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

Continuous-Time Decision Transformer for Healthcare Applications. 用于医疗保健应用的连续时间决策变压器。

Proceedings of machine learning research Pub Date : 2023-04-01

Zhiyue Zhang, Hongyuan Mei, Yanxun Xu

{"title":"Continuous-Time Decision Transformer for Healthcare Applications.","authors":"Zhiyue Zhang, Hongyuan Mei, Yanxun Xu","doi":"","DOIUrl":"","url":null,"abstract":"Offline reinforcement learning (RL) is a promising approach for training intelligent medical agents to learn treatment policies and assist decision making in many healthcare applications, such as scheduling clinical visits and assigning dosages for patients with chronic conditions. In this paper, we investigate the potential usefulness of Decision Transformer (Chen et al., 2021)-a new offline RL paradigm-in medical domains where decision making in continuous time is desired. As Decision Transformer only handles discrete-time (or turn-based) sequential decision making scenarios, we generalize it to Continuous-Time Decision Transformer that not only considers the past clinical measurements and treatments but also the timings of previous visits, and learns to suggest the timings of future visits as well as the treatment plan at each visit. Extensive experiments on synthetic datasets and simulators motivated by real-world medical applications demonstrate that Continuous-Time Decision Transformer is able to outperform competitors and has clinical utility in terms of improving patients' health and prolonging their survival by learning high-performance policies from logged data generated using policies of different levels of quality.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"206 ","pages":"6245-6262"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10907982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140023967","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 Learning through Deliberate Undersampling. 通过故意减少取样进行因果学习

Proceedings of machine learning research Pub Date : 2023-04-01

Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis

引用次数: 0

Causal isotonic calibration for heterogeneous treatment effects 异质治疗效果的因果等渗校准

Proceedings of machine learning research Pub Date : 2023-02-27 DOI: 10.48550/arXiv.2302.14011

L. Laan, Ernesto Ulloa-P'erez, M. Carone, Alexander Luedtke

引用次数: 0

Learning Object-Centric Dynamic Modes from Video and Emerging Properties. 从视频和新兴属性中学习以对象为中心的动态模式。

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

Armand Comas Massague, Christian Fernandez-Lopez, Sandesh Ghimire, Haolin Li, Mario Sznaier, Octavia Camps

{"title":"Learning Object-Centric Dynamic Modes from Video and Emerging Properties.","authors":"Armand Comas Massague, Christian Fernandez-Lopez, Sandesh Ghimire, Haolin Li, Mario Sznaier, Octavia Camps","doi":"","DOIUrl":"","url":null,"abstract":"One of the long-term objectives of Machine Learning is to endow machines with the capacity of structuring and interpreting the world as we do. This is particularly challenging in scenes involving time series, such as video sequences, since seemingly different data can correspond to the same underlying dynamics. Recent approaches seek to decompose video sequences into their composing objects, attributes and dynamics in a self-supervised fashion, thus simplifying the task of learning suitable features that can be used to analyze each component. While existing methods can successfully disentangle dynamics from other components, there have been relatively few efforts in learning parsimonious representations of these underlying dynamics. In this paper, motivated by recent advances in non-linear identification, we propose a method to decompose a video into moving objects, their attributes and the dynamic modes of their trajectories. We model video dynamics as the output of a Koopman operator to be learned from the available data. In this context, the dynamic information contained in the scene is encapsulated in the eigenvalues and eigenvectors of the Koopman operator, providing an interpretable and parsimonious representation. We show that such decomposition can be used for instance to perform video analytics, predict future frames or generate synthetic video. We test our framework in a variety of datasets that encompass different dynamic scenarios, while illustrating the novel features that emerge from our dynamic modes decomposition: Video dynamics interpretation and user manipulation at test-time. We successfully forecast challenging object trajectories from pixels, achieving competitive performance while drawing useful insights.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"5 ","pages":"745-769"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981795","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

Generative Graph Dictionary Learning. 生成图字典学习。

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

Zhichen Zeng, Ruike Zhu, Yinglong Xia, Hanqing Zeng, Hanghang Tong

引用次数: 0

Fair admission risk prediction with proportional multicalibration. 比例多重校准的公平入场风险预测。

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

William G La Cava, Elle Lett, Guangya Wan

{"title":"Fair admission risk prediction with proportional multicalibration.","authors":"William G La Cava, Elle Lett, Guangya Wan","doi":"","DOIUrl":"","url":null,"abstract":"Fair calibration is a widely desirable fairness criteria in risk prediction contexts. One way to measure and achieve fair calibration is with multicalibration. Multicalibration constrains calibration error among flexibly-defined subpopulations while maintaining overall calibration. However, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it is possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose proportional multicalibration, a criteria that constrains the percent calibration error among groups and within prediction bins. We prove that satisfying proportional multicalibration bounds a model's multicalibration as well its differential calibration, a fairness criteria that directly measures how closely a model approximates sufficiency. Therefore, proportionally calibrated models limit the ability of decision makers to distinguish between model performance on different patient groups, which may make the models more trustworthy in practice. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"209 ","pages":"350-378"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417639/pdf/nihms-1917236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10008223","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

Directed Graphical Models and Causal Discovery for Zero-Inflated Data. 零膨胀数据的有向图模型和因果发现。

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

Shiqing Yu, Mathias Drton, Ali Shojaie

引用次数: 0

Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data. 技能，还是风格？胎儿超声眼动追踪数据的分类。

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

Clare Teng, Lior Drukker, Aris T Papageorghiou, J Alison Noble

引用次数: 0

Contrastive Representation Learning for Gaze Estimation. 用于凝视估计的对比表征学习

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

Swati Jindal, Roberto Manduchi

{"title":"Contrastive Representation Learning for Gaze Estimation.","authors":"Swati Jindal, Roberto Manduchi","doi":"","DOIUrl":"","url":null,"abstract":"Self-supervised learning (SSL) has become prevalent for learning representations in computer vision. Notably, SSL exploits contrastive learning to encourage visual representations to be invariant under various image transformations. The task of gaze estimation, on the other hand, demands not just invariance to various appearances but also equivariance to the geometric transformations. In this work, we propose a simple contrastive representation learning framework for gaze estimation, named Gaze Contrastive Learning (GazeCLR). GazeCLR exploits multi-view data to promote equivariance and relies on selected data augmentation techniques that do not alter gaze directions for invariance learning. Our experiments demonstrate the effectiveness of GazeCLR for several settings of the gaze estimation task. Particularly, our results show that GazeCLR improves the performance of cross-domain gaze estimation and yields as high as 17.2% relative improvement. Moreover, the GazeCLR framework is competitive with state-of-the-art representation learning methods for few-shot evaluation. The code and pre-trained models are available at https://github.com/jswati31/gazeclr.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"210 ","pages":"37-49"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270367/pdf/nihms-1862058.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9715239","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

Privacy-preserving patient clustering for personalized federated learning. 针对个性化联合学习的隐私保护患者聚类。

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

Ahmed Elhussein, Gamze Gürsoy

{"title":"Privacy-preserving patient clustering for personalized federated learning.","authors":"Ahmed Elhussein, Gamze Gürsoy","doi":"","DOIUrl":"","url":null,"abstract":"Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation if the data is non-identically independently distributed (non-IID). This is a problem in medical settings, where variations in the patient population contribute significantly to distribution differences across hospitals. Personalized FL addresses this issue by accounting for site-specific distribution differences. Clustered FL, a Personalized FL variant, was used to address this problem by clustering patients into groups across hospitals and training separate models on each group. However, privacy concerns remained as a challenge as the clustering process requires exchange of patient-level information. This was previously solved by forming clusters using aggregated data, which led to inaccurate groups and performance degradation. In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic technique, to securely calculate patient-level similarity scores across hospitals. We then evaluate PCBFL by training a federated mortality prediction model using 20 sites from the eICU dataset. We compare the performance gain from PCBFL against traditional and existing Clustered FL frameworks. Our results show that PCBFL successfully forms clinically meaningful cohorts of low, medium, and high-risk patients. PCBFL outperforms traditional and existing Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC improvement of 7.8%.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"219 ","pages":"150-166"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11376435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141965","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