Proceedings of machine learning research最新文献_第7页

CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation. CACTI：利用复制屏蔽和上下文信息来改进表格数据输入。

Proceedings of machine learning research Pub Date : 2025-07-01

Aditya Gorla, Ryan Wang, Zhengtong Liu, Ulzee An, Sriram Sankararaman

引用次数: 0

Contrastive Patient-level Pretraining Enables Longitudinal and Multimodal Fusion for Lung Cancer Risk Prediction. 对比患者水平的预训练使纵向和多模式融合用于肺癌风险预测。

Proceedings of machine learning research Pub Date : 2025-07-01

Thomas Z Li, Lianrui Zuo, Yihao Liu, Aravind R Krishnan, Kim L Sandler, Thomas A Lasko, Fabien Maldonado, Bennett A Landman

{"title":"Contrastive Patient-level Pretraining Enables Longitudinal and Multimodal Fusion for Lung Cancer Risk Prediction.","authors":"Thomas Z Li, Lianrui Zuo, Yihao Liu, Aravind R Krishnan, Kim L Sandler, Thomas A Lasko, Fabien Maldonado, Bennett A Landman","doi":"","DOIUrl":"","url":null,"abstract":"Leveraging longitudinal and multimodal data is important for clinical predictive tasks. Contrastive language-image pretraining (CLIP) has been successful in learning multimodal representations by aligning paired images and captions, i.e. medical images and corresponding radiology report. However, in real clinical settings, the alignment of unpaired modalities, such as medical images and clinical notes collected at different times, is an open challenge, even though such data are ubiquitous in practice. This study conducts contrastive pretraining between longitudinal chest CTs and clinical variables on the patient level using a large public lung cancer screening dataset. Leveraging a time-distanced transformer to encode longitudinal imaging and an open-source text embedding to encode clinical variables, we optimize contrastive loss between the embedded modalities from same patient (positive pair) against those from different patients (negative pair). We find that finetuning the CLIP representation significantly improves prediction of lung cancer risk in two types of clinical populations (0.895 and 0.893 AUC) compared to conventional multimodal fusion (0.873 and 0.875 AUC) and single modality baselines. These results demonstrate how contrastive patient-level pretraining can enable longitudinal and multimodal fusion without additional training data. We released our code and pre-trained weights at https://github.com/MASILab/lung-cplp.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"301 ","pages":"1008-1020"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13197062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019212","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

Learning Survival Distributions with the Asymmetric Laplace Distribution. 用非对称拉普拉斯分布学习生存分布。

Proceedings of machine learning research Pub Date : 2025-07-01

Deming Sheng, Ricardo Henao

引用次数: 0

Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN. 基于FAMPNN的全原子蛋白质序列侧链调节与建模。

Proceedings of machine learning research Pub Date : 2025-07-01

Talal Widatalla, Richard W Shuai, Brian L Hie, Po-Ssu Huang

{"title":"Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.","authors":"Talal Widatalla, Richard W Shuai, Brian L Hie, Po-Ssu Huang","doi":"","DOIUrl":"","url":null,"abstract":"Leading deep learning-based methods for fixed-backbone protein sequence design do not model protein sidechain conformation during sequence generation despite the large role the three-dimensional arrangement of sidechain atoms play in protein conformation, stability, and overall protein function. Instead, these models implicitly reason about crucial sidechain interactions based on backbone geometry and known amino acid sequence labels. To address this, we present FAMPNN (Full-Atom MPNN), a sequence design method that explicitly models both sequence identity and sidechain conformation for each residue, where the per-token distribution of a residue's discrete amino acid identity and its continuous sidechain conformation are learned with a combined categorical cross-entropy and diffusion loss objective. We demonstrate that learning these distributions jointly is a highly synergistic task that both improves sequence recovery while achieving state-of-the-art sidechain packing. Furthermore, benefits from full-atom modeling generalize from sequence recovery to practical protein design applications, such as zero-shot prediction of experimental binding and stability measurements.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"267 ","pages":"66746-66771"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12646570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145643666","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

Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. 通过热狗调频免费核心马尔科夫链蒙特卡洛。

Proceedings of machine learning research Pub Date : 2025-07-01 Epub Date: 2025-07-21

Naitong Chen, Jonathan H Huggins, Trevor Campbell

引用次数: 0

Stream-level Flow Matching with Gaussian Processes. 高斯过程的流级流量匹配。

Proceedings of machine learning research Pub Date : 2025-07-01

Ganchao Wei, Li Ma

引用次数: 0

I²MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts. I2MoE：可解释的多模态交互感知混合专家。

Proceedings of machine learning research Pub Date : 2025-07-01

Jiayi Xin, Sukwon Yun, Jie Peng, Inyoung Choi, Jenna L Ballard, Tianlong Chen, Qi Long

{"title":"I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts.","authors":"Jiayi Xin, Sukwon Yun, Jie Peng, Inyoung Choi, Jenna L Ballard, Tianlong Chen, Qi Long","doi":"","DOIUrl":"","url":null,"abstract":"Modality fusion is a cornerstone of multimodal learning, enabling information integration from diverse data sources. However, vanilla fusion methods are limited by (1) inability to account for heterogeneous interactions between modalities and (2) lack of interpretability in uncovering the multimodal interactions inherent in the data. To this end, we propose I2MoE (Interpretable Multimodal Interaction-aware Mixture of Experts), an end-to-end MoE framework designed to enhance modality fusion by explicitly modeling diverse multimodal interactions, as well as providing interpretation on a local and global level. First, I2MoE utilizes different interaction experts with weakly supervised interaction losses to learn multimodal interactions in a data-driven way. Second, I2MoE deploys a reweighting model that assigns importance scores for the output of each interaction expert, which offers sample-level and dataset-level interpretation. Extensive evaluation of medical and general multimodal datasets shows that I2MoE is flexible enough to be combined with different fusion techniques, consistently improves task performance, and provides interpretation across various real-world scenarios. Code is available at https://github.com/Raina-Xin/I2MoE.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"267 ","pages":"68870-68888"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500701","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

Fast Calculation of Feature Contributions in Boosting Trees. 增强树中特征贡献的快速计算。

Proceedings of machine learning research Pub Date : 2025-07-01

Zhongli Jiang, Min Zhang, Dabao Zhang

引用次数: 0

Structure-informed Risk Minimization for Robust Ensemble Learning. 基于结构的鲁棒集成学习风险最小化。

Proceedings of machine learning research Pub Date : 2025-07-01

Fengchun Qiao, Yanlin Chen, Xi Peng

{"title":"Structure-informed Risk Minimization for Robust Ensemble Learning.","authors":"Fengchun Qiao, Yanlin Chen, Xi Peng","doi":"","DOIUrl":"","url":null,"abstract":"Ensemble learning is a powerful approach for improving generalization under distribution shifts, but its effectiveness heavily depends on how individual models are combined. Existing methods often optimize ensemble weights based on validation data, which may not represent unseen test distributions, leading to suboptimal performance in out-of-distribution (OoD) settings. Inspired by Distributionally Robust Optimization (DRO), we propose Structure-informed Risk Minimization (SRM), a principled framework that learns robust ensemble weights without access to test data. Unlike standard DRO, which defines uncertainty sets based on divergence metrics alone, SRM incorporates structural information of training distributions, ensuring that the uncertainty set aligns with plausible real-world shifts. This approach mitigates the over-pessimism of traditional worst-case optimization while maintaining robustness. We introduce a computationally efficient optimization algorithm with theoretical guarantees and demonstrate that SRM achieves superior OoD generalization compared to existing ensemble combination strategies across diverse benchmarks. Code is available at: https://github.com/deep-real/SRM.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"267 ","pages":"50130-50141"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824469","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

Learn Singularly Perturbed Solutions via Homotopy Dynamics. 通过同伦动力学学习奇摄动解。

Proceedings of machine learning research Pub Date : 2025-07-01

Chuqi Chen, Yahong Yang, Yang Xiang, Wenrui Hao

引用次数: 0