Zobrazeno 1 - 10
of 897
pro vyhledávání: '"Wells, Martin A."'
Large generative models (LMs) are increasingly being considered for high-stakes decision-making. This work considers how such models compare to humans and predictive AI models on a specific case of recidivism prediction. We combine three datasets --
Externí odkaz:
http://arxiv.org/abs/2410.15471
Autor:
Candelori, Luca, Abanov, Alexander G., Berger, Jeffrey, Hogan, Cameron J., Kirakosyan, Vahagn, Musaelian, Kharen, Samson, Ryan, Smith, James E. T., Villani, Dario, Wells, Martin T., Xu, Mengjia
We propose a new data representation method based on Quantum Cognition Machine Learning and apply it to manifold learning, specifically to the estimation of intrinsic dimension of data sets. The idea is to learn a representation of each data point as
Externí odkaz:
http://arxiv.org/abs/2409.12805
Autor:
Ramdas, Tejas, Wells, Martin T.
In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predi
Externí odkaz:
http://arxiv.org/abs/2409.05192
In this article, we develop nonparametric inference methods for comparing survival data across two samples, which are beneficial for clinical trials of novel cancer therapies where long-term survival is a critical outcome. These therapies, including
Externí odkaz:
http://arxiv.org/abs/2409.02209
Mediation analyses allow researchers to quantify the effect of an exposure variable on an outcome variable through a mediator variable. If a binary mediator variable is misclassified, the resulting analysis can be severely biased. Misclassification i
Externí odkaz:
http://arxiv.org/abs/2407.06970
Pretrial risk assessment tools are used in jurisdictions across the country to assess the likelihood of "pretrial failure," the event where defendants either fail to appear for court or reoffend. Judicial officers, in turn, use these assessments to d
Externí odkaz:
http://arxiv.org/abs/2309.08599
Sparse regression has emerged as a popular technique for learning dynamical systems from temporal data, beginning with the SINDy (Sparse Identification of Nonlinear Dynamics) framework proposed by arXiv:1509.03580. Quantifying the uncertainty inheren
Externí odkaz:
http://arxiv.org/abs/2308.09166
In biomedical and public health association studies, binary outcome variables may be subject to misclassification, resulting in substantial bias in effect estimates. The feasibility of addressing binary outcome misclassification in regression models
Externí odkaz:
http://arxiv.org/abs/2303.10215
Autor:
Tai, Yi-Cheng, Wang, Weijing, Wells, Martin T., U., National Yang Ming Chiao Tung, U, Cornell
We consider a Kendall's tau measure between a binary group indicator and the continuous variable under investigation to develop a thorough two-sample comparison procedure. The measure serves as a useful alternative to the hazard ratio whose applicabi
Externí odkaz:
http://arxiv.org/abs/2207.14445
We present an approach to clustering time series data using a model-based generalization of the K-Means algorithm which we call K-Models. We prove the convergence of this general algorithm and relate it to the hard-EM algorithm for mixture modeling.
Externí odkaz:
http://arxiv.org/abs/2207.00039