Introduces a measure-transformation-based surrogate loss for solver-free training in predict-then-optimize problems, with Fisher consistency and excess risk bounds.
Estimate-then- optimize versus integrated-estimation-optimization versus sample average approximation: A stochastic dominance perspective
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Action-conditional conformal prediction sets provide per-action safety guarantees for risk-averse policies that optimize conditional value-at-risk through pinball-loss minimization.
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.
Lagrangian decomposition yields a scalable surrogate objective and losses for decision-focused learning that outperforms prior DFL methods on large multi-dimensional knapsack and quadratic portfolio instances.
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.
citing papers explorer
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A Solver-Free Training Method for Predict-then-Optimize
Introduces a measure-transformation-based surrogate loss for solver-free training in predict-then-optimize problems, with Fisher consistency and excess risk bounds.
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Conformal Risk-Averse Decision Making with Action Conditional Guarantee
Action-conditional conformal prediction sets provide per-action safety guarantees for risk-averse policies that optimize conditional value-at-risk through pinball-loss minimization.
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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.