QueryMarket: Cost-Aware Online Active Learning in Data Markets

• URL: http://arxiv.org/abs/2606.17805v1
• Date: Tue, 16 Jun 2026 11:28:07 GMT
• Title: QueryMarket: Cost-Aware Online Active Learning in Data Markets
• Abstract summary: We propose OVBAL (online variance-based active learning), which integrates data pricing with information-driven selection.<n>OVBAL yields a simple, fully online decision rule that adapts to nonstationary streams and heterogeneous label costs.<n>Experiments on synthetic data and a real-world solar power generation forecasting task show that OVBAL is particularly effective under seller-centric pricing.
• Score: 2.7429630700600893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data acquisition is a major bottleneck for learning in real-time streams: analysts must decide on the fly which labels to purchase while respecting a rolling budget. However, existing online active learning rarely unifies pricing, information gain, and rolling budget constraints under concept drift. We introduce QueryMarket, a market-inspired framework that queries each incoming data point based on its estimated utility to the model and its price. Within this framework, we propose OVBAL (online variance-based active learning), which integrates data pricing with information-driven selection by estimating each sample's marginal utility via a D-optimality criterion with exponential forgetting and executing cost-aware purchases under rolling budget constraints. OVBAL yields a simple, fully online decision rule that adapts to nonstationary streams and heterogeneous label costs. Experiments on synthetic data and a real-world solar power generation forecasting task show that OVBAL is particularly effective under seller-centric pricing and yields a more favorable long-run error-cost trade-off in the real-world task under both pricing schemes.

Related papers

• How to Purchase Labels? A Cost-Effective Approach Using Active Learning Markets [2.7429630700600893]
  We introduce and analyse active learning markets as a way to purchase labels.<n>This comes in contrast to the many proposals that already exist to purchase features and examples.<n>We focus on a single-buyer-multiple-seller setup and propose the use of two active learning strategies.
  arXiv  Detail & Related papers  (2025-11-25T18:34:33Z)
• BAT: Benchmark for Auto-bidding Task [67.56067222427946]
  We present an auction benchmark encompassing the two most prevalent auction formats.<n>We implement a series of robust baselines on a novel dataset.<n>This benchmark provides a user-friendly and intuitive framework for researchers and practitioners to develop and refine innovative autobidding algorithms.
  arXiv  Detail & Related papers  (2025-05-13T12:12:34Z)
• Compute-Constrained Data Selection [77.06528009072967]
  We find that many powerful data selection methods are almost never compute-optimal.<n>For compute-optimal training, we find that perplexity and gradient data selection require training-to-selection model size ratios of 5x and 10x, respectively.
  arXiv  Detail & Related papers  (2024-10-21T17:11:21Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning [70.22819290458581]
  Reinforcement learning with human feedback (RLHF) is a widely adopted approach in current large language model pipelines. Our approach introduces two key innovations: (1) on-policy query to avoid OOD and imbalance issues in seed data, and (2) active learning to select the most informative data for preference queries.
  arXiv  Detail & Related papers  (2024-07-02T10:09:19Z)
• A Bargaining-based Approach for Feature Trading in Vertical Federated Learning [54.51890573369637]
  We propose a bargaining-based feature trading approach in Vertical Federated Learning (VFL) to encourage economically efficient transactions. Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties.
  arXiv  Detail & Related papers  (2024-02-23T10:21:07Z)
• Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
  We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets. We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers. Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
  arXiv  Detail & Related papers  (2023-11-22T22:15:17Z)
• Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm [14.206050847214652]
  We introduce a new algorithm to solve budget allocation and revenue allocation problems simultaneously in linear time. The new algorithm employs an adaptive sampling process that selects data from those providers who are contributing the most to the model. We provide theoretical guarantees for the algorithm that show the budget is used efficiently and the properties of revenue allocation are similar to Shapley's.
  arXiv  Detail & Related papers  (2023-06-05T02:28:19Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements [3.2732273647357437]
  We study an online variant of Fisher markets, wherein users with privately known utility and budget parameters arrive sequentially. In this setting, we first study the limitations of static pricing algorithms, which set uniform prices for all users. We design adaptive posted-pricing algorithms, one with knowledge of the distribution of users' budget and utility parameters and another that adjusts prices solely based on past observations of user consumption.
  arXiv  Detail & Related papers  (2022-04-27T05:03:45Z)
• Stream-based Active Learning with Verification Latency in Non-stationary Environments [6.883906273999368]
  We investigate the influence of finite, time-variable, and unknown verification delay, in the presence of concept drift on AL approaches. We propose PRopagate, a latency independent utility estimator which predicts the requested, but not yet known, labels. We empirically show that the proposed method consistently outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2022-04-14T08:51:15Z)
• Data-Driven Online Interactive Bidding Strategy for Demand Response [0.30586855806896046]
  Demand response (DR) provides the services of peak shaving, enhancing the efficiency of renewable energy utilization with a short response period, and low cost. Various categories of DR are established, e.g. automated DR, incentive DR, emergency DR, and demand bidding. This paper determines the bidding and purchasing strategy simultaneously employing the smart meter data and functions. The results prove that when facing diverse situations the proposed model can earn the optimal profit via off/online learning the bidding rules and robustly making the proper bid.
  arXiv  Detail & Related papers  (2022-02-09T02:44:20Z)

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.