Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

• URL: http://arxiv.org/abs/2410.11820v1
• Date: Tue, 15 Oct 2024 17:47:44 GMT
• Title: Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
• Authors: Yiding Jiang, Allan Zhou, Zhili Feng, Sadhika Malladi, J. Zico Kolter,
• Abstract summary: We introduce Adaptive Data Optimization (ADO), an algorithm that optimize data distributions in an online fashion, concurrent with model training. ADO does not require external knowledge, proxy models, or modifications to the model update. ADO uses per-domain scaling laws to estimate the learning potential of each domain during training and adjusts the data mixture accordingly.
• Score: 59.03420759554073
• License:
• Abstract: The composition of pretraining data is a key determinant of foundation models' performance, but there is no standard guideline for allocating a limited computational budget across different data sources. Most current approaches either rely on extensive experiments with smaller models or dynamic data adjustments that also require proxy models, both of which significantly increase the workflow complexity and computational overhead. In this paper, we introduce Adaptive Data Optimization (ADO), an algorithm that optimizes data distributions in an online fashion, concurrent with model training. Unlike existing techniques, ADO does not require external knowledge, proxy models, or modifications to the model update. Instead, ADO uses per-domain scaling laws to estimate the learning potential of each domain during training and adjusts the data mixture accordingly, making it more scalable and easier to integrate. Experiments demonstrate that ADO can achieve comparable or better performance than prior methods while maintaining computational efficiency across different computation scales, offering a practical solution for dynamically adjusting data distribution without sacrificing flexibility or increasing costs. Beyond its practical benefits, ADO also provides a new perspective on data collection strategies via scaling laws.

Related papers

• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• Dynamic Model Switching for Improved Accuracy in Machine Learning [0.0]
  We introduce an adaptive ensemble that intuitively transitions between CatBoost and XGBoost. The user sets a benchmark, say 80% accuracy, prompting the system to dynamically shift to the new model only if it guarantees improved performance. This dynamic model-switching mechanism aligns with the evolving nature of data in real-world scenarios.
  arXiv  Detail & Related papers  (2024-01-31T00:13:02Z)
• Functional Graphical Models: Structure Enables Offline Data-Driven Optimization [111.28605744661638]
  We show how structure can enable sample-efficient data-driven optimization. We also present a data-driven optimization algorithm that infers the FGM structure itself.
  arXiv  Detail & Related papers  (2024-01-08T22:33:14Z)
• Navigating Scaling Laws: Compute Optimality in Adaptive Model Training [39.96209967632896]
  In recent years, the state-of-the-art in deep learning has been dominated by very large models that have been pre-trained on vast amounts of data. We extend the concept of optimality by allowing for an adaptive' model, i.e. a model that can change its shape during training.
  arXiv  Detail & Related papers  (2023-11-06T16:20:28Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case [7.674023644408741]
  We propose a transfer learning algorithm that combines new and historical data with different input dimensions. Our approach achieves state-of-the-art performance on 9 real-life datasets.
  arXiv  Detail & Related papers  (2022-02-10T14:57:15Z)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)
• Dynamic Scale Training for Object Detection [111.33112051962514]
  We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection. Experimental results demonstrate the efficacy of our proposed DST towards scale variation handling. It does not introduce inference overhead and could serve as a free lunch for general detection configurations.
  arXiv  Detail & Related papers  (2020-04-26T16:48:17Z)

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.