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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.

169,341 papers · 148 categories

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4 results for “DRP”

Ordinality in Discrete-level Question Difficulty Estimation: Introducing Balanced DRPS and OrderedLogitNNcs.LG

This study introduces balanced DRPS and OrderedLogitNN for better QDE of discrete-level questions.

problem Lack of ordinal regression methods and fair evaluation metrics for discrete-level QDE.

method Introduces balanced DRPS and OrderedLogitNN, fine-tunes BERT on RACE++ and ARC datasets.

result OrderedLogitNN outperforms other models on complex QDE tasks.

Question Difficulty Estimation Ordinal Regression Discrete-Level Balanced DRPS OrderedLogitNN

2025-07-01abs ↗pdf ↗

Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressivecs.LG

A new online local metric learning framework reduces overfitting and scales with data dimensions.

problem Global metrics fail to capture the complexity of real-world datasets.

method Proposes an online multiple metric learning framework with a global and local component.

result Reduces overfitting and scales with data dimensions.

metric learning local online learning overfitting scalability DRP

2018-04-05abs ↗pdf ↗

Towards Differential Relational Privacy and its use in Question Answeringcs.LG

Introduces Relational Privacy (RP) to control relation memorization in question answering models.

problem Relation memorization in question answering models can lead to privacy issues.

method Formalizes Relational Privacy (RP) and Differential Relational Privacy (DrP), providing bounds on relation memorization.

result DrP allows effective learning of general properties of underlying concepts while preventing relation memorization.

Relational Privacy Differential Relational Privacy Question Answering Relation Memorization Privacy

2022-03-30abs ↗pdf ↗

Distributional Robust Portfolio Construction based on Investor Aversionmath.OC

A new portfolio model considers investor aversion to loss and risk.

problem Constructing a robust portfolio under uncertain asset returns and investor aversion.

method Distributional robust optimization (DRP) with a Wasserstein ball centered on empirical distribution, mixed-integer quadratic programming, and hybrid algorithm.

result Empirical testing shows superior performance in asset allocation compared to common strategies.

portfolio optimization distributional robustness behavioral finance aversion to loss Wasserstein ball

2022-03-26abs ↗pdf ↗