Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
Estimate-then- optimize versus integrated-estimation-optimization versus sample average approximation: A stochastic dominance perspective
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Establishes equivalence conditions between nested and joint risk assessments in contextual optimization, shows policy independence from contextual risk measure under conditions, and proves SAA consistency in RKHS.
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.
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Risk-Controlled Post-Processing of Decision Policies
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
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Risk-averse Decision Making with Contextual Information: Model, Sample Average Approximation, and Kernelization
Establishes equivalence conditions between nested and joint risk assessments in contextual optimization, shows policy independence from contextual risk measure under conditions, and proves SAA consistency in RKHS.
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Decision-Focused Learning: When and Why Traditional Prediction Models Fail
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.