arXiv research

Study predicts individual treatment effects in ride-sharing competitions.

Paper tackles ride-sharing user experience enhancement with weakly supervised learning.

Ride sharing has important implications in terms of environmental, social and individual goals by reducing carbon footprints, fostering social interactions and economizing commuter costs. The ride sharing systems that are commonly available lack adaptive and scalable techniques that can simultaneously learn from the la…

Deep RL tackles fleet management and dispatching for ride-sharing platforms.

We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology, behavioral economics, an…

This chapter reviews statistical tools for reinforcement learning.

DN estimator mitigates network interference in experiments.

Predicts destinations and routes from partial trajectory data.

A new framework uses multi-agent reinforcement learning for evaluating policies in two-sided markets.

The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal sol…

This paper explores the possibility of near-optimally solving multi-agent, multi-task NP-hard planning problems with time-dependent rewards using a learning-based algorithm. In particular, we consider a class of robot/machine scheduling problems called the multi-robot reward collection problem (MRRC). Such MRRC problem…

Efficiently projects points onto polytopes, especially useful in web-scale applications.

A new method improves ridesharing efficiency using QMIX.

We study Exo-MDPs to reduce sample complexity in reinforcement learning.

Proposes a graph neural network for efficient multi-agent routing.

Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth dat…

ADRL improves participant selection in MCS systems.

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on pred…

GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.

New framework validates counterfactual estimations in network interference settings.

Synthetic control method improves policy evaluation in high-dimensional settings.