Time Fairness in Online Knapsack Problems

• URL: http://arxiv.org/abs/2305.13293v2
• Date: Wed, 17 Apr 2024 14:13:52 GMT
• Title: Time Fairness in Online Knapsack Problems
• Authors: Adam Lechowicz, Rik Sengupta, Bo Sun, Shahin Kamali, Mohammad Hajiesmaili,
• Abstract summary: The online knapsack problem is a classic problem in the field of online algorithms. We formalize a practically-relevant notion of time fairness which effectively models a trade off between static and dynamic pricing. We develop a nearly-optimal learning-augmented algorithm which is fair, consistent, and robust (competitive)
• Score: 6.435514551504644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The online knapsack problem is a classic problem in the field of online algorithms. Its canonical version asks how to pack items of different values and weights arriving online into a capacity-limited knapsack so as to maximize the total value of the admitted items. Although optimal competitive algorithms are known for this problem, they may be fundamentally unfair, i.e., individual items may be treated inequitably in different ways. We formalize a practically-relevant notion of time fairness which effectively models a trade off between static and dynamic pricing in a motivating application such as cloud resource allocation, and show that existing algorithms perform poorly under this metric. We propose a parameterized deterministic algorithm where the parameter precisely captures the Pareto-optimal trade-off between fairness (static pricing) and competitiveness (dynamic pricing). We show that randomization is theoretically powerful enough to be simultaneously competitive and fair; however, it does not work well in experiments. To further improve the trade-off between fairness and competitiveness, we develop a nearly-optimal learning-augmented algorithm which is fair, consistent, and robust (competitive), showing substantial performance improvements in numerical experiments.

Related papers

• Nonstationary Dual Averaging and Online Fair Allocation [32.85609942201268]
  We develop new online learning results for the dual averaging algorithm under nonstationary input models. Our results apply to several nonstationary input models: adversarial corruption, ergodic input, and blockindependent (including periodic) input.
  arXiv  Detail & Related papers  (2022-02-23T16:50:24Z)
• Optimal Algorithms for Decentralized Stochastic Variational Inequalities [113.43047601775453]
  This work concentrates on the decentralized setting, which is increasingly important but not well understood. We present lower bounds for both communication and local iterations and construct optimal algorithms that match these lower bounds. Our algorithms are the best among the available not only in the decentralized case, but also in the deterministic and non-distributed literature.
  arXiv  Detail & Related papers  (2022-02-06T13:14:02Z)
• Fairer LP-based Online Allocation [13.478067250930101]
  We consider a Linear Program (LP)-based online resource allocation problem where a decision maker accepts or rejects incoming customer requests irrevocably. We propose a fair algorithm that uses an interior-point LP solver and dynamically detects unfair resource spending. Our approach do not formulate the fairness requirement as a constrain in the optimization instance, and instead we address the problem from the perspective of algorithm design.
  arXiv  Detail & Related papers  (2021-10-27T17:45:20Z)
• Learning Equilibria in Matching Markets from Bandit Feedback [139.29934476625488]
  We develop a framework and algorithms for learning stable market outcomes under uncertainty. Our work takes a first step toward elucidating when and how stable matchings arise in large, data-driven marketplaces.
  arXiv  Detail & Related papers  (2021-08-19T17:59:28Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• Algorithmic Challenges in Ensuring Fairness at the Time of Decision [6.228560624452748]
  Algorithmic decision-making in societal contexts can be framed as optimization under bandit feedback. Recent lawsuits accuse companies that deploy algorithmic pricing practices of price gouging. We introduce the well-studied fairness notion of envy-freeness within the context of convex optimization.
  arXiv  Detail & Related papers  (2021-03-16T19:06:28Z)
• Cost-Efficient Online Hyperparameter Optimization [94.60924644778558]
  We propose an online HPO algorithm that reaches human expert-level performance within a single run of the experiment. Our proposed online HPO algorithm reaches human expert-level performance within a single run of the experiment, while incurring only modest computational overhead compared to regular training.
  arXiv  Detail & Related papers  (2021-01-17T04:55:30Z)
• Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms [85.97516436641533]
  We study the problem of improving the performance of online algorithms by incorporating machine-learned predictions. The goal is to design algorithms that are both consistent and robust. We provide the first set of non-trivial lower bounds for competitive analysis using machine-learned predictions.
  arXiv  Detail & Related papers  (2020-10-22T04:51:01Z)
• Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms [0.0]
  This study is the most comprehensive of its kind. It considers fairness, predictive-performance, calibration quality, and speed of 28 different modelling pipelines. We also found that fairness-aware algorithms can induce fairness without material drops in predictive power.
  arXiv  Detail & Related papers  (2020-10-08T13:58:09Z)
• Beyond Individual and Group Fairness [90.4666341812857]
  We present a new data-driven model of fairness that is guided by the unfairness complaints received by the system. Our model supports multiple fairness criteria and takes into account their potential incompatibilities.
  arXiv  Detail & Related papers  (2020-08-21T14:14:44Z)
• 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)

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.