Data analysis, statistics, and data engineering
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
Hi all, I’m curious about the current review dynamics for ICML 2026, especially after the rebuttal phase. For those who are reviewers (or...
MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...
![[2509.19405] Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper presents a novel mobile data augmentation framework to enhance outdoor multi-cell fingerprinting-based positioning, improving ...
The paper introduces GRASP, a novel framework for model compression that replaces redundant layers in large language models with adaptive...
This paper presents a novel algorithmic framework that integrates momentum terms with stochastic line search methods to optimize finite-s...
This paper explores hypergraphs as weighted directed self-looped graphs, focusing on their spectral properties, clustering algorithms, an...
The MAPS algorithm offers a novel approach to generating model-agnostic, distribution-free prediction intervals in supervised learning, a...
SQL-Exchange introduces a framework for transforming SQL queries across different database schemas while maintaining structural integrity...
The paper explores Quantum Convolutional Neural Networks (QCNNs) and their ability to be classically simulated, revealing insights about ...
This paper explores the local convergence rates of stochastic first-order methods under the local α-Polyak-Lojasiewicz condition, establi...
This paper investigates model selection and parameter estimation for one-dimensional Gaussian mixture models (GMMs), focusing on optimal ...
This article presents a novel methodology called Stochastic Localization via Iterative Posterior Sampling (SLIPS) for sampling from unnor...
This paper presents a novel approach using the graph Laplacian to analyze singularities in point clouds, offering theoretical guarantees ...
This paper presents SCINet, a novel framework for partial multi-label learning that integrates semantic co-occurrence knowledge to improv...
This article presents a novel approach to symbolic regression through the introduction of SimpliPy, a simplification engine that signific...
This study evaluates the performance of generalist Vision Language Models (VLMs) compared to specialist medical VLMs, revealing that gene...
The paper presents Landscaper, an open-source Python package for analyzing loss landscapes in neural networks using multi-dimensional top...
The paper presents EBPO, a novel framework that enhances Group Relative Policy Optimization (GRPO) by employing Empirical Bayes shrinkage...
The paper introduces TextME, a framework that enables zero-shot cross-modal transfer using only text descriptions, addressing the limitat...
The paper discusses the systemic risks posed by algorithmic collisions in interconnected AI systems, highlighting the need for improved g...
The paper explores how fine-tuning large language models can unintentionally create vulnerabilities, analyzing factors like dataset chara...
This paper presents a novel framework combining Variational Autoencoder and Double Machine Learning for analyzing greenwashing in the min...
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