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The paper proposes a single-level optimization formulation that incorporates KKT conditions into decision-focused learning for mean-variance portfolio optimization, improving performance in experiments on real-world ETF data.
Proposes learned predictive ambiguity sets (LPAS) for distributionally robust optimization, where a deep contextual model outputs a nominal scenario distribution, state-dependent Wasserstein radius, and ground metric, trained with decision loss and calibration. Applied to portfolio optimization on S&P 500 data, the method achieves higher returns and Sharpe ratio with reduced conservatism compared to fixed-radius baselines.
This paper proposes a decision-focused generative framework for correlated scenario generation in distributionally robust optimization for grid dispatch, optimizing scenarios based on downstream operational cost rather than forecast accuracy. It reduces operational cost by 0.80–2.02% compared to accuracy-oriented methods across different generative models.
This paper proposes an end-to-end decision-focused learning framework for sparse tangent portfolio optimization that replaces discrete asset selection with a smooth top-k operator, enabling gradient flow through prediction and optimization to directly maximize Sharpe ratio.
This paper introduces a decision-focused learning approach for survival analysis that aligns predictive models with downstream allocation decisions, using NDCG optimization. Applied to US heart transplant data, it improves ranking performance by 50-100%, potentially yielding thousands of additional life-years annually.
This paper identifies 'staleness amplification' in bilevel optimization under delayed feedback and proposes IGT-OMD, which uses Implicit Gradient Transport to achieve sublinear regret and improve decision loss on benchmarks like Warcraft shortest-path and LQR.