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On-device research index

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.

168,742 papers · 148 categories

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9 results for score-optimization

New algorithm learns Markov network structures efficiently.

problem Learning Markov network structures without chordality assumptions.
method Local penalized likelihood ratio tests and two-stage hill-climbing algorithm.
result PLRHC-BIC0.5_{0.5} algorithm compares favorably against state-of-the-art methods.

DDPMs are robust to noisy score estimates and achieve optimal convergence rates in Wasserstein-2 distance.

problem Evaluating the quality of DDPMs in Wasserstein distance with noisy score estimates.
method Established finite-sample guarantees in Wasserstein-2 distance for DDPMs, considering noisy score estimates.
result Optimal convergence rates in Wasserstein-2 distance for DDPMs, matching Gaussian case.

Study improves prediction of UK road accidents' severity using AI.

problem Improving prediction of UK road traffic accident severity.
method Combination of machine learning, econometric, and statistical methods on historical data.
result XGBoost model with RMSE of 0.176 and MAE of 0.087 outperforms naive forecasting.

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in th…

2019-06-26abs ↗pdf ↗

Optimizes risk assessment tools using mixed-integer programming.

problem Challenges in healthcare risk assessment due to label scarcity and asymmetric misclassification costs.
method Jointly optimizes scoring weights and category thresholds via mixed-integer programming (MIP).
result Prevents label-scarce category collapse and achieves more accurate risk categorization.

W-Flow generates images in one step, faster and better than multi-step methods.

problem Efficiently generating images from a simple reference distribution to a target data distribution.
method W-Flow uses Wasserstein gradient flows to transform the reference distribution to the target distribution in a single step, trained with Sinkhorn divergence.
result W-Flow achieves state-of-the-art results in ImageNet 256imes imes256 generation with improved mode coverage and faster sampling.

OptCS optimizes model selection after conformal inference, controlling FDR and power loss.

problem Challenges in model selection for conformal inference, especially when limited labeled data and many model choices are available.
method OptCS framework that allows valid statistical testing after flexible data-driven model optimization, using novel multiple testing procedures.
result Valid conformal p-values constructed despite substantial data reuse, maintaining FDR control.