Scaling Laws for the Value of Individual Data Points in Machine Learning
- URL: http://arxiv.org/abs/2405.20456v1
- Date: Thu, 30 May 2024 20:10:24 GMT
- Title: Scaling Laws for the Value of Individual Data Points in Machine Learning
- Authors: Ian Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou,
- Abstract summary: We introduce a new perspective by investigating scaling behavior for the value of individual data points.
We provide learning theory to support our scaling law, and we observe empirically that it holds across diverse model classes.
Our work represents a first step towards understanding and utilizing scaling properties for the value of individual data points.
- Score: 55.596413470429475
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent works have shown that machine learning models improve at a predictable rate with the total amount of training data, leading to scaling laws that describe the relationship between error and dataset size. These scaling laws can help design a model's training dataset, but they typically take an aggregate view of the data by only considering the dataset's size. We introduce a new perspective by investigating scaling behavior for the value of individual data points: we find that a data point's contribution to model's performance shrinks predictably with the size of the dataset in a log-linear manner. Interestingly, there is significant variability in the scaling exponent among different data points, indicating that certain points are more valuable in small datasets while others are relatively more useful as a part of large datasets. We provide learning theory to support our scaling law, and we observe empirically that it holds across diverse model classes. We further propose a maximum likelihood estimator and an amortized estimator to efficiently learn the individualized scaling behaviors from a small number of noisy observations per data point. Using our estimators, we provide insights into factors that influence the scaling behavior of different data points. Finally, we demonstrate applications of the individualized scaling laws to data valuation and data subset selection. Overall, our work represents a first step towards understanding and utilizing scaling properties for the value of individual data points.
Related papers
- Capturing the Temporal Dependence of Training Data Influence [100.91355498124527]
We formalize the concept of trajectory-specific leave-one-out influence, which quantifies the impact of removing a data point during training.
We propose data value embedding, a novel technique enabling efficient approximation of trajectory-specific LOO.
As data value embedding captures training data ordering, it offers valuable insights into model training dynamics.
arXiv Detail & Related papers (2024-12-12T18:28:55Z) - Diffusion Models as Data Mining Tools [87.77999285241219]
This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining.
We show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure.
This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease.
arXiv Detail & Related papers (2024-07-20T17:14:31Z) - Scaling Laws For Dense Retrieval [22.76001461620846]
We investigate whether the performance of dense retrieval models follows the scaling law as other neural models.
Results indicate that, under our settings, the performance of dense retrieval models follows a precise power-law scaling related to the model size and the number of annotations.
arXiv Detail & Related papers (2024-03-27T15:27:36Z) - Towards Neural Scaling Laws on Graphs [54.435688297561015]
We investigate how the performance of deep graph models changes with model and dataset sizes.
For model scaling, we identify that despite the parameter numbers, the model depth also plays an important role in affecting the model scaling behaviors.
We reform the data scaling law with the number of nodes or edges as the metric to address the irregular graph sizes.
arXiv Detail & Related papers (2024-02-03T06:17:21Z) - Combining Public Human Activity Recognition Datasets to Mitigate Labeled
Data Scarcity [1.274578243851308]
We propose a novel strategy to combine publicly available datasets with the goal of learning a generalized HAR model.
Our experimental evaluation, which includes experimenting with different state-of-the-art neural network architectures, shows that combining public datasets can significantly reduce the number of labeled samples.
arXiv Detail & Related papers (2023-06-23T18:51:22Z) - Self-supervised Activity Representation Learning with Incremental Data:
An Empirical Study [7.782045150068569]
This research examines the impact of using a self-supervised representation learning model for time series classification tasks.
We analyzed the effect of varying the size, distribution, and source of the unlabeled data on the final classification performance across four public datasets.
arXiv Detail & Related papers (2023-05-01T01:39:55Z) - Revisiting Neural Scaling Laws in Language and Vision [43.57394336742374]
We argue for a more rigorous methodology based on the extrapolation loss, instead of reporting the best-fitting parameters.
We present a recipe for estimating scaling law parameters reliably from learning curves.
We demonstrate that it extrapolates more accurately than previous methods in a wide range of architecture families across several domains.
arXiv Detail & Related papers (2022-09-13T09:41:51Z) - Towards Open-World Feature Extrapolation: An Inductive Graph Learning
Approach [80.8446673089281]
We propose a new learning paradigm with graph representation and learning.
Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
arXiv Detail & Related papers (2021-10-09T09:02:45Z) - Scaling Laws for Transfer [0.5432984841650929]
We study scaling laws for transfer learning between distributions in an unsupervised, fine-tuning setting.
We find that the effective data transferred is described well in the low data regime by a power-law of parameter count and fine-tuning dataset size.
arXiv Detail & Related papers (2021-02-02T04:07:38Z) - Dataset Cartography: Mapping and Diagnosing Datasets with Training
Dynamics [118.75207687144817]
We introduce Data Maps, a model-based tool to characterize and diagnose datasets.
We leverage a largely ignored source of information: the behavior of the model on individual instances during training.
Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
arXiv Detail & Related papers (2020-09-22T20:19:41Z)
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.