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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv stat.ML English(EN) · · [2 sources]

    A Rigorous, Tractable Measure of Model Complexity

    Researchers have developed a new, mathematically sound, and computationally efficient method for measuring model complexity. This approach, based on analyzing similarities in model gradients across different inputs, is applicable to a wide range of models, including parametric, non-parametric, and kernel-based types. The proposed measure unifies and generalizes existing complexity metrics for various models like decision trees and neural networks, offering new insights into phenomena such as double descent. AI

    IMPACT Provides a unified and tractable method for assessing model complexity, aiding in interpretation, generalization, and model selection across various AI architectures.

  2. TOOL · arXiv cs.LG English(EN) ·

    Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification

    Researchers have developed new algorithms to efficiently calculate the Banzhaf value, a game-theoretic method for data valuation, specifically for k-nearest neighbors (kNN) classifiers. The study proves the computational hardness of the problem but introduces practical exact algorithms using dynamic programming, achieving pseudo-polynomial time complexity for weighted kNN and linear time complexity for unweighted kNN. Experiments on real-world datasets confirm the efficiency and effectiveness of these novel valuation methods. AI

    Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification

    IMPACT Introduces more efficient methods for understanding data contributions, potentially improving model training and interpretability.