LambdaRankIC derives closed-form lambda gradients for pairwise rank swaps to directly optimize Rank IC within the LambdaRank framework, outperforming regression and NDCG losses on simulated and real financial data.
Quantitative Finance , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.
citing papers explorer
-
LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction
LambdaRankIC derives closed-form lambda gradients for pairwise rank swaps to directly optimize Rank IC within the LambdaRank framework, outperforming regression and NDCG losses on simulated and real financial data.
-
Decision-Focused Learning via Tangent-Space Projection of Prediction Error
PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.