Search Results for author: David Xiao

Found 6 papers, 0 papers with code

No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases

no code implementations16 May 2024 Thomas Z. Li, Kaiwen Xu, Aravind Krishnan, Riqiang Gao, Michael N. Kammer, Sanja Antic, David Xiao, Michael Knight, Yency Martinez, Rafael Paez, Robert J. Lentz, Stephen Deppen, Eric L. Grogan, Thomas A. Lasko, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman

This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy.

Lung Cancer Diagnosis

Hedge Fund Index Rules and Construction

no code implementations23 Mar 2024 David Xiao

A Hedge Fund Index is very useful for tracking the performance of hedge fund investments, especially the timing of fund redemption.

Default Process Modeling and Credit Valuation Adjustment

no code implementations6 Sep 2023 David Xiao

This paper presents a convenient framework for modeling default process and pricing derivative securities involving credit risk.

Valuation of Equity Linked Securities with Guaranteed Return

no code implementations26 Jun 2023 David Xiao

The contract pays off a guaranteed amount plus a payment linked to the performance of a basket of equities averaged over a certain period.

Generic Forward Curve Dynamics for Commodity Derivatives

no code implementations22 Jun 2023 David Xiao

This article presents a generic framework for modeling the dynamics of forward curves in commodity market as commodity derivatives are typically traded by futures or forwards.

Sample Complexity Bounds on Differentially Private Learning via Communication Complexity

no code implementations25 Feb 2014 Vitaly Feldman, David Xiao

Our second contribution and the main tool is an equivalence between the sample complexity of (pure) differentially private learning of a concept class $C$ (or $SCDP(C)$) and the randomized one-way communication complexity of the evaluation problem for concepts from $C$.

PAC learning

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