Proceedings of machine learning research最新文献_第10页

Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning. 不同的审查和测试不足:临床机器学习中标签偏见的来源。

Proceedings of machine learning research Pub Date : 2022-08-01

Trenton Chang, Michael W Sjoding, Jenna Wiens

{"title":"Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.","authors":"Trenton Chang, Michael W Sjoding, Jenna Wiens","doi":"","DOIUrl":"","url":null,"abstract":"As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e., differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders are affected by clinical and resource considerations, testing may not be uniform in patient populations, giving rise to disparate censorship. Disparate censorship in patients of equivalent risk leads to undertesting in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"182 ","pages":"343-390"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162497/pdf/nihms-1868579.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9437970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survival Mixture Density Networks. 生存混合密度网络。

Proceedings of machine learning research Pub Date : 2022-08-01

Xintian Han, Mark Goldstein, Rajesh Ranganath

引用次数: 0

Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning 差异审查和测试不足：临床机器学习中标签偏见的来源

Proceedings of machine learning research Pub Date : 2022-08-01 DOI: 10.48550/arXiv.2208.01127

Trenton Chang, M. Sjoding, J. Wiens

{"title":"Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning","authors":"Trenton Chang, M. Sjoding, J. Wiens","doi":"10.48550/arXiv.2208.01127","DOIUrl":"https://doi.org/10.48550/arXiv.2208.01127","url":null,"abstract":"As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e., differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders are affected by clinical and resource considerations, testing may not be uniform in patient populations, giving rise to disparate censorship. Disparate censorship in patients of equivalent risk leads to undertesting in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"182 1","pages":"343-390"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49527996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Fair Generalized Linear Models with a Convex Penalty. 具有凹凸惩罚的公平广义线性模型

Proceedings of machine learning research Pub Date : 2022-07-01

Hyungrok Do, Preston Putzel, Axel Martin, Padhraic Smyth, Judy Zhong

引用次数: 0

FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation. FBNetGen:基于任务感知gnn的脑功能网络生成fMRI分析。

Proceedings of machine learning research Pub Date : 2022-07-01

Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang

{"title":"FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation.","authors":"Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang","doi":"","DOIUrl":"","url":null,"abstract":"Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep brain network generation. In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks. Along with the process, the key novel component is the graph generator which learns to transform raw time-series features into task-oriented brain networks. Our learnable graphs also provide unique interpretations by highlighting prediction-related brain regions. Comprehensive experiments on two datasets, i.e., the recently released and currently largest publicly available fMRI dataset Adolescent Brain Cognitive Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior effectiveness and interpretability of FBNETGEN. The implementation is available at https://github.com/Wayfear/FBNETGEN.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"172 ","pages":"618-637"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296778/pdf/nihms-1811216.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9718097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient and Communication Efficient Learning for Heterogeneous Federated Systems. 异构联盟系统的弹性和通信效率学习。

Proceedings of machine learning research Pub Date : 2022-07-01

Zhuangdi Zhu, Junyuan Hong, Steve Drew, Jiayu Zhou

{"title":"Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.","authors":"Zhuangdi Zhu, Junyuan Hong, Steve Drew, Jiayu Zhou","doi":"","DOIUrl":"","url":null,"abstract":"The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. However, the heterogeneity of edge network topologies and the uncertainty of wireless transmission are two major obstructions of FL's wide application in edge computing, leading to prohibitive convergence time and high communication cost. In this work, we propose an FL scheme to address both challenges simultaneously. Specifically, we enable edge devices to learn self-distilled neural networks that are readily prunable to arbitrary sizes, which capture the knowledge of the learning domain in a nested and progressive manner. Not only does our approach tackle system heterogeneity by serving edge devices with varying model architectures, but it also alleviates the issue of connection uncertainty by allowing transmitting part of the model parameters under faulty network connections, without wasting the contributing knowledge of the transmitted parameters. Extensive empirical studies show that under system heterogeneity and network instability, our approach demonstrates significant resilience and higher communication efficiency compared to the state-of-the-art.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"162 ","pages":"27504-27526"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097502/pdf/nihms-1888103.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9315231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi Resolution Analysis (MRA) for Approximate Self-Attention 近似自注意的多分辨率分析

Proceedings of machine learning research Pub Date : 2022-07-01 DOI: 10.48550/arXiv.2207.10284

Zhanpeng Zeng, Sourav Pal, Jeffery Kline, G. Fung, Vikas Singh

引用次数: 4

A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. 一种基于树状模型的平均方法，用于从异构数据源中估计个性化治疗效果。

Proceedings of machine learning research Pub Date : 2022-07-01

Xiaoqing Tan, Chung-Chou H Chang, Ling Zhou, Lu Tang

{"title":"A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources.","authors":"Xiaoqing Tan, Chung-Chou H Chang, Ling Zhou, Lu Tang","doi":"","DOIUrl":"","url":null,"abstract":"Accurately estimating personalized treatment effects within a study site (e.g., a hospital) has been challenging due to limited sample size. Furthermore, privacy considerations and lack of resources prevent a site from leveraging subject-level data from other sites. We propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects (CATE) at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data. To our best knowledge, there is no established model averaging approach for distributed data with a focus on improving the estimation of treatment effects. Specifically, under distributed data networks, our framework provides an interpretable tree-based ensemble of CATE estimators that joins models across study sites, while actively modeling the heterogeneity in data sources through site partitioning. The performance of this approach is demonstrated by a real-world study of the causal effects of oxygen therapy on hospital survival rate and backed up by comprehensive simulation results.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"162 ","pages":"21013-21036"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10711748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138814728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LIMO: Latent Inceptionism for Targeted Molecule Generation. LIMO:靶向分子生成的潜在初始论。

Proceedings of machine learning research Pub Date : 2022-07-01

Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K Gilson, Rose Yu

{"title":"LIMO: Latent Inceptionism for Targeted Molecule Generation.","authors":"Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K Gilson, Rose Yu","doi":"","DOIUrl":"","url":null,"abstract":"Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted K D (a measure of binding affinity) of 6 · 10-14 M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":" ","pages":"5777-5792"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9527083/pdf/nihms-1836710.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33485557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forward Operator Estimation in Generative Models with Kernel Transfer Operators. 使用核转移算子的生成模型中的前向算子估计。

Proceedings of machine learning research Pub Date : 2022-07-01

Zhichun Huang, Rudrasis Chakraborty, Vikas Singh

引用次数: 0