 Source ↗PDF ↗Scholar ↗