Title: Is Softmax Loss all you need? A Principled Analysis of Softmax Loss and its Variants
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Softmax Loss, Fenchel-Young Loss, Consistency, Convergence, Classification, Ranking

Abstract: **The Softmax Loss** is one of the most widely employed surrogate objectives for classification and ranking, owing to its elegant algebraic structure, intuitive probabilistic interpretation, and consistently strong empirical performance. To elucidate its theoretical properties, recent works have introduced the Fenchel–Young framework, situating Softmax loss as a canonical instance within a broad family of convex surrogates. This perspective not only clarifies the origins of its favorable properties, but also unifies it with alternatives such as Sparsemax and $\alpha$-Entmax under a common theoretical foundation. Concurrently, another line of research has addressed on the challenge of scalability: when the number of classes is exceedingly large, computations of the partition function become prohibitively expensive. Numerous approximation strategies have thus been proposed to retain the benefits of the exact objective while improving efficiency. However, their theoretical fidelity remains unclear, and practical adoption often relies on heuristics or exhaustive search.

Building on these two perspectives, we present a principled investigation of the **Softmax-family** losses, encompassing both statistical and computational aspects. Within the Fenchel–Young framework, we examine whether different surrogates satisfy consistency with classification and ranking metrics, and analyze their gradient dynamics to reveal distinct convergence behaviors. For approximate Softmax methods, we introduce a systematic bias–variance decomposition that provides convergence guarantees. We further derive a per-epoch complexity analysis across the entire family, highlighting explicit trade-offs between accuracy and efficiency. Finally, extensive experiments on a representative recommendation task corroborate our theoretical findings, demonstrating a strong alignment between consistency, convergence, and empirical performance. Together, these results establish a principled foundation and offer practical guidance for loss selections in large-class machine learning applications.

TL;DR: We theoretically analyze the properties of "Softmax-family" losses and provide a clear guide on understanding and choosing surrogates for real-world scenarios.