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Conformal prediction using conditional histograms 21

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Conformal prediction using conditional histograms

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Matteo Sesia Yaniv Romano Department of Data Sciences and Operations University of Southern California Departments of Electrical and Computer Engineering and of Computer Science Technion Israel

ISBN: (纸本)9781713845393

This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data. Leveraging black-box machine learning algorithms to estimate the conditional distribution of the outcome using histograms, it translates their output into the shortest prediction intervals with approximate conditional coverage. The resulting prediction intervals provably have marginal coverage in finite samples, while asymptotically achieving conditional coverage and optimal length if the black-box model is consistent. Numerical experiments with simulated and real data demonstrate improved performance compared to state-of-the-art alternatives, including conformalized quantile regression and other distributional conformal prediction approaches.

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Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer

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arXiv 2023年

作者： Goyal, Manu Tafe, Laura J. Feng, James X. Muller, Kristen E. Hondelink, Liesbeth Bentz, Jessica L. Hassanpour, Saeed Department of Biomedical Data Science Dartmouth College HanoverNH United States Department of Pathology and Laboratory Medicine Dartmouth-Health LebanonNH United States Geisel School of Medicine Dartmouth College HanoverNH United States Department of Pathology and Laboratory Medicine Dartmouth-Health Lebanon United States Department of Pathology Leiden University Medical Center Leiden Netherlands Departments of Biomedical Data Science Computer Science and Epidemiology Dartmouth College HanoverNH United States

Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2.8% in women. Precise histologic evaluation and molecular classification of endometrial cancer is important for effective patient management and determining the best treatment modalities. This study introduces EndoNet, which uses convolutional neural networks for extracting histologic features and a vision transformer for aggregating these features and classifying slides based on their visual characteristics into high- and low- grade. The model was trained on 929 digitized hematoxylin and eosin-stained whole-slide images of endometrial cancer from hysterectomy cases at Dartmouth-Health. It classifies these slides into low-grade (Endometroid Grades 1 and 2) and high-grade (endometroid carcinoma FIGO grade 3, uterine serous carcinoma, carcinosarcoma) categories. EndoNet was evaluated on an internal test set of 110 patients and an external test set of 100 patients from the public TCGA database. The model achieved a weighted average F1-score of 0.91 (95% CI: 0.86–0.95) and an AUC of 0.95 (95% CI: 0.89–0.99) on the internal test, and 0.86 (95% CI: 0.80–0.94) for F1-score and 0.86 (95% CI: 0.75–0.93) for AUC on the external test. Pending further validation, EndoNet has the potential to support pathologists without the need of manual annotations in classifying the grades of gynecologic pathology tumors. © 2023, CC BY.

关键词： Diseases

Topological data Analysis to Engineer Features from Audio Signals for Depression Detection 19

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Topological Data Analysis to Engineer Features from Audio Si...

19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Tlachac, M.L. Sargent, Adam Toto, Ermal Paffenroth, Randy Rundensteiner, Elke Worcester Polytechnic Institute Data Science Department WorcesterMA United States Worcester Polytechnic Institute Computer Science and Mathematics Departments WorcesterMA United States

ISBN: (纸本)9781728184708

Topological data Analysis (TDA) can be used to extract features from raw signal data. These features, in the form of Betti curves, can be leveraged by machine learning models. Betti curves have been shown to mitigate bias from individual differences when classifying signals. One of the major challenges in detecting depression from audio clips is the variance of audio expression between participants. Thus, we hypothesize that Betti curves could help mitigate this audio expression variance. In this research, we are the first to construct Betti curves from audio signals. We then leverage these Betti curves to detect depression with audio clips from open-ended clinical interviews and scripted crowd-sourced recordings. For both datasets, machine learning models built on Betti curves achieve statistically significantly higher F1, AUC, and Accuracy scores than the same models built on only state-of-the-art audio engineered features. The AUC metric improvement was 0.054 for the clinical interviews and 0.066 for the crowd-sourced audio. Thus, we demonstrate TDA can be useful in screening for depression from audio signals. © 2020 IEEE.

关键词： Topology

Statistical learning methods for neuroimaging data analysis with applications

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arXiv 2022年

作者： Zhu, Hongtu Li, Tengfei Zhao, Bingxin Departments of Biostatistics Statistics Genetics and Computer Science and Biomedical Research Imaging Center University of North Carolina Chapel Hill United States Departments of Radiology Biomedical Research Imaging Center University of North Carolina Chapel Hill United States Department of Statistics and Data Science University of Pennsylvania United States

The aim of this paper is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate the four common themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four common themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges. © 2022, CC BY-SA.

关键词： data handling

Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature

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arXiv 2023年

作者： Deo, Yash Dou, Haoran Ravikumar, Nishant Frangi, Alejandro F. Lassila, Toni School of Computing and School of Medicine University of Leeds Leeds United Kingdom Leeds United Kingdom Alan Turing Institute London United Kingdom Electrical Engineering and Cardiovascular Sciences Departments Ku Leuven Leuven Belgium Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom

The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of the CoW is paramount to advance research on cerebrovascular diseases and refine clinical interventions. However, comprehensive investigation of less prevalent CoW variations remains challenging because of the dominance of a few commonly occurring configurations. We propose a novel generative approach utilising a conditional latent diffusion model with shape and anatomical guidance to generate realistic 3D CoW segmentations, including different phenotypical variations. Our conditional latent diffusion model incorporates shape guidance to better preserve vessel continuity and demonstrates superior performance when compared to alternative generative models, including conditional variants of 3D GAN and 3D VAE. We observed that our model generated CoW variants that are more realistic and demonstrate higher visual fidelity than competing approaches with an FID score 53% better than the best-performing GAN-based model. © 2023, CC BY.

关键词： Diffusion

Conformal prediction using conditional histograms

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arXiv 2021年

作者： Sesia, Matteo Romano, Yaniv Department of Data Sciences and Operations University of Southern California United States Departments of Electrical and of Computer Engineering and of Computer Science Technion Israel

关键词： Sampling

DP2-Pub: Differentially Private High-Dimensional data Publication with Invariant Post Randomization

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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

Minimum Φ-distance estimators for finite mixing measures

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arXiv 2023年

作者： Wei, Yun Mukherjee, Sayan Nguyen, XuanLong Department of Statistical Science Duke University NC United States Departments of Mathematics Computer Science Biostatistics & Bioinformatics Duke University NC United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Leipzig Germany Department of Statistics University of Michigan Ann ArborMI United States

Finite mixture models have long been used across a variety of fields in engineering and sciences. Recently there has been a great deal of interest in quantifying the convergence behavior of the mixing measure, a fundamental object that encapsulates all unknown parameters in a mixture distribution. In this paper we propose a general framework for estimating the mixing measure arising in finite mixture models, which we term minimum Φ-distance estimators. We establish a general theory for the minimum Φ-distance estimator, where sharp probability bounds are obtained on the estimation error for the mixing measures in terms of the suprema of the associated empirical processes for a suitably chosen function class Φ. Our framework includes several existing and seemingly distinct estimation methods as special cases but also motivates new estimators. For instance, it extends the minimum Kolmogorov-Smirnov distance estimator to the multivariate setting, and it extends the method of moments to cover a broader family of probability kernels beyond the Gaussian. Moreover, it also includes methods that are applicable to complex (e.g., non-Euclidean) observation domains, using tools from reproducing kernel Hilbert spaces. It will be shown that under general conditions the methods achieve optimal rates of estimation under Wasserstein metrics in either minimax or pointwise sense of convergence;the latter case can be achieved when no upper bound on the finite number of components is *** Codes Primary 62H30, 62C20, Secondary 62F12 © 2023, CC BY-NC-ND.

关键词： Method of moments

Bayesian multinomial logistic normal models through marginally latent matrix-T processes

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2022年第1期23卷 255-296页

作者： Justin D. Silverman Kimberly Roche Zachary C. Holmes Lawrence A. David Sayan Mukherjee College of Information Science and Technology Department of Statistics and Institute for Computational and Data Science Penn State University University Park PA Program in Computational Biology and Bioinformatics Duke University Durham NC Department of Molecular Genetics and Microbiology Duke University Durham NC Department of Molecular Genetics and Microbiology and Center for Genomic and Computational Biology Duke University Durham NC Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to small datasets due to the non-conjugacy of the multinomial and logistic-normal distributions. Motivated by the need to develop efficient inference for Bayesian MLN models, we develop two key ideas. First, we develop the class of Marginally Latent Matrix-T Process (Marginally LTP) models. We demonstrate that many popular MLN models, including those with latent linear, non-linear, and dynamic linear structure are special cases of this class. Second, we develop an efficient inference scheme for Marginally LTP models with specific accelerations for the MLN subclass. Through application to MLN models, we demonstrate that our inference scheme are both highly accurate and often 4-5 orders of magnitude faster than MCMC.

关键词： Bayesian statistics multivariate analysis count data microbiome gene expression