Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding
arXiv:2602.15159v1 Announce Type: new Abstract: Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning, represent missingness through a...
Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge
arXiv:2602.15184v1 Announce Type: new Abstract: Recent advances in scientific machine learning (SciML) have enabled neural operators (NOs) to serve as powerful surrogates for modeling the dynamic evolution of physical systems governed by partial differential equations (PDEs). While existing approaches focus...
Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics
arXiv:2602.15253v1 Announce Type: new Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored. We present the first...
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
arXiv:2602.15304v1 Announce Type: new Abstract: Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to...
FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently, it achieves faster training speed compared to traditional federated...
Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
arXiv:2602.15405v1 Announce Type: new Abstract: Robust classification in noisy environments remains a fundamental challenge in machine learning. Standard approaches typically treat signal enhancement and classification as separate, sequential stages: first enhancing the signal and then applying a classifier. This approach...
Benchmarking IoT Time-Series AD with Event-Level Augmentations
arXiv:2602.15457v1 Announce Type: new Abstract: Anomaly detection (AD) for safety-critical IoT time series should be judged at the event level: reliability and earliness under realistic perturbations. Yet many studies still emphasize point-level results on curated base datasets, limiting value for...
Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
arXiv:2602.15478v1 Announce Type: new Abstract: Mood instability is a key behavioral indicator of mental health, yet traditional assessments rely on infrequent and retrospective reports that fail to capture its continuous nature. Smartphone-based mobile sensing enables passive, in-the-wild mood inference from...
LLM-as-Judge on a Budget
arXiv:2602.15481v1 Announce Type: new Abstract: LLM-as-a-judge has emerged as a cornerstone technique for evaluating large language models by leveraging LLM reasoning to score prompt-response pairs. Since LLM judgments are stochastic, practitioners commonly query each pair multiple times to estimate mean...
Approximation Theory for Lipschitz Continuous Transformers
arXiv:2602.15503v1 Announce Type: new Abstract: Stability and robustness are critical for deploying Transformers in safety-sensitive settings. A principled way to enforce such behavior is to constrain the model's Lipschitz constant. However, approximation-theoretic guarantees for architectures that explicitly preserve Lipschitz continuity...
On the Geometric Coherence of Global Aggregation in Federated GNN
arXiv:2602.15510v1 Announce Type: new Abstract: Federated Learning (FL) enables distributed training across multiple clients without centralized data sharing, while Graph Neural Networks (GNNs) model relational data through message passing. In federated GNN settings, client graphs often exhibit heterogeneous structural and...
Uniform error bounds for quantized dynamical models
arXiv:2602.15586v1 Announce Type: new Abstract: This paper provides statistical guarantees on the accuracy of dynamical models learned from dependent data sequences. Specifically, we develop uniform error bounds that apply to quantized models and imperfect optimization algorithms commonly used in practical...
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Supreme Court updates recusals process
The Supreme Court on Tuesday revealed that it has put new software in place to “assist in identifying potential conflicts” of interest for the justices. In a press release issued […]The postSupreme Court updates recusals processappeared first onSCOTUSblog.
From Scarcity to Scale: A Release-Level Analysis of the Pashto Common Voice Dataset
arXiv:2602.14062v1 Announce Type: new Abstract: Large, openly licensed speech datasets are essential for building automatic speech recognition (ASR) systems, yet many widely spoken languages remain underrepresented in public resources. Pashto, spoken by more than 60 million people, has historically lacked...
BLUEPRINT Rebuilding a Legacy: Multimodal Retrieval for Complex Engineering Drawings and Documents
arXiv:2602.13345v1 Announce Type: new Abstract: Decades of engineering drawings and technical records remain locked in legacy archives with inconsistent or missing metadata, making retrieval difficult and often manual. We present Blueprint, a layout-aware multimodal retrieval system designed for large-scale engineering...
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
arXiv:2602.13485v1 Announce Type: new Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal...
Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity
arXiv:2602.13486v1 Announce Type: new Abstract: Federated low-rank adaptation (FedLoRA) has facilitated communication-efficient and privacy-preserving fine-tuning of foundation models for downstream tasks. In practical federated learning scenarios, client heterogeneity in system resources and data distributions motivates heterogeneous LoRA ranks across clients....
Fast Swap-Based Element Selection for Multiplication-Free Dimension Reduction
arXiv:2602.13532v1 Announce Type: new Abstract: In this paper, we propose a fast algorithm for element selection, a multiplication-free form of dimension reduction that produces a dimension-reduced vector by simply selecting a subset of elements from the input. Dimension reduction is...
Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?
arXiv:2602.13626v1 Announce Type: new Abstract: The expanding integration of Large Language Models (LLMs) into recommender systems poses critical challenges to evaluation reliability. This paper identifies and investigates a previously overlooked issue: benchmark data leakage in LLM-based recommendation. This phenomenon occurs...
Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
arXiv:2602.13651v1 Announce Type: new Abstract: In real-world federated learning (FL) systems, client participation is intermittent, heterogeneous, and often correlated with data characteristics or resource constraints. Existing fairness approaches in FL primarily focus on equalizing loss or accuracy conditional on participation,...
Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling
arXiv:2602.13659v1 Announce Type: new Abstract: Fine-tuning large pretrained language models (LLMs) is a cornerstone of modern NLP, yet its growing memory demands (driven by backpropagation and large optimizer States) limit deployment in resource-constrained settings. Zero-order (ZO) methods bypass backpropagation by...