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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Data Validation01:03

Data Validation

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Data Validation01:15

Data Validation

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Censoring Survival Data01:09

Censoring Survival Data

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Related Experiment Videos

Early Stopping Without Validation Data in Weakly Supervised Learning.

Suqin Yuan, Muyang Li, Lei Feng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 13, 2026
    PubMed
    Summary

    Label Wave is a novel early stopping method for machine learning that eliminates the need for validation data. It effectively prevents overfitting in weakly supervised learning by monitoring training set prediction changes.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Overfitting is a significant challenge in machine learning, particularly in weakly supervised learning scenarios.
    • Traditional early stopping methods require substantial validation sets, creating a performance trade-off between training and validation data.
    • Validation data is often unavailable or unreliable in many weakly supervised learning contexts.

    Purpose of the Study:

    • To introduce Label Wave, a new early stopping technique that removes the dependency on validation data.
    • To enable effective model selection across diverse weakly supervised learning paradigms, including learning with noisy labels (LNL).
    • To address the limitations of conventional early stopping in data-scarce or unreliable validation scenarios.

    Main Methods:

    • Label Wave tracks alterations in model predictions on the training set during the training process.
    • It identifies the optimal training epoch by detecting minimum prediction fluctuations, signaling the transition from learning reliable patterns to fitting misleading ones.
    • The method is evaluated across various LNL conditions, model architectures, optimizers, and data types.

    Main Results:

    • Label Wave consistently selects near-optimal model checkpoints, with small test-accuracy gaps compared to oracle benchmarks.
    • The method significantly improves the performance of seven LNL methods, outperforming traditional hold-out validation approaches.
    • Experiments demonstrate robust performance across diverse noise levels, model families, optimizers, and data modalities in LNL tasks.

    Conclusions:

    • Label Wave offers a viable solution for early stopping without requiring validation data, particularly beneficial for weakly supervised learning.
    • The proposed method effectively mitigates overfitting and enhances model selection accuracy in challenging learning scenarios.
    • Label Wave demonstrates broad applicability and effectiveness across various machine learning paradigms and data conditions.