arXiv:2504.10796v4 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) is widely used for decision-making under uncertainty, but its adversarial focus on worst-case loss can lead to overly conservative policies. To mitigate this, we study ex-ante Distributionally Robust Regret Optimization (DRRO) with Wasserstein ambiguity sets, designed to balance robustness with upside potential. We develop a theory of Wasserstein DRRO (WDRRO) paralleling Wasserstein DRO. Under smoothness and regularity, WDRRO selects among ERM optima by a first-order gradient-discrepancy rule.
The paper addresses a prevalent issue in AI deployment where overly conservative optimizations hinder real-world applicability, leading to a focus on mitigating this with new theoretical frameworks like WDRRO.
This research provides a more nuanced approach to decision-making under uncertainty in AI, potentially leading to more effective and deployable AI systems by balancing robustness with performance upside.
The optimization paradigm for AI under uncertainty could shift from purely worst-case scenarios to a more balanced approach that considers regret optimization, improving model utility in complex environments.
- · AI developers
- · Risk management sectors
- · Machine learning researchers
- · AI models relying solely on worst-case DRO
AI models could become more performant and less conservative in real-world applications.
Increased adoption of AI in high-stakes environments due to improved robustness and reduced conservatism.
New AI-driven financial products or autonomous systems that are less risk-averse but still robust could emerge.
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Read at arXiv cs.LG