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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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3 results for “LambdaMART”

Learning to rank for uplift modelingcs.LG

A new method for uplift modeling using learning-to-rank techniques.

problem Improving customer targeting in marketing and retention.

method Unified formalization of uplift measures, learning-to-rank with PCG metric, LambdaMART optimization.

result Improved results compared to standard learning-to-rank metrics and state-of-the-art uplift modeling.

uplift modeling learning-to-rank LambdaMART promoted cumulative gain customer targeting

2020-02-14abs ↗pdf ↗

A Fast Sampling Gradient Tree Boosting Frameworkcs.LG

As an adaptive, interpretable, robust, and accurate meta-algorithm for arbitrary differentiable loss functions, gradient tree boosting is one of the most popular machine learning techniques, though the computational expensiveness severely limits its usage. Stochastic gradient boosting could be adopted to accelerates gr…

2019-11-20abs ↗pdf ↗

Transductive Conformal Inference for Full Rankingcs.LG

Method quantifies uncertainty in full ranking with new CP approach.

problem Uncertainty quantification in full ranking with unknown ground truth.

method Transductive Conformal Prediction (CP) method to construct distribution-free bounds.

result Valid prediction sets and false coverage proportion control for full ranking.

Conformal Prediction Ranking Uncertainty Quantification Transductive False Coverage

2025-01-20abs ↗pdf ↗