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Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimization

Maria-Eleni Sfyraki, Jun-Kun Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26790 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

機械学習(ML)モデルをトレーニングすることは、通常、事前に基本的な真実ラベルを用いた例のセットを必要とする最適化プロセスです。これらの基本的な真実を取得するには、通常、労働力のある人間の努力を必要とし、コストがかかる可能性があります。人間のラベル化のスケーラビリティは、ラベル化する必要がある多数のデータポイントがあるときに懸念されます。この実践的な問題を解決するために、サブセットのサンプルが基本的な真実ラベルを含む最適化設定を検討しますが、残りのデータポイントはML予測器によってラベル化され、これらのポイントに間違ったラベルを割り当てることができる可能性があります。我々は、真実にラベル化されたサンプルとMLラベル化されたサンプル

Machine translation (Meta M2M100); the English original follows.

Abstract

Training a machine learning (ML) model is an optimization process that typically requires a set of examples with ground-truth labels beforehand. Obtaining these ground truths usually requires laborious human effort and can be costly. The scalability of human labeling becomes a concern when there are a large number of data points that need to be labeled. To address this practical issue, we consider an optimization setting where a subset of samples has ground-truth labels, while the remaining data points were labeled by an ML predictor, which could potentially assign incorrect labels to those points. We propose both first-order and zeroth-order stochastic optimization methods for training on datasets consisting of a mix of true-labeled and ML-labeled samples. We further conduct comprehensive empirical studies from training a linear regression model to fine-tuning a large language model (LLM) to showcase the effectiveness of the proposed algorithms for training with ML-labeled data.

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Topics: Computer science, Machine learning, Training set, Training (meteorology), Process (computing), Set (abstract data type)

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