Proceedings of machine learning research最新文献_第2页

Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation. 用于体育锻炼数据估算的时空多尺度稀疏自我关注。

Proceedings of machine learning research Pub Date : 2024-06-01

Hui Wei, Maxwell A Xu, Colin Samplawski, James M Rehg, Santosh Kumar, Benjamin M Marlin

引用次数: 0

Online Calibrated and Conformal Prediction Improves Bayesian Optimization. 在线校准和适形预测改进了贝叶斯优化。

Proceedings of machine learning research Pub Date : 2024-05-01

Shachi Deshpande, Charles Marx, Volodymyr Kuleshov

引用次数: 0

Adaptive Discretization for Event PredicTion (ADEPT). 事件预测自适应离散化（ADEPT）。

Proceedings of machine learning research Pub Date : 2024-05-01

Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael Pencina, Matthew Engelhard

{"title":"Adaptive Discretization for Event PredicTion (ADEPT).","authors":"Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael Pencina, Matthew Engelhard","doi":"","DOIUrl":"","url":null,"abstract":"Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time intervals. By avoiding placing strong parametric assumptions on the event density, this approach tends to improve prediction performance, particularly when data are plentiful. However, in clinical settings with limited available data, it is often preferable to judiciously partition the event time space into a limited number of intervals well suited to the prediction task at hand. In this work, we develop Adaptive Discretization for Event PredicTion (ADEPT) to learn from data a set of cut points defining such a partition. We show that in two simulated datasets, we are able to recover intervals that match the underlying generative model. We then demonstrate improved prediction performance on three real-world observational datasets, including a large, newly harmonized stroke risk prediction dataset. Finally, we argue that our approach facilitates clinical decision-making by suggesting time intervals that are most appropriate for each task, in the sense that they facilitate more accurate risk prediction.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"1351-1359"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11078624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900566","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

Proceedings of machine learning research Pub Date : 2024-05-01

Zongyu Dai, Emily Getzen, Qi Long

{"title":"SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data.","authors":"Zongyu Dai, Emily Getzen, Qi Long","doi":"","DOIUrl":"","url":null,"abstract":"Missing values are prevalent in temporal electronic health records (EHR) data and are known to complicate data analysis and lead to biased results. The current state-of-the-art (SOTA) models for imputing missing values in EHR primarily leverage correlations across time points and across features, which perform well when data have strong correlation across time points, such as in ICU data where high-frequency time series data are collected. However, this is often insufficient for temporal EHR data from non-ICU settings (e.g., outpatient visits for primary care or specialty care), where data are collected at substantially sparser time points, resulting in much weaker correlation across time points. To address this methodological gap, we propose the Similarity-Aware Diffusion Model-Based Imputation (SADI), a novel imputation method that leverages the diffusion model and utilizes information across dependent variables. We apply SADI to impute incomplete temporal EHR data and propose a similarity-aware denoising function, which includes a self-attention mechanism to model the correlations between time points, features, and similar patients. To the best of our knowledge, this is the first time that the information of similar patients is directly used to construct imputation for incomplete temporal EHR data. Our extensive experiments on two datasets, the Critical Path For Alzheimer's Disease (CPAD) data and the PhysioNet Challenge 2012 data, show that SADI outperforms the current SOTA under various missing data mechanisms, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"4195-4203"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11391213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302980","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

Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods. 在线双级优化：在线梯度交替法的遗憾分析

Proceedings of machine learning research Pub Date : 2024-05-01

Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou, Li Shen, Laura Balzano

引用次数: 0

On the Generalization Ability of Unsupervised Pretraining. 论无监督预训练的泛化能力

Proceedings of machine learning research Pub Date : 2024-05-01

Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi

{"title":"On the Generalization Ability of Unsupervised Pretraining.","authors":"Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi","doi":"","DOIUrl":"","url":null,"abstract":"Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset affects the generalization of the fine-tuned model is lacking. Existing theoretical research does not adequately account for the heterogeneity of the distribution and tasks in pre-training and fine-tuning stage. To bridge this gap, this paper introduces a novel theoretical framework that illuminates the critical factor influencing the transferability of knowledge acquired during unsupervised pre-training to the subsequent fine-tuning phase, ultimately affecting the generalization capabilities of the fine-tuned model on downstream tasks. We apply our theoretical framework to analyze generalization bound of two distinct scenarios: Context Encoder pre-training with deep neural networks and Masked Autoencoder pre-training with deep transformers, followed by fine-tuning on a binary classification task. Finally, inspired by our findings, we propose a novel regularization method during pre-training to further enhances the generalization of fine-tuned model. Overall, our results contribute to a better understanding of unsupervised pre-training and fine-tuning paradigm, and can shed light on the design of more effective pre-training algorithms.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"4519-4527"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482673","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

Online learning in bandits with predicted context. 有预测背景的匪帮在线学习

Proceedings of machine learning research Pub Date : 2024-05-01

Yongyi Guo, Ziping Xu, Susan Murphy

引用次数: 0

Optimal Sparse Survival Trees. 最优稀疏生存树

Proceedings of machine learning research Pub Date : 2024-05-01

Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin

引用次数: 0

Fusing Individualized Treatment Rules Using Secondary Outcomes. 利用次要结果融合个性化治疗规则。

Proceedings of machine learning research Pub Date : 2024-05-01

Daiqi Gao, Yuanjia Wang, Donglin Zeng

{"title":"Fusing Individualized Treatment Rules Using Secondary Outcomes.","authors":"Daiqi Gao, Yuanjia Wang, Donglin Zeng","doi":"","DOIUrl":"","url":null,"abstract":"An individualized treatment rule (ITR) is a decision rule that recommends treatments for patients based on their individual feature variables. In many practices, the ideal ITR for the primary outcome is also expected to cause minimal harm to other secondary outcomes. Therefore, our objective is to learn an ITR that not only maximizes the value function for the primary outcome, but also approximates the optimal rule for the secondary outcomes as closely as possible. To achieve this goal, we introduce a fusion penalty to encourage the ITRs based on different outcomes to yield similar recommendations. Two algorithms are proposed to estimate the ITR using surrogate loss functions. We prove that the agreement rate between the estimated ITR of the primary outcome and the optimal ITRs of the secondary outcomes converges to the true agreement rate faster than if the secondary outcomes are not taken into consideration. Furthermore, we derive the non-asymptotic properties of the value function and misclassification rate for the proposed method. Finally, simulation studies and a real data example are used to demonstrate the finite-sample performance of the proposed method.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"712-720"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382691","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

Simple and Scalable Algorithms for Cluster-Aware Precision Medicine. 集群感知精准医学的简单可扩展算法。

Proceedings of machine learning research Pub Date : 2024-05-01

Amanda M Buch, Conor Liston, Logan Grosenick

{"title":"Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.","authors":"Amanda M Buch, Conor Liston, Logan Grosenick","doi":"","DOIUrl":"","url":null,"abstract":"AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable approach for cluster-aware embedding that combines latent factor methods with a convex clustering penalty in a modular way. Our novel approach overcomes the complexity and limitations of current joint embedding and clustering methods and enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through numerical experiments and real-world examples, we demonstrate that our approach outperforms fourteen clustering methods on highly underdetermined problems (e.g., with limited sample size) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, yields improved model selection if they do, and yields interpretable hierarchically clustered embedding dendrograms. Thus, our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data and enables scalable and interpretable biomarkers for precision medicine.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"136-144"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11251711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629518","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