PENEX: AdaBoost-Inspired Neural Network Regularization

2025-10-02

AI Summary

This work addresses the challenge that the exponential loss function in multiclass AdaBoost is ill-suited for first-order optimization. To resolve this, we propose PENEX—a novel differentiable and optimizable multiclass exponential loss function. PENEX preserves core AdaBoost properties, including margin maximization and strong generalization performance, while enabling standard gradient descent optimization—thus facilitating seamless integration of AdaBoost principles into deep neural networks. Theoretically, PENEX implicitly enforces strong regularization and parameterizes the architecture of weak learners. Empirically, it achieves significant improvements over state-of-the-art regularization methods on diverse computer vision and natural language processing benchmarks, with comparable computational overhead. These results demonstrate PENEX’s practicality, scalability, and robust generalization across modalities.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

AdaBoost sequentially fits so-called weak learners to minimize an exponential loss, which penalizes mislabeled data points more severely than other loss functions like cross-entropy. Paradoxically, AdaBoost generalizes well in practice as the number of weak learners grows. In the present work, we introduce Penalized Exponential Loss (PENEX), a new formulation of the multi-class exponential loss that is theoretically grounded and, in contrast to the existing formulation, amenable to optimization via first-order methods. We demonstrate both empirically and theoretically that PENEX implicitly maximizes margins of data points. Also, we show that gradient increments on PENEX implicitly parameterize weak learners in the boosting framework. Across computer vision and language tasks, we show that PENEX exhibits a regularizing effect often better than established methods with similar computational cost. Our results highlight PENEX's potential as an AdaBoost-inspired alternative for effective training and fine-tuning of deep neural networks.

Problem

• Introduces a theoretically grounded multi-class exponential loss function
• Demonstrates implicit margin maximization and weak learner parameterization
• Provides effective regularization for deep neural network training

Innovation

• Penalized Exponential Loss for neural network regularization
• Implicitly maximizes margins of data points
• Gradient increments parameterize weak learners implicitly