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Researchers study learning curves for revenue maximization in machine learning

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

This paper introduces the study of learning curves for revenue maximization, focusing on the scenario of a single item and a single buyer. The research demonstrates that while a Bayes-consistent algorithm can achieve zero error for any valuation distribution as sample size increases, this convergence can be arbitrarily slow. However, if the optimal revenue is obtained through a finite price, the learning curve decay rate approaches $1/\sqrt{n}$. For distributions on discrete values, the paper shows an almost exponential decay rate, surpassing the PAC framework's limitations. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces new theoretical bounds for learning curves in revenue maximization, potentially impacting algorithmic pricing strategies.

RANK_REASON Academic paper introducing a new theoretical framework for learning curves in revenue maximization.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Steve Hanneke, Alkis Kalavasis, Shay Moran, Grigoris Velegkas ·

    On the Learning Curves of Revenue Maximization

    arXiv:2604.26922v1 Announce Type: cross Abstract: Learning curves are a fundamental primitive in supervised learning, describing how an algorithm's performance improves with more data and providing a quantitative measure of its generalization ability. Formally, a learning curve p…

  2. arXiv stat.ML TIER_1 · Grigoris Velegkas ·

    On the Learning Curves of Revenue Maximization

    Learning curves are a fundamental primitive in supervised learning, describing how an algorithm's performance improves with more data and providing a quantitative measure of its generalization ability. Formally, a learning curve plots the decay of an algorithm's error for a fixed…

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