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Linear-Core Surrogates offer smooth loss functions with linear rates for classification

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Linear-Core (LC) Surrogates, a novel family of convex loss functions designed to combine the benefits of smooth and piecewise-linear losses in machine learning. These surrogates are differentiable and achieve linear consistency bounds, offering improved statistical efficiency. In structured prediction tasks, LC Surrogates enable a more efficient stochastic gradient estimator, bypassing quadratic complexity and leading to significant computational and energy savings. AI

IMPACT Introduces a new loss function family that improves optimization speed and statistical efficiency, potentially accelerating training and reducing energy consumption in structured prediction tasks.

RANK_REASON Academic paper introducing a new class of loss functions with theoretical and empirical advantages.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Linear-Core Surrogates offer smooth loss functions with linear rates for classification

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Academic paper introducing a new class of loss functions with theoretical and empirical advantages.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Mehryar Mohri, Yutao Zhong ·

    Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction

    arXiv:2604.27742v1 Announce Type: cross Abstract: The choice of loss function in classification involves a fundamental trade-off: smooth losses (like Cross-Entropy) enable fast optimization rates but yield slow square-root consistency bounds, while piecewise-linear losses (like H…

  2. arXiv stat.ML TIER_1 English(EN) · Yutao Zhong ·

    Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction

    The choice of loss function in classification involves a fundamental trade-off: smooth losses (like Cross-Entropy) enable fast optimization rates but yield slow square-root consistency bounds, while piecewise-linear losses (like Hinge) offer fast linear consistency rates but suff…

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