Proceedings of machine learning research最新文献

Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes. 自我思考上下文学习增强新表位的T细胞受体生成。

Proceedings of machine learning research Pub Date : 2025-09-01

Pengfei Zhang, Sonal Sujit Prabhu, Gloria Grama, Seojin Bang, Heewook Lee

{"title":"Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes.","authors":"Pengfei Zhang, Sonal Sujit Prabhu, Gloria Grama, Seojin Bang, Heewook Lee","doi":"","DOIUrl":"","url":null,"abstract":"Computational design of T cell receptors (TCRs) that bind to epitopes holds the potential to revolutionize targeted immunotherapy. However, computational design of TCRs for novel epitopes is challenging due to the scarcity of training data, and the absence of known cognate TCRs for novel epitopes. In this study, we aim to generate high-quality cognate TCRs particularly for novel epitopes with no known cognate TCRs, a problem that remains under-explored in the field. We propose to incorporate in-context learning, successfully used with large language models to perform new generative tasks, to the task of TCR generation for novel epitopes. By providing cognate TCRs as additional context, we enhance the model's ability to generate high-quality TCRs for novel epitopes. We first unlock the power of in-context learning by training a model to generate new TCRs based on both a target epitope and a small set of its cognate TCRs, so-called in-context training (ICT). We then self-generate its own TCR contexts based on a target epitope, as novel epitopes lack known binding TCRs, and use it as an inference prompt, referred to as self-contemplation prompting (SCP). Our experiments first demonstrate that aligning training and inference distribution by ICT is critical for effectively leveraging context TCRs. Subsequently, we show that providing context TCRs significantly improves TCR generation for novel epitopes. Furthermore, we show TCR generation using SCP-synthesized context TCRs achieves performance comparable to, and sometimes surpassing, ground-truth context TCRs, especially when combined with refined prompt selection based on binding affinity and authenticity metrics. We assess the designed sequences' binding probability and sequence authenticity using seven diverse computational models.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"311 ","pages":"251-269"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791471","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

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning. ProtoECGNet：基于案例的可解释深度学习的多标签心电分类对比学习。

Proceedings of machine learning research Pub Date : 2025-08-01

Sahil Sethi, David Chen, Thomas Statchen, Michael C Burkhart, Nipun Bhandari, Bashar Ramadan, Brett Beaulieu-Jones

{"title":"ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.","authors":"Sahil Sethi, David Chen, Thomas Statchen, Michael C Burkhart, Nipun Bhandari, Bashar Ramadan, Brett Beaulieu-Jones","doi":"","DOIUrl":"","url":null,"abstract":"Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency maps may fail to reflect a model's true decision process. Prototype-based reasoning offers a more transparent alternative by grounding decisions in similarity to learned representations of real ECG segments-enabling faithful, case-based explanations. We introduce ProtoECGNet, a prototype-based deep learning model for interpretable, multi-label ECG classification. ProtoECGNet employs a structured, multi-branch architecture that reflects clinical interpretation workflows: it integrates a 1D CNN with global prototypes for rhythm classification, a 2D CNN with time-localized prototypes for morphology-based reasoning, and a 2D CNN with global prototypes for diffuse abnormalities. Each branch is trained with a prototype loss designed for multi-label learning, combining clustering, separation, diversity, and a novel contrastive loss that encourages appropriate separation between prototypes of unrelated classes while allowing clustering for frequently co-occurring diagnoses. We evaluate ProtoECGNet on all 71 labels from the PTB-XL dataset, demonstrating competitive performance relative to state-of-the-art black-box models while providing structured, case-based explanations. To assess prototype quality, we conduct a structured clinician review of the final model's projected prototypes, finding that they are rated as representative and clear. ProtoECGNet shows that prototype learning can be effectively scaled to complex, multi-label time-series classification, offering a practical path toward transparent and trustworthy deep learning models for clinical decision support.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"298 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12700622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758662","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

Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model. 利用嵌入式神经霍克斯过程模型平衡诊断轨迹建模的可解释性和灵活性。

Proceedings of machine learning research Pub Date : 2025-08-01

Yuankang Zhao, Matthew M Engelhard

{"title":"Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model.","authors":"Yuankang Zhao, Matthew M Engelhard","doi":"","DOIUrl":"","url":null,"abstract":"The Hawkes process (HP) is commonly used to model event sequences with self-reinforcing dynamics, including electronic health records (EHRs). Traditional HPs capture self-reinforcement via parametric impact functions that can be inspected to understand how each event modulates the intensity of others. Neural network-based HPs offer greater flexibility, resulting in improved fit and prediction performance, but at the cost of interpretability, which is often critical in healthcare. In this work, we aim to understand and improve upon this tradeoff. We propose a novel HP formulation in which impact functions are modeled by defining a flexible impact kernel, instantiated as a neural network, in event embedding space, which allows us to model large-scale event sequences with many event types. This approach is more flexible than traditional HPs yet more interpretable than other neural network approaches, and allows us to explicitly trade flexibility for interpretability by adding transformer encoder layers to further contextualize the event embeddings. Results show that our method accurately recovers impact functions in simulations, achieves competitive performance on MIMIC-IV procedure dataset, and gains clinically meaningful interpretation on Duke-EHR with children diagnosis dataset even without transformer layers. This suggests that our flexible impact kernel is often sufficient to capture self-reinforcing dynamics in EHRs and other data effectively, implying that interpretability can be maintained without loss of performance.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"298 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12646569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145643662","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

Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models. 使用大语言模型对治疗顽固性高血压的可计算表型进行迭代学习。

Proceedings of machine learning research Pub Date : 2025-08-01

Guilherme Seidyo Imai Aldeia, Daniel S Herman, William G La Cava

引用次数: 0

Stage-Aware Event-Based Modeling (SA-EBM) for Disease Progression. 基于阶段感知事件的疾病进展模型（SA-EBM）

Proceedings of machine learning research Pub Date : 2025-08-01

Hongtao Hao, Vivek Prabhakaran, Veena A Nair, Nagesh Adluru, Joseph L Austerweil

引用次数: 0

Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning. 借鉴未来：通过对比学习加强早期风险评估。

Proceedings of machine learning research Pub Date : 2025-08-01

Minghui Sun, Matthew M Engelhard, Benjamin A Goldstein

引用次数: 0

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach. 恢复校准对齐的大型语言模型：校准感知微调方法。

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

Jiancong Xiao, Bojian Hou, Zhanliang Wang, Ruochen Jin, Qi Long, Weijie J Su, Li Shen

{"title":"Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach.","authors":"Jiancong Xiao, Bojian Hou, Zhanliang Wang, Ruochen Jin, Qi Long, Weijie J Su, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"One of the key technologies for the success of Large Language Models (LLMs) is preference alignment. However, a notable side effect of preference alignment is poor calibration: while the pre-trained models are typically well-calibrated, LLMs tend to become poorly calibrated after alignment with human preferences. In this paper, we investigate why preference alignment affects calibration and how to address this issue. For the first question, we observe that the preference collapse issue in alignment undesirably generalizes to the calibration scenario, causing LLMs to exhibit overconfidence and poor calibration. To address this, we demonstrate the importance of fine-tuning with domain-specific knowledge to alleviate the overconfidence issue. To further analyze whether this affects the model's performance, we categorize models into two regimes: calibratable and non-calibratable, defined by bounds of Expected Calibration Error (ECE). In the calibratable regime, we propose a calibration-aware fine-tuning approach to achieve proper calibration without compromising LLMs' performance. However, as models are further fine-tuned for better performance, they enter the non-calibratable regime. For this case, we develop an EM-algorithm-based ECE regularization for the fine-tuning loss to maintain low calibration error. Extensive experiments validate the effectiveness of the proposed methods.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"267 ","pages":"68364-68390"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500745","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

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. “谁会经历大的模型衰减，为什么？”一种诊断异构性能漂移的分层框架。

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

Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C Hong, Jean Feng

{"title":"\"Who experiences large model decay and why?\" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift.","authors":"Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C Hong, Jean Feng","doi":"","DOIUrl":"","url":null,"abstract":"Machine learning (ML) models frequently experience performance degradation when deployed in new contexts. Such degradation is rarely uniform: some subgroups may suffer large performance decay while others may not. Understanding where and how large differences in performance arise is critical for designing targeted corrective actions that mitigate decay for the most affected subgroups while minimizing any unintended effects. Current approaches do not provide such detailed insight, as they either (i) explain how average performance shifts arise or (ii) identify adversely affected subgroups without insight into how this occurred. To this end, we introduce a Subgroup-scanning Hierarchical Inference Framework for performance drifT (SHIFT). SHIFT first asks \"Is there any subgroup with unacceptably large performance decay due to covariate/outcome shifts?\" (Where?) and, if so, dives deeper to ask \"Can we explain this using more detailed variable(subset)-specific shifts?\" (How?). In real-world experiments, we find that SHIFT identifies interpretable subgroups affected by performance decay, and suggests targeted actions that effectively mitigate the decay.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"267 ","pages":"55757-55787"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12747154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145866889","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

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models. 猛禽：利用预训练的2D基础模型的3D医疗卷的可扩展无训练嵌入。

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

Ulzee An, Moonseong Jeong, Simon A Lee, Aditya Gorla, Yuzhe Yang, Sriram Sankararaman

{"title":"Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models.","authors":"Ulzee An, Moonseong Jeong, Simon A Lee, Aditya Gorla, Yuzhe Yang, Sriram Sankararaman","doi":"","DOIUrl":"","url":null,"abstract":"Current challenges in developing foundational models for volumetric imaging data, such as magnetic resonance imaging (MRI), stem from the computational complexity of training state-of-the-art architectures in high dimensions and curating sufficiently large datasets of volumes. To address these challenges, we introduce Raptor (Random Planar Tensor Reduction), a train-free method for generating semantically rich embeddings for volumetric data. Raptor leverages a frozen 2D foundation model, pretrained on natural images, to extract visual tokens from individual cross-sections of medical volumes. These tokens are then spatially compressed using random projections, significantly reducing computational complexity while retaining semantic information. Extensive experiments on ten diverse medical volume tasks verify the superior performance of Raptor over state-of-the-art methods, including those pretrained exclusively on medical volumes (+3% SuPreM, +6% MISFM, +10% Merlin, +13% VoCo, and +14% SLIViT), while entirely bypassing the need for costly training. Our results highlight the effectiveness and versatility of Raptor as a foundation for advancing deep learning-based methods for medical volumes (code: github.com/sriramlab/raptor).","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"267 ","pages":"1462-1482"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12893380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146183762","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

NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders. 神经树：精神健康障碍的分层功能脑通路解码。

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

Jun-En Ding, Dongsheng Luo, Chenwei Wu, Feng Liu

{"title":"NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders.","authors":"Jun-En Ding, Dongsheng Luo, Chenwei Wu, Feng Liu","doi":"","DOIUrl":"","url":null,"abstract":"Mental disorders are among the most widespread diseases globally. Analyzing functional brain networks through functional magnetic resonance imaging (fMRI) is crucial for understanding mental disorder behaviors. Although existing fMRI-based graph neural networks (GNNs) have demonstrated significant potential in brain network feature extraction, they often fail to characterize complex relationships between brain regions and demographic information in mental disorders. To overcome these limitations, we propose a learnable NeuroTree framework that integrates a <math><mi>k</mi></math> -hop AGE-GCN with neural ordinary differential equations (ODEs) and contrastive masked functional connectivity (CMFC) to enhance similarities and dissimilarities of brain region distance. Furthermore, NeuroTree effectively decodes fMRI network features into tree structures, which improves the capture of high-order brain regional pathway features and enables the identification of hierarchical neural behavioral patterns essential for understanding disease-related brain subnetworks. Our empirical evaluations demonstrate that NeuroTree achieves state-of-the-art performance across two distinct mental disorder datasets. It provides valuable insights into age-related deterioration patterns, elucidating their underlying neural mechanisms. The code and datasets are available at https://github.com/Ding1119/NeuroTree.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"267 ","pages":"13845-13869"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13061348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647829","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