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Found 8 papers, 1 papers with code
An Approximate Dynamic Programming Approach to Adversarial Online Learning
We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses.
The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms
This paper proposes the Square Root Agreement Rule (SRA): a simple reward mechanism that incentivizes truthful responses to objective evaluations on such platforms.
Matching while Learning
We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time.
Sequential Relevance Maximization with Binary Feedback
For every product category, each type has an associated relevance feedback that is assumed to be binary: the category is either relevant or irrelevant.
Exploration vs. Exploitation in Team Formation
An online labor platform faces an online learning problem in matching workers with jobs and using the performance on these jobs to create better future matches.
Individual Fairness in Hindsight
In this paper, we extend the notion of IF to account for the time at which a decision is made, in settings where there exists a notion of conduciveness of decisions as perceived by the affected individuals.
Maximal Objectives in the Multi-armed Bandit with Applications
In this paper, motivated by certain operational concerns in online platforms, we consider a new objective in the classical setup.
Algorithmic Challenges in Ensuring Fairness at the Time of Decision
The goal of this paper is to highlight the rich interface between temporal notions of fairness and online decision-making through a novel meta-objective of ensuring fairness at the time of decision.
