Online Scoring with Delayed Information: A Convex Optimization Viewpoint

TL;DR

This paper presents an online convex optimization framework for scoring agents with delayed and partially observed context information, providing bounds on estimation error that improve over time.

Contribution

It introduces a novel online convex game approach to estimate delayed context in scoring systems, extending to Banach spaces and considering adversarial delays.

Findings

Error scales as O(1/√T) over time

Incorporating side information improves error bounds

Framework applies to fixed and adversarial delays

Abstract

We consider a system where agents enter in an online fashion and are evaluated based on their attributes or context vectors. There can be practical situations where this context is partially observed, and the unobserved part comes after some delay. We assume that an agent, once left, cannot re-enter the system. Therefore, the job of the system is to provide an estimated score for the agent based on her instantaneous score and possibly some inference of the instantaneous score over the delayed score. In this paper, we estimate the delayed context via an online convex game between the agent and the system. We argue that the error in the score estimate accumulated over $T$ iterations is small if the regret of the online convex game is small. Further, we leverage side information about the delayed context in the form of a correlation function with the known context. We consider the settings where the delay is fixed or arbitrarily chosen by an adversary. Furthermore, we extend the formulation to the setting where the contexts are drawn from some Banach space. Overall, we show that the average penalty for not knowing the delayed context while making a decision scales with $\mathcal{O}(\frac{1}{\sqrt{T}})$, where this can be improved to $\mathcal{O}(\frac{\log T}{T})$ under special setting.