Lillian Ratliff

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  • Data-Driven Spatio-Temporal Analysis of Curbside Parking Demand: A Case-Study in Seattle

    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 needs, performance-based pricing schemes have received a significant amount of attention. However, several recent studies suggest location, time-of-day, and awareness of policies are the primary factors that drive parking decisions. In light of this, we provide an extensive data-driven study of the spatio-temporal characteristics of curbside parking. This work advances the understanding of where and when to set pricing policies, as well as where to target information and incentives to drivers looking to park. Harnessing data provided by the Seattle Department of Transportation, we develop a Gaussian mixture model based technique to identify zones with similar spatial parking demand as quantified by spatial autocorrelation. In support of this technique, we introduce a metric based on the repeatability of our Gaussian mixture model to investigate temporal consistency.

    12/02/2017 ∙ by Tanner Fiez, et al. ∙ 0 share

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  • Sequential Experimental Design for Transductive Linear Bandits

    In this paper we introduce the transductive linear bandit problem: given a set of measurement vectors X⊂R^d, a set of items Z⊂R^d, a fixed confidence δ, and an unknown vector θ^∗∈R^d, the goal is to infer argmax_z∈Z z^θ^∗ with probability 1-δ by making as few sequentially chosen noisy measurements of the form x^θ^∗ as possible. When X=Z, this setting generalizes linear bandits, and when X is the standard basis vectors and Z⊂{0,1}^d, combinatorial bandits. Such a transductive setting naturally arises when the set of measurement vectors is limited due to factors such as availability or cost. As an example, in drug discovery the compounds and dosages X a practitioner may be willing to evaluate in the lab in vitro due to cost or safety reasons may differ vastly from those compounds and dosages Z that can be safely administered to patients in vivo. Alternatively, in recommender systems for books, the set of books X a user is queried about may be restricted to well known best-sellers even though the goal might be to recommend more esoteric titles Z. In this paper, we provide instance-dependent lower bounds for the transductive setting, an algorithm that matches these up to logarithmic factors, and an evaluation. In particular, we provide the first non-asymptotic algorithm for linear bandits that nearly achieves the information theoretic lower bound.

    06/20/2019 ∙ by Tanner Fiez, et al. ∙ 0 share

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  • Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games

    Markov decision process (MDP) congestion game is an extension of classic congestion games, where a continuous population of selfish agents solves Markov decision processes with congestion: the payoff of a strategy decreases as more population uses it. We draw parallels between key concepts from capacitated congestion games and MDP. In particular, we show that population mass constraints in MDP congestion games are equivalent to imposing tolls/incentives on the reward function, which can be utilized by social planners to achieve auxiliary objectives. We demonstrate such methods in a simulated Seattle ride-share model, where tolls and incentives are enforced for two separate objectives: to guarantee minimum driver density in downtown Seattle, and to shift the game equilibrium towards a maximum social output.

    03/02/2019 ∙ by Sarah H. Q. Li, et al. ∙ 0 share

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