mlsanityData to 7 Oct 2026

Optimum Statistical Estimation with Strategic Data Sources

arXiv 1408.2539 ↗First posted 11 Aug 2014stat.ML cs.GT cs.LGcited by 28

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Abstract (arXiv)

We propose an optimum mechanism for providing monetary incentives to the data sources of a statistical estimator such as linear regression, so that high quality data is provided at low cost, in the sense that the sum of payments and estimation error is minimized. The mechanism applies to a broad range of estimators, including linear and polynomial regression, kernel regression, and, under some additional assumptions, ridge regression. It also generalizes to several objectives, including minimizing estimation error subject to budget constraints. Besides our concrete results for regression problems, we contribute a mechanism design framework through which to design and analyze statistical estimators whose examples are supplied by workers with cost for labeling said examples.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2014.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 7; 2024: 1; 2025: 4; 2026: 1.

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  1. 2021How to Learn when Data Reacts to Your Model: Performative Gradient Descentcited by 70
  2. 2021Information Discrepancy in Strategic Learningcited by 48
  3. 2021Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responsescited by 32
  4. 2021Auctions Between Regret-Minimizing Agentscited by 22
  5. 2022Optimal and Differentially Private Data Acquisition: Central and Local Mechanismscited by 22
  6. 2022Learning When to Advise Human Decision Makerscited by 18
  7. 2021How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agentscited by 17
  8. 2022An Equivalence Between Data Poisoning and Byzantine Gradient Attackscited by 16
  9. 2023Delegated Classificationcited by 10
  10. 2023Delegating Data Collection in Decentralized Machine Learningcited by 8
  11. 2023Online covariance estimation for stochastic gradient descent under Markovian samplingcited by 7
  12. 2022Constrained Stochastic Nonconvex Optimization with State-dependent Markov Datacited by 6
16 more
  1. 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimizationcited by 6
  2. 2025SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixingcited by 5
  3. 2021Optimal Scoring Rule Design under Partial Knowledgecited by 4
  4. 2023Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labelingcited by 4
  5. 2024Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contributioncited by 4
  6. 2023Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regressioncited by 3
  7. 2021Auctions and Peer Prediction for Academic Peer Reviewcited by 2
  8. 2022Metric-Fair Classifier Derandomizationcited by 2
  9. 2021Strategyproof Learning: Building Trustworthy User-Generated Datasetscited by 1
  10. 2022Long-term Data Sharing under Exclusivity Attackscited by 1
  11. 2022Truthful Generalized Linear Modelscited by 1
  12. 2023No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctionscited by 0
  13. 2025Incentivizing High-Quality Human Annotations with Golden Questionscited by 0
  14. 2025A Cram'er-von Mises Approach to Incentivizing Truthful Data Sharingcited by 0
  15. 2025Users as Annotators: LLM Preference Learning from Comparison Modecited by 0
  16. 2026Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guaranteescited by 0