Sampler-Robust Optimization under Generative Models

2026-04-30

AI Summary

This work addresses the unreliability of decisions in generative-model-driven stochastic optimization, which arises from sampler misspecification and limited simulation budgets. To tackle this issue, the paper introduces a Sampler-Robust Optimization (SRO) framework that, for the first time, incorporates robust optimization at the sampler level. SRO explicitly models the worst-case sampler by perturbing the learned generator and integrates a minimax optimization strategy with a sharpness-aware robustness mechanism. The approach is applicable to both density-explicit and implicit generative models. Theoretical analysis provides high-probability upper bounds on performance, and empirical results demonstrate that SRO significantly improves out-of-sample performance and decision stability in portfolio optimization tasks.

Abstract

Modern stochastic optimization pipelines increasingly rely on learned generative models to represent uncertainty, while downstream decisions are evaluated almost entirely through Monte Carlo scenarios. This shifts the operational object of uncertainty from an explicit probability law to the sampler induced by the learned generator. Reliability therefore depends on two errors: sampler misspecification and finite-simulation error. We propose Sampler-Robust Optimization (SRO), which optimizes decisions against the worst-case sampler induced by perturbing the learned generator. This sampler-first formulation aligns with simulation-based decision pipelines and admits a sharpness-aware interpretation: it favors decisions whose performance is stable under generator perturbations, rather than merely under the nominal sampler. Under a coverage assumption, we show that the empirical worst-case objective provides a high-probability upper certificate for the true population objective, with finite-simulation error partially absorbed by the robustification used to guard against sampler misspecification. The framework accommodates generative models with or without explicit densities and admits efficient minimax procedures. Portfolio-optimization experiments show that SRO produces more stable decisions and improves out-of-sample performance under distribution shift.

Problem

• sampler misspecification
• finite-simulation error
• generative models
• stochastic optimization
• distribution shift

Innovation

• Sampler-Robust Optimization
• generative models
• distributional robustness
• Monte Carlo simulation
• minimax optimization