Transactions on machine learning research最新文献

Scale-Invariant, Robust Sparse PCA for Large Data via Differentiable Penalties. 基于可微惩罚的大数据尺度不变鲁棒稀疏PCA。

Transactions on machine learning research Pub Date : 2026-09-01

Manu Aggarwal, Vipul Periwal

{"title":"Scale-Invariant, Robust Sparse PCA for Large Data via Differentiable Penalties.","authors":"Manu Aggarwal, Vipul Periwal","doi":"","DOIUrl":"","url":null,"abstract":"Sparse PCA finds low-dimensional structure that loads on few features. Existing methods couple learning and feature selection by applying non-differentiable penalties that force retain-or-zero decisions during optimization. This eliminates features before the optimizer has established which ones matter, limits scalability to serial coordinate-update solvers, and fails when components share support. We introduce DROSS-PCA (Differentiable RObust Scalable Sparse PCA), which decouples learning and feature selection. It learns by optimizing a fully differentiable objective combining robust reconstruction, a smooth sparsity penalty, and an orthogonality term. It selects features post-hoc via cosine-preserving pruning. DROSS-PCA is GPU-accelerated, scaling sparse PCA to large data sets beyond the reach of existing methods. All loss terms are calibrated to be independent of data dimensionality and scale, giving penalty weights consistent meaning across data sets. On synthetic benchmarks, DROSS-PCA matches or outperforms established methods on support recovery. Under outlier contamination it substantially exceeds non-robust methods and remains competitive with dedicated robust methods, which do not scale to large problems. The smooth formulation of DROSS-PCA enables stability analysis via random initializations since no feature is eliminated during training. We find that the method's feature selection is stable when the generating basis is theoretically identifiable and shows increased uncertainty precisely when it is not. On the Human Lung Cell Atlas (584,944 × 27,402), gene selection is 97% consistent across independent random initializations, and consensus gene modules identified from reproducibility across runs are enriched for known biological pathways. On daily sea-surface temperature fields (ERA5, 23,376 × 484,778), the leading sparse mode reproduces the observed El Niño index and the leading modes localize to individual ocean basins.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119957","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

SensX: Model-Agnostic Local Feature Attribution via Calibrated Global Sensitivity Analysis. 基于校准全局敏感性分析的模型不可知局部特征归因。

Transactions on machine learning research Pub Date : 2026-05-01

Manu Aggarwal, Nick Cogan, Vipul Periwal

{"title":"SensX: Model-Agnostic Local Feature Attribution via Calibrated Global Sensitivity Analysis.","authors":"Manu Aggarwal, Nick Cogan, Vipul Periwal","doi":"","DOIUrl":"","url":null,"abstract":"Local feature attribution is a standard tool for auditing and debugging deep learning predictions, but existing attribution methods are not designed for systems that chain pretrained, frozen, or API-only modules. Gradient-based methods such as Integrated Gradients require an end-to-end computational graph that may be unavailable. Perturbation-based methods such as KernelSHAP require a reference input or background distribution whose choice can substantially alter attributions and may not be defensible for composite pipelines. We present SensX, a local attribution method that treats the model as a black box and replaces arbitrary design choices with interpretable, application-grounded parameters. SensX adapts Morris-style coordinate walks from global sensitivity analysis to local attribution. It requires no access to model internals, training data, or arbitrary reference inputs. We validate SensX across four case studies, each targeting a distinct limitation of existing methods. On a synthetic benchmark where ground-truth relevant features vary per input, SensX reaches 95% top-2 attribution accuracy versus 58% for the best KernelSHAP/Integrated Gradients variant. On a ViT with > 150,000 pixel-channel features, SensX produces spatially coherent maps and exposes systematic intra-patch bias where KernelSHAP is infeasible and Integrated Gradients yields task-irrelevant attributions. On single-cell classifiers with unstructured gene-expression features, SensX attains the lowest top- <math><mi>k</mi></math> perturbation AUC. On a composite spatial transcriptomics system where neither method is applicable, SensX reveals reliance on preprocessing grid artifacts and a bias toward low-staining regions.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13629644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119948","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

Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation. 肿瘤锚定深度特征随机森林在肺癌分割中的分布外检测。

Transactions on machine learning research Pub Date : 2026-01-01

Aneesh Rangnekar, Harini Veeraraghavan

{"title":"Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation.","authors":"Aneesh Rangnekar, Harini Veeraraghavan","doi":"","DOIUrl":"","url":null,"abstract":"Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment. Despite self-supervised pre-training on numerous datasets, state-of-the-art transformer backbones remain susceptible to out-of-distribution (OOD) inputs, often producing confidently incorrect segmentations with potential risk in clinical deployment. Hence, we introduce RF-Deep, a lightweight post-hoc random forests-based framework that uses deep features trained with limited outlier exposure, requiring as few as 40 labeled scans (20 in-distribution and 20 OOD), to improve scan-level OOD detection. RF-Deep repurposes the hierarchical features from the pretrained-then-finetuned segmentation backbones, aggregating features from multiple regions-of-interest anchored to predicted tumor regions to capture OOD likelihood. We evaluated RF-Deep on 2,232 CT volumes spanning near-OOD (pulmonary embolism, COVID-19 negative) and far-OOD (kidney cancer, healthy pancreas) datasets. RF-Deep achieved AUROC > 93 on the challenging near-OOD datasets, where it outperformed the next best method by 4-7 percentage points, and produced near-perfect detection (AUROC > 99) on far-OOD datasets. The approach also showed transferability to two blinded validation datasets under the ensemble configuration (COVID-19 positive and breast cancer; AUROC > 94). RF-Deep maintained consistent performance across backbones of different depths and pretraining strategies, demonstrating applicability of post-hoc detectors as a safety filter for clinical deployment of tumor segmentation pipelines.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347710","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

Optimized Graph Structures for Calibrating Graph Neural Networks with Out-of-Distribution Nodes. 具有分布外节点的图神经网络的优化图结构。

Transactions on machine learning research Pub Date : 2026-01-01

Weili Shi, Xueying Yang, Xujiang Zhao, Haifeng Chen, Zhiqiang Tao, Sheng Li

{"title":"Optimized Graph Structures for Calibrating Graph Neural Networks with Out-of-Distribution Nodes.","authors":"Weili Shi, Xueying Yang, Xujiang Zhao, Haifeng Chen, Zhiqiang Tao, Sheng Li","doi":"","DOIUrl":"","url":null,"abstract":"Calibrating graph neural networks (GNNs) in the presence of out-of-distribution (OOD) nodes is challenging because messages propagated from OOD neighbors can distort both the predictions and confidence estimates of in-distribution (ID) nodes. Existing graph calibration methods primarily adjust model outputs through node-wise temperature scaling or reweight edges according to general predictive importance and homophily. However, they do not directly account for the adverse effects of ID-OOD connections and may therefore preserve or even amplify misleading information propagated from OOD nodes. Inspired by the ability of graph structure learning to filter noisy topological information, we empirically observe that downweighting edges between ID and OOD nodes can substantially improve calibration. Identifying these edges and assigning appropriate weights is challenging because the identities of OOD nodes are unknown. To address this challenge, we propose Graph Calibration via Structure Optimization (GCSO), a novel framework for calibrating GNNs in the presence of OOD nodes. GCSO introduces an iterative edge-sampling mechanism to capture graph topological information and formulates adaptive structure optimization as a Markov Decision Process (MDP). An actor-critic policy then dynamically adjusts edge weights based on their effects on target-node predictions. We further design a tailored calibration-aware reward to guide the policy toward an adaptive graph structure that suppresses harmful OOD information propagation. Moreover, the optimized graph structure can be seamlessly integrated with existing temperature-scaling methods for further calibration improvements. Experiments on benchmark datasets demonstrate that GCSO significantly reduces expected calibration error while maintaining competitive classification accuracy. The source code is available at https://github.com/DamoSWL/GCSO.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198273","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

Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction. 仍然有竞争力：重新审视不规则时间序列预测的循环模型。

Transactions on machine learning research Pub Date : 2026-01-01

Ankitkumar Joshi, Milos Hauskrecht

{"title":"Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction.","authors":"Ankitkumar Joshi, Milos Hauskrecht","doi":"","DOIUrl":"","url":null,"abstract":"Modeling irregularly sampled multivariate time series is a persistent challenge in domains like healthcare and sensor networks. While recent works have explored a variety of complex learning architectures to solve the prediction problems for irregularly sampled time series, it remains unclear what the true benefits of some of these architectures are, and whether clever modifications of simpler and more efficient RNN-based algorithms are still competitive, i.e. they are on par with or even superior to these methods. In this work, we propose and study GRUwE: Gated Recurrent Unit with Exponential basis functions, that builds upon RNN-based architectures for observations made at irregular times. GRUwE supports both regression-based and event-based predictions in continuous time. GRUwE works by maintaining a Markov state representation of the time series that updates with the arrival of irregular observations. The Markov state update relies on two reset mechanisms: (i) observation-triggered reset to account for the new observation, and (ii) time-triggered reset that relies on learnable exponential decays, to support the predictions in continuous time. Our empirical evaluations across several real-world benchmarks on next-observation and next-event prediction tasks demonstrate that GRUwE can indeed achieve competitive or superior performance compared to the recent state-of-the-art (SOTA) methods. Thanks to its simplicity, GRUwE offers compelling advantages: it is easy to implement, requires minimal hyper-parameter tuning efforts, and significantly reduces the computational overhead in the online deployment.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13138518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847309","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

Sparse-Input Neural Network using Group Concave Regularization. 基于群凹正则化的稀疏输入神经网络。

Transactions on machine learning research Pub Date : 2025-12-01

Bin Luo, Susan Halabi

{"title":"Sparse-Input Neural Network using Group Concave Regularization.","authors":"Bin Luo, Susan Halabi","doi":"","DOIUrl":"","url":null,"abstract":"Simultaneous feature selection and non-linear function estimation is challenging in modeling, especially in high-dimensional settings where the number of variables exceeds the available sample size. In this article, we investigate the problem of feature selection in neural networks. Although the group least absolute shrinkage and selection operator (LASSO) has been utilized to select variables for learning with neural networks, it tends to select unimportant variables into the model to compensate for its over-shrinkage. To overcome this limitation, we propose a framework of sparse-input neural networks using group concave regularization for feature selection in both low-dimensional and high-dimensional settings. The main idea is to apply a proper concave penalty to the <math> <msub><mrow><mi>l</mi></mrow> <mrow><mn>2</mn></mrow> </msub> </math> norm of weights from all outgoing connections of each input node, and thus obtain a neural net that only uses a small subset of the original variables. In addition, we develop an effective algorithm based on backward path-wise optimization to yield stable solution paths, in order to tackle the challenge of complex optimization landscapes. We provide a rigorous theoretical analysis of the proposed framework, establishing finite-sample guarantees for both variable selection consistency and prediction accuracy. These results are supported by extensive simulation studies and real data applications, which demonstrate the finite-sample performance of the estimator in feature selection and prediction across continuous, binary, and time-to-event outcomes.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13061425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647701","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

Multi-Modal Foundation Models for Computational Pathology: A Survey. 计算病理学的多模态基础模型：综述。

Transactions on machine learning research Pub Date : 2025-11-01

Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Xiaohui Chen, Yi He, Zhong Chen, Peter K Sorger, Chen Zhao

{"title":"Multi-Modal Foundation Models for Computational Pathology: A Survey.","authors":"Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Xiaohui Chen, Yi He, Zhong Chen, Peter K Sorger, Chen Zhao","doi":"","DOIUrl":"","url":null,"abstract":"Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopathological images. While early developments centered on uni-modal models trained solely on visual data, recent advances have highlighted the promise of multi-modal foundation models that integrate heterogeneous data sources such as textual reports, structured domain knowledge, and molecular profiles. In this survey, we provide a comprehensive and up-to-date review of multi-modal foundation models in CPath, with a particular focus on models built upon hematoxylin and eosin (H&E) stained whole slide images (WSIs) and tile-level representations. We categorize 34 state-of-the-art multi-modal foundation models into three major paradigms: vision-language, vision-knowledge graph, and vision-gene expression. We further divide vision-language models into non-LLM-based and LLM-based approaches. Additionally, we analyze 30 available multi-modal datasets tailored for pathology, grouped into image-text pairs, instruction datasets, and image-other modality pairs. Our survey also presents a taxonomy of downstream tasks, highlights training and evaluation strategies, and identifies key challenges and future directions. We aim for this survey to serve as a valuable resource for researchers and practitioners working at the intersection of pathology and AI.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13081565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700452","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

Scalable Generative Modeling of Weighted Graphs. 加权图的可伸缩生成建模。

Transactions on machine learning research Pub Date : 2025-10-01

Richard Williams, Eric Nalisnick, Andrew Holbrook

{"title":"Scalable Generative Modeling of Weighted Graphs.","authors":"Richard Williams, Eric Nalisnick, Andrew Holbrook","doi":"","DOIUrl":"","url":null,"abstract":"Weighted graphs are ubiquitous throughout biology, chemistry, and the social sciences, motivating the development of generative models for abstract weighted graph data using deep neural networks. However, most current deep generative models are designed for unweighted graphs and cannot be easily extended to weighted topologies. Among those that do incorporate edge weights, few consider a joint distribution with the topology of the graph. Furthermore, learning a distribution over weighted graphs must account for complex nonlocal dependencies between both the edges of the graph and corresponding weights of each edge. We develop an autoregressive model BiGG-E, a nontrivial extension of the BiGG model, that learns a joint distribution over weighted graphs while exploiting sparsity to generate a weighted graph with <math><mi>n</mi></math> nodes and <math><mi>m</mi></math> edges in <math><mi>O</mi> <mo>(</mo> <mo>(</mo> <mi>n</mi> <mo>+</mo> <mi>m</mi> <mo>)</mo> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>n</mi> <mo>)</mo></math> time. Simulation studies and experiments on a variety of benchmark datasets demonstrate that BiGG-E best captures distributions over weighted graphs while remaining scalable and computationally efficient.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763242","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

LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models. litl - lvm：使用潜在变量模型的线性预测中相互作用项的结构化正则化。

Transactions on machine learning research Pub Date : 2025-09-01

Mohammadreza Nemati, Zhipeng Huang, Kevin S Xu

{"title":"LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models.","authors":"Mohammadreza Nemati, Zhipeng Huang, Kevin S Xu","doi":"","DOIUrl":"","url":null,"abstract":"Some of the simplest, yet most frequently used predictors in statistics and machine learning use weighted linear combinations of features. Such linear predictors can model non-linear relationships between features by adding interaction terms corresponding to the products of all pairs of features. We consider the problem of accurately estimating coefficients for interaction terms in linear predictors. We hypothesize that the coefficients for different interaction terms have an approximate low-dimensional structure and represent each feature by a latent vector in a low-dimensional space. This low-dimensional representation can be viewed as a structured regularization approach that further mitigates overfitting in high-dimensional settings beyond standard regularizers such as the lasso and elastic net. We demonstrate that our approach, called LIT-LVM, achieves superior prediction accuracy compared to the elastic net, hierarchical lasso, and factorization machines on a wide variety of simulated and real data, particularly when the number of interaction terms is high compared to the number of samples. LIT-LVM also provides low-dimensional latent representations for features that are useful for visualizing and analyzing their relationships.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12912803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146222345","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

Solving Inverse Problems using Diffusion with Iterative Colored Renoising. 用扩散迭代彩色重构求解逆问题。

Transactions on machine learning research Pub Date : 2025-08-01

Matthew C Bendel, Saurav K Shastri, Rizwan Ahmad, Philip Schniter

引用次数: 0