# Delegated Classification

arXiv: https://arxiv.org/abs/2306.11475 · HTML: https://mlsanity.com/paper/2306.11475/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 47 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [A Constructive Prediction of the Generalization Error Across Scales](https://mlsanity.com/paper/1909.12673/) (2019)
- [Revisiting Neural Scaling Laws in Language and Vision](https://mlsanity.com/paper/2209.06640/) (2022)
- [Scaling Laws for Neural Machine Translation](https://mlsanity.com/paper/2109.07740/) (2021)
- [Learning Curve Theory](https://mlsanity.com/paper/2102.04074/) (2021)
- [How Do Classifiers Induce Agents To Invest Effort Strategically?](https://mlsanity.com/paper/1807.05307/) (2018)
- [Training Trajectories of Language Models Across Scales](https://mlsanity.com/paper/2212.09803/) (2022)
- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [A Theory of Universal Learning](https://mlsanity.com/paper/2011.04483/) (2020)
- [Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems](https://mlsanity.com/paper/1405.2875/) (2014)
- [The Sample Complexity of Online Contract Design](https://mlsanity.com/paper/2211.05732/) (2022)
- [Simple versus Optimal Contracts](https://mlsanity.com/paper/1808.03713/) (2018)
- [Optimum Statistical Estimation with Strategic Data Sources](https://mlsanity.com/paper/1408.2539/) (2014)
- [How Much More Data Do I Need? Estimating Requirements for Downstream Tasks](https://mlsanity.com/paper/2207.01725/) (2022)
- [Learning Curves for Analysis of Deep Networks](https://mlsanity.com/paper/2010.11029/) (2020)
- [Principal-Agent Hypothesis Testing](https://mlsanity.com/paper/2205.06812/) (2022)
- [Multi-Agent Contracts](https://mlsanity.com/paper/2211.05434/) (2022)
- [Learning Approximately Optimal Contracts](https://mlsanity.com/paper/1811.06736/) (2018)
- [Combinatorial Contracts](https://mlsanity.com/paper/2109.14260/) (2021)
- [Monotone Learning](https://mlsanity.com/paper/2202.05246/) (2022)
- [Delegated Search Approximates Efficient Search](https://mlsanity.com/paper/1806.06933/) (2018)
- [A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs](https://mlsanity.com/paper/2105.01867/) (2021)
- [Data Budgeting for Machine Learning](https://mlsanity.com/paper/2210.00987/) (2022)

## Built on by (most cited first, 10 of 10)

- [Incentivizing Quality Text Generation via Statistical Contracts](https://mlsanity.com/paper/2406.11118/) (2024)
- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Delegating Data Collection in Decentralized Machine Learning](https://mlsanity.com/paper/2309.01837/) (2023)
- [Contractual Reinforcement Learning: Pulling Arms with Invisible Hands](https://mlsanity.com/paper/2407.01458/) (2024)
- [Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains](https://mlsanity.com/paper/2403.06672/) (2024)
- [Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap](https://mlsanity.com/paper/2502.11981/) (2025)
- [Incentivizing High-Quality Human Annotations with Golden Questions](https://mlsanity.com/paper/2505.19134/) (2025)
- [LARP: Learner-Agnostic Robust Data Prefiltering](https://mlsanity.com/paper/2506.20573/) (2025)
- [Microeconomic Foundations of Multi-Agent Learning](https://mlsanity.com/paper/2601.03451/) (2026)
- [Incentive Aware AI Regulations: A Credal Characterisation](https://mlsanity.com/paper/2603.05175/) (2026)

## Abstract (arXiv)

When machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance. In this work, we propose a theoretical framework for incentive-aware delegation of machine learning tasks. We model delegation as a principal-agent game, in which accurate learning can be incentivized by the principal using performance-based contracts. Adapting the economic theory of contract design to this setting, we define budget-optimal contracts and prove they take a simple threshold form under reasonable assumptions. In the binary-action case, the optimality of such contracts is shown to be equivalent to the classic Neyman-Pearson lemma, establishing a formal connection between contract design and statistical hypothesis testing. Empirically, we demonstrate that budget-optimal contracts can be constructed using small-scale data, leveraging recent advances in the study of learning curves and scaling laws. Performance and economic outcomes are evaluated using synthetic and real-world classification tasks.