Proceedings of machine learning research最新文献_第6页

Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search. Py-Tetrad 和 RPy-Tetrad：为 Tetrad 因果搜索提供 R 支持的新 Python 接口。

Proceedings of machine learning research Pub Date : 2023-08-01

Joseph D Ramsey, Bryan Andrews

引用次数: 0

Fair Survival Time Prediction via Mutual Information Minimization. 当多则少时：加入额外的数据集可能会引入虚假相关性，从而影响性能。

Proceedings of machine learning research Pub Date : 2023-08-01

Hyungrok Do, Yuxin Chang, Yoon Sang Cho, Padhraic Smyth, Judy Zhong

引用次数: 0

Typed Markers and Context for Clinical Temporal Relation Extraction. 用于临床时空关系提取的类型标记和上下文。

Proceedings of machine learning research Pub Date : 2023-08-01

Cheng Cheng, Jeremy C Weiss

引用次数: 0

EASL: A Framework for Designing, Implementing, and Evaluating ML Solutions in Clinical Healthcare Settings. EASL：在临床医疗环境中设计、实施和评估 ML 解决方案的框架。

Proceedings of machine learning research Pub Date : 2023-08-01

Eric Prince, Todd C Hankinson, Carsten Görg

{"title":"EASL: A Framework for Designing, Implementing, and Evaluating ML Solutions in Clinical Healthcare Settings.","authors":"Eric Prince, Todd C Hankinson, Carsten Görg","doi":"","DOIUrl":"","url":null,"abstract":"We introduce the Explainable Analytical Systems Lab (EASL) framework, an end-to-end solution designed to facilitate the development, implementation, and evaluation of clinical machine learning (ML) tools. EASL is highly versatile and applicable to a variety of contexts and includes resources for data management, ML model development, visualization and user interface development, service hosting, and usage analytics. To demonstrate its practical applications, we present the EASL framework in the context of a case study: designing and evaluating a deep learning classifier to predict diagnoses from medical imaging. The framework is composed of three modules, each with their own set of resources. The Workbench module stores data and develops initial ML models, the Canvas module contains a medical imaging viewer and web development framework, and the Studio module hosts the ML model and provides web analytics and support for conducting user studies. EASL encourages model developers to take a holistic view by integrating the model development, implementation, and evaluation into one framework, and thus ensures that models are both effective and reliable when used in a clinical setting. EASL contributes to our understanding of machine learning applied to healthcare by providing a comprehensive framework that makes it easier to develop and evaluate ML tools within a clinical setting.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"219 ","pages":"612-630"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11235083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581781","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

Causal Inference With Outcome-Dependent Missingness And Self-Censoring. 因果推断与结果相关的缺失和自我审查

Proceedings of machine learning research Pub Date : 2023-08-01

Jacob M Chen, Daniel Malinsky, Rohit Bhattacharya

{"title":"Causal Inference With Outcome-Dependent Missingness And Self-Censoring.","authors":"Jacob M Chen, Daniel Malinsky, Rohit Bhattacharya","doi":"","DOIUrl":"","url":null,"abstract":"We consider missingness in the context of causal inference when the outcome of interest may be missing. If the outcome directly affects its own missingness status, i.e., it is \"self-censoring\", this may lead to severely biased causal effect estimates. Miao et al. [2015] proposed the shadow variable method to correct for bias due to self-censoring; however, verifying the required model assumptions can be difficult. Here, we propose a test based on a randomized incentive variable offered to encourage reporting of the outcome that can be used to verify identification assumptions that are sufficient to correct for both self-censoring and confounding bias. Concretely, the test confirms whether a given set of pre-treatment covariates is sufficient to block all backdoor paths between the treatment and outcome as well as all paths between the treatment and missingness indicator after conditioning on the outcome. We show that under these conditions, the causal effect is identified by using the treatment as a shadow variable, and it leads to an intuitive inverse probability weighting estimator that uses a product of the treatment and response weights. We evaluate the efficacy of our test and downstream estimator via simulations.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"216 ","pages":"358-368"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627020","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

Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions. 评估上下文推理误差和部分可观察性对用于及时适应性干预的 RL 方法的影响。

Proceedings of machine learning research Pub Date : 2023-08-01

Karine Karine, Predrag Klasnja, Susan A Murphy, Benjamin M Marlin

引用次数: 0

Half-Hop: A graph upsampling approach for slowing down message passing 半跳：一种降低消息传递速度的图形上采样方法

Proceedings of machine learning research Pub Date : 2023-07-01 DOI: 10.48550/arXiv.2308.09198

Mehdi Azabou, Venkataraman Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, M. Vaĺko, Petar Velickovic, Eva L. Dyer

{"title":"Half-Hop: A graph upsampling approach for slowing down message passing","authors":"Mehdi Azabou, Venkataraman Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, M. Vaĺko, Petar Velickovic, Eva L. Dyer","doi":"10.48550/arXiv.2308.09198","DOIUrl":"https://doi.org/10.48550/arXiv.2308.09198","url":null,"abstract":"Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this work, we introduce a simple yet general framework for improving learning in message passing neural networks. Our approach essentially upsamples edges in the original graph by adding \"slow nodes\" at each edge that can mediate communication between a source and a target node. Our method only modifies the input graph, making it plug-and-play and easy to use with existing models. To understand the benefits of slowing down message passing, we provide theoretical and empirical analyses. We report results on several supervised and self-supervised benchmarks, and show improvements across the board, notably in heterophilic conditions where adjacent nodes are more likely to have different labels. Finally, we show how our approach can be used to generate augmentations for self-supervised learning, where slow nodes are randomly introduced into different edges in the graph to generate multi-scale views with variable path lengths.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"202 1","pages":"1341-1360"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45894268","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: 1

A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging. 用于加速多线圈磁共振成像的条件归一化流程

Proceedings of machine learning research Pub Date : 2023-07-01

Jeffrey Wen, Rizwan Ahmad, Philip Schniter

引用次数: 0

Radiology Reports Improve Visual Representations Learned from Radiographs. 放射学报告改进了从射线照片中学到的可视化表达。

Proceedings of machine learning research Pub Date : 2023-07-01

Haoxu Huang, Samyak Rawlekar, Sumit Chopra, Cem M Deniz

{"title":"Radiology Reports Improve Visual Representations Learned from Radiographs.","authors":"Haoxu Huang, Samyak Rawlekar, Sumit Chopra, Cem M Deniz","doi":"","DOIUrl":"","url":null,"abstract":"Although human's ability to visually understand the structure of the World plays a crucial role in perceiving the World and making appropriate decisions, human perception does not solely rely on vision but amalgamates the information from acoustic, verbal, and visual stimuli. An active area of research has been revolving around designing an efficient framework that adapts to multiple modalities and ideally improves the performance of existing tasks. While numerous frameworks have proved effective on natural datasets like ImageNet, a limited number of studies have been carried out in the biomedical domain. In this work, we extend the available frameworks for natural data to biomedical data by leveraging the abundant, unstructured multi-modal data available as radiology images and reports. We attempt to answer the question, \"For multi-modal learning, self-supervised learning and joint learning using both learning strategies, which one improves the visual representation for downstream chest radiographs classification tasks the most?\". Our experiments indicated that in limited labeled data settings with 1% and 10% labeled data, the joint learning with multi-modal and self-supervised models outperforms self-supervised learning and is at par with multi-modal learning. Additionally, we found that multi-modal learning is generally more robust on out-of-distribution datasets. The code is publicly available online.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"227 ","pages":"1385-1405"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11234265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581782","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

Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. 具有潜在稀疏高斯过程的完全贝叶斯自动编码器。

Proceedings of machine learning research Pub Date : 2023-07-01

Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone

引用次数: 0