arXiv:2602.02877v2 Announce Type: replace Abstract: This paper studies optimization for a family of problems termed $\textbf{compositional entropic risk minimization}$, in which each data's loss is formulated as a Log-Expectation-Exponential (Log-E-Exp) function. The Log-E-Exp formulation serves as an abstraction of the Log-Sum-Exponential (LogSumExp) function when the explicit summation inside the logarithm is taken over a gigantic number of items and is therefore expensive to evaluate. While entropic risk objectives of this form arise in many machine learning problems, existing optimization
This paper presents a geometry-aware efficient algorithm for compositional entropic risk minimization, addressing a known computational bottleneck in advanced machine learning models.
Improved optimization techniques for complex risk functions can unlock the scalability and efficiency of new AI architectures, potentially accelerating progress in cutting-edge applications.
The computational barrier for certain types of entropic risk minimization, particularly those involving 'gigantic' summations, is reduced, making more complex models feasible.
- · AI researchers
- · Machine learning startups
- · Cloud AI providers
- · Organizations with inefficient ML infrastructure
More sophisticated AI models become computationally tractable for training and deployment.
This could lead to breakthroughs in areas requiring highly expressive risk formulations, such as reinforcement learning or robust optimization.
The enhanced efficiency might indirectly contribute to the compute supply chain by allowing more work to be done with existing hardware, or shifting demand towards more specialized compute.
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Read at arXiv cs.LG