Online Learning Demands in Max-min Fairness

• URL: http://arxiv.org/abs/2012.08648v1
• Date: Tue, 15 Dec 2020 22:15:20 GMT
• Title: Online Learning Demands in Max-min Fairness
• Authors: Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E Gonzalez, Michael I Jordan, Ion Stoica
• Abstract summary: We describe mechanisms for the allocation of a scarce resource among multiple users in a way that is efficient, fair, and strategy-proof. The mechanism is repeated for multiple rounds and a user's requirements can change on each round. At the end of each round, users provide feedback about the allocation they received, enabling the mechanism to learn user preferences over time.
• Score: 91.37280766977923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe mechanisms for the allocation of a scarce resource among multiple users in a way that is efficient, fair, and strategy-proof, but when users do not know their resource requirements. The mechanism is repeated for multiple rounds and a user's requirements can change on each round. At the end of each round, users provide feedback about the allocation they received, enabling the mechanism to learn user preferences over time. Such situations are common in the shared usage of a compute cluster among many users in an organisation, where all teams may not precisely know the amount of resources needed to execute their jobs. By understating their requirements, users will receive less than they need and consequently not achieve their goals. By overstating them, they may siphon away precious resources that could be useful to others in the organisation. We formalise this task of online learning in fair division via notions of efficiency, fairness, and strategy-proofness applicable to this setting, and study this problem under three types of feedback: when the users' observations are deterministic, when they are stochastic and follow a parametric model, and when they are stochastic and nonparametric. We derive mechanisms inspired by the classical max-min fairness procedure that achieve these requisites, and quantify the extent to which they are achieved via asymptotic rates. We corroborate these insights with an experimental evaluation on synthetic problems and a web-serving task.

Related papers

• Active Learning for Fair and Stable Online Allocations [6.23798328186465]
  We consider feedback from a select subset of agents at each epoch of the online resource allocation process. Our algorithms provide regret bounds that are sub-linear in number of time-periods for various measures. We show that efficient decision-making does not require extensive feedback and produces efficient outcomes for a variety of problem classes.
  arXiv  Detail & Related papers  (2024-06-20T23:23:23Z)
• Resilient Constrained Learning [94.27081585149836]
  This paper presents a constrained learning approach that adapts the requirements while simultaneously solving the learning task. We call this approach resilient constrained learning after the term used to describe ecological systems that adapt to disruptions by modifying their operation.
  arXiv  Detail & Related papers  (2023-06-04T18:14:18Z)
• Classification Performance Metric Elicitation and its Applications [5.5637552942511155]
  Despite its practical interest, there is limited formal guidance on how to select metrics for machine learning applications. This thesis outlines metric elicitation as a principled framework for selecting the performance metric that best reflects implicit user preferences.
  arXiv  Detail & Related papers  (2022-08-19T03:57:17Z)
• Emergent specialization from participation dynamics and multi-learner retraining [26.913065669463247]
  We analyze a class of dynamics where users allocate their participation amongst services to reduce the individual risk they experience. We find that repeated myopic updates with multiple learners lead to better outcomes.
  arXiv  Detail & Related papers  (2022-06-06T15:12:56Z)
• Learning from Heterogeneous Data Based on Social Interactions over Graphs [58.34060409467834]
  This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting.
  arXiv  Detail & Related papers  (2021-12-17T12:47:18Z)
• Online Learning of Competitive Equilibria in Exchange Economies [94.24357018178867]
  In economics, the sharing of scarce resources among multiple rational agents is a classical problem. We propose an online learning mechanism to learn agent preferences. We demonstrate the effectiveness of this mechanism through numerical simulations.
  arXiv  Detail & Related papers  (2021-06-11T21:32:17Z)
• Conditional Meta-Learning of Linear Representations [57.90025697492041]
  Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks. In this work we overcome this issue by inferring a conditioning function, mapping the tasks' side information into a representation tailored to the task at hand. We propose a meta-algorithm capable of leveraging this advantage in practice.
  arXiv  Detail & Related papers  (2021-03-30T12:02:14Z)
• Regularized Online Allocation Problems: Fairness and Beyond [7.433931244705934]
  We introduce the emphregularized online allocation problem, a variant that includes a non-linear regularizer acting on the total resource consumption. In this problem, requests repeatedly arrive over time and, for each request, a decision maker needs to take an action that generates a reward and consumes resources. The objective is to simultaneously maximize additively separable rewards and the value of a non-separable regularizer subject to the resource constraints.
  arXiv  Detail & Related papers  (2020-07-01T14:24:58Z)
• Empowering Active Learning to Jointly Optimize System and User Demands [70.66168547821019]
  We propose a new active learning approach that jointly optimize the active learning system (training efficiently) and the user (receiving useful instances) We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
  arXiv  Detail & Related papers  (2020-05-09T16:02:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.