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cs.LG2026
Beyond Surrogates: A Quantitative Analysis for Inter-Metric Relationships
Yuanhao Pu, Defu Lian, Enhong Chen
The Consistency property between surrogate losses and evaluation metrics has been extensively studied to ensure that minimizing a loss leads to metric optimality. However, the dire…
cs.LG2026
Is Softmax Loss All You Need? A Principled Analysis of Softmax-family Loss
Yuanhao Pu, Defu Lian, Enhong Chen
The Softmax loss is one of the most widely employed surrogate objectives for classification and ranking tasks. To elucidate its theoretical properties, the Fenchel-Young framework…
cs.LG2025
NDCG-Consistent Softmax Approximation with Accelerated Convergence
Yuanhao Pu, Defu Lian, Xiaolong Chen +3
Ranking tasks constitute fundamental components of extreme similarity learning frameworks, where extremely large corpora of objects are modeled through relative similarity relation…