78 citations · 333 across the 57 of their papers we have counts for
7 papers · 1 filter
Data-Driven Spatio-Temporal Analysis of Curbside Parking Demand: A Case-Study in Seattle
Tanner Fiez, Lillian Ratliff
Due to rapid expansion of urban areas in recent years, management of curbside parking has become increasingly important. To mitigate congestion, while meeting a city's diverse need…
Uncertainty in Multi-Commodity Routing Networks: When does it help?
Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff +1
We study the equilibrium behavior in a multi-commodity selfish routing game with many types of uncertain users where each user over- or under-estimates their congestion costs by a…
A Robust Utility Learning Framework via Inverse Optimization
Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin +2
In many smart infrastructure applications flexibility in achieving sustainability goals can be gained by engaging end-users. However, these users often have heterogeneous preferenc…
Statistical Estimation with Strategic Data Sources in Competitive Settings
Tyler Westenbroek, Roy Dong, Lillian J. Ratliff +1
In this paper, we introduce a preliminary model for interactions in the data market. Recent research has shown ways in which a data aggregator can design mechanisms for users to en…
Optimizing Curbside Parking Resources Subject to Congestion Constraints
Chase Dowling, Tanner Fiez, Lillian Ratliff +1
To gain theoretical insight into the relationship between parking scarcity and congestion, we describe block-faces of curbside parking as a network of queues. Due to the nature of…
Inverse Risk-Sensitive Reinforcement Learning
Lillian J. Ratliff, Eric Mazumdar
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…