Tootfinder

Detection of quantum information masking via machine learning
Sheng-Ao Mao, Lin Zhang, Bo Li
https://arxiv.org/abs/2510.12507 https://arxiv.org/pdf/2510.12…

Detection of quantum information masking via machine learning
Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing machine learning to detect quantum information masking. In this work, we investigate supervised machine learning for detecting quantum information masking in both pure and mixed qubit states. For pure qubit states, we randomly generate the corresponding dens…

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Kevin Kuo, Chhavi Yadav, Virginia Smith
https://arxiv.org/abs/2510.12595 https://

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we conduct an interview study to understand the practical challenges associated with cross-silo FL adoption. With interviews spanning a diverse …

Robot Learning: A Tutorial
Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, Michel Aractingi
https://arxiv.org/abs/2510.12403 https://arxiv.…

Robot Learning: A Tutorial
Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecedented capabilities in autonomous systems. This tutorial navigates the landscape of modern robot learning, charting a course from the foundational principles of Reinforcement Learning and Behavioral Cloning to generalist, language-conditione…

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Nges Brian Njungle, Eric Jahns, Luigi Mastromauro, Edwin P. Kayang, Milan Stojkov, Michel A. Kinsy
https://arxiv.org/abs/2510.09985

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Machine learning has become a crucial part of our lives, with applications spanning nearly every aspect of our daily activities. However, using personal information in machine learning applications has sparked significant security and privacy concerns about user data. To address these challenges, different privacy-preserving machine learning (PPML) frameworks have been developed to protect sensitive information in machine learning applications. These frameworks generally attempt to balance desi…

Every week, Metacurity delivers our free and paid subscribers a run-down of the top infosec-related long reads we didn't have time for during the daily crush of cyber news.
This week's selection covers
--Massive surveillance in Mexico City leaves crime high,
--Workplace surveillance can harm workers,
--Machine learning privacy attacks are less effective in reality than they are in theory,
--LLMs produce more secure code when trained on flaw-free code,

Best infosec-related long reads for the week of 11/8/25
Massive surveillance in Mexico City leaves crime high, Workplace surveillance can harm workers, Machine learning privacy attacks are less effective in reality than they are in theory, LLMs produce more secure code when trained on flaw-free code, Three major kinds of bots cause web resource headaches

Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering
Arjun Sahney, Ram Gorthi, Cezary {\L}astowski, Javier Vega
https://arxiv.org/abs/2510.11694

Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering
We present Operand Quant, a single-agent, IDE-based architecture for autonomous machine learning engineering (MLE). Operand Quant departs from conventional multi-agent orchestration frameworks by consolidating all MLE lifecycle stages -- exploration, modeling, experimentation, and deployment -- within a single, context-aware agent. On the MLE-Benchmark (2025), Operand Quant achieved a new state-of-the-art (SOTA) result, with an overall medal rate of 0.3956 +/- 0.0565 across 75 problems -- the h…

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
Luis Rojas, Sebasti\'an Espinoza, Esteban Gonz\'alez, Carlos Maldonado, Fei Luo
https://arxiv.org/abs/2510.09876

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied t…

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Kai E. Yang, Xudong Sun, Lucas A. Tarr, Jiayi Liu, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
https://…

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Inferring the three-dimensional (3D) solar atmospheric structures from observations is a critical task for advancing our understanding of the magnetic fields and electric currents that drive solar activity. In this work, we introduce a novel, Physics-Informed Machine Learning method to reconstruct the 3D structure of the lower solar atmosphere based on the output of optical depth sampled spectropolarimetric inversions, wherein both the fully disambiguated vector magnetic fields and the geometri…

Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy
Fabio Magarelli, Geraldine B. Boylan, Saeed Montazeri, Feargal O'Sullivan, Dominic Lightbody, Minoo Ashoori, Tamara Skoric Ceranic, John M. O'Toole
https://arxiv.org/abs/2509.09695

Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy
Machine learning (ML) has the potential to support and improve expert performance in monitoring the brain function of at-risk newborns. Developing accurate and reliable ML models depends on access to high-quality, annotated data, a resource in short supply. ML competitions address this need by providing researchers access to expertly annotated datasets, fostering shared learning through direct model comparisons, and leveraging the benefits of crowdsourcing diverse expertise. We compiled a retro…

A meta-analysis on the performance of machine-learning based language models for sentiment analysis
Elena Rohde, Jonas Klingwort, Christian Borgs
https://arxiv.org/abs/2509.09728

A meta-analysis on the performance of machine-learning based language models for sentiment analysis
This paper presents a meta-analysis evaluating ML performance in sentiment analysis for Twitter data. The study aims to estimate the average performance, assess heterogeneity between and within studies, and analyze how study characteristics influence model performance. Using PRISMA guidelines, we searched academic databases and selected 195 trials from 20 studies with 12 study features. Overall accuracy, the most reported performance metric, was analyzed using double arcsine transformation and …

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Performance of Machine Learning Methods for Gravity Inversion: Successes and Challenges
Vahid Negahdari, Shirin Samadi Bahrami, Seyed Reza Moghadasi, Mohammad Reza Razvan

Steelers vs. Bengals NFL player props: Self-learning AI backs Chase over 69.5 yards on Thursday Night Football

https://www.cbssports.com/nfl/news/steelers-beng…

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Weilong Chen, Franz G\"orlich, Paul Fuchs, Julija Zavadlav
https://arxiv.org/abs/2510.11148

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer timescales that are otherwise infeasible with atomistic models. Machine learning potentials (MLPs), with their capacity to capture many-body interactions, can provide accurate approximations of the potential of mean force (PMF) in CG models. Current CG MLPs are typically trained in a bottom-up manner via force matching, which in practice relies on configurations sampled from the unbiased equilibrium Bo…

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
Yusheng Zheng, Wenxue Liu, Yunhong Che, Ferdinand Grimm, Jingyuan Zhao, Xiaosong Hu, Simona Onori, Remus Teodorescu, Gregory J. Offer
https://arxiv.org/abs/2509.10380…

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for resource-efficient and scalable development of accurate, robust, and adaptive internal temperature estimation algorithms by blending physics-based modeling with machine learning, in order to address the key challenges in data collection, model parameterization…

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Jaesun Kim, Jinmu You, Yutack Park, Yunsung Lim, Yujin Kang, Jisu Kim, Haekwan Jeon, Deokgi Hong, Seung Yul Lee, Saerom Choi, Yongdeok Kim, Jae W. Lee, Seungwu Han
https://arxiv.org/abs/2510.11241

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to narrow datasets or computational protocols, limiting their reliability across chemical and functional domains. We introduce a transferable multi-domain training strategy that jointly optimizes universal and task-specific parameters through selective regularization, coupled with a domain-bridging set (DBS) that aligns …

Machine Learning Frameworks for Large-Scale Radio Surveys: A Summary of Recent Studies
Nikhel Gupta
https://arxiv.org/abs/2510.11145 https://arxiv.org/pdf/…

Machine Learning Frameworks for Large-Scale Radio Surveys: A Summary of Recent Studies
The rapid growth of large-scale radio surveys, generating over 100 petabytes of data annually, has created a pressing need for automated data analysis methods. Recent research has explored the application of machine learning techniques to address the challenges associated with detecting and classifying radio galaxies, as well as discovering peculiar radio sources. This paper provides an overview of our investigations with the Evolutionary Map of the Universe (EMU) survey, detailing the methodol…

Physics-informed sensor coverage through structure preserving machine learning
Benjamin David Shaffer, Brooks Kinch, Joseph Klobusicky, M. Ani Hsieh, Nathaniel Trask
https://arxiv.org/abs/2509.10363

Physics-informed sensor coverage through structure preserving machine learning
We present a machine learning framework for adaptive source localization in which agents use a structure-preserving digital twin of a coupled hydrodynamic-transport system for real-time trajectory planning and data assimilation. The twin is constructed with conditional neural Whitney forms (CNWF), coupling the numerical guarantees of finite element exterior calculus (FEEC) with transformer-based operator learning. The resulting model preserves discrete conservation, and adapts in real time to s…

If you can distinguish, you can express: Galois theory, Stone--Weierstrass, machine learning, and linguistics
Ben Blum-Smith, Claudia Brugman, Thomas Conners, Soledad Villar
https://arxiv.org/abs/2510.09902

If you can distinguish, you can express: Galois theory, Stone--Weierstrass, machine learning, and linguistics
This essay develops a parallel between the Fundamental Theorem of Galois Theory and the Stone--Weierstrass theorem: both can be viewed as assertions that tie the distinguishing power of a class of objects to their expressive power. We provide an elementary theorem connecting the relevant notions of "distinguishing power". We also discuss machine learning and data science contexts in which these theorems, and more generally the theme of links between distinguishing power and expressive power, ap…

Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods
Mohammad Noormohammadi, Atefeh Javadi, Mehdi Khakian Ghomi
https://arxiv.org/abs/2510.10296

Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods
Multiple structures within stellar groups are an intriguing subject for theoretical and observational studies of stellar formation. With the accuracy and completeness of data from Gaia Data Release 3, we now have new opportunities to detect reliable members of stellar groups across a larger field of view than in previous studies. In this work, using machine learning methods and high-accuracy data, we investigate the possibility of detecting multiple structures within 500 arcmin of ASCC 32. We f…

Machine Learning-Integrated Hybrid Fluid-Kinetic Framework for Quantum Electrodynamic Laser Plasma Simulations
Sadra Saremi, Amirhossein Ahmadkhan Kordbacheh
https://arxiv.org/abs/2510.11174

Machine Learning-Integrated Hybrid Fluid-Kinetic Framework for Quantum Electrodynamic Laser Plasma Simulations
High-intensity laser plasma interactions create complex computational problems because they involve both fluid and kinetic regimes, which need models that maintain physical precision while keeping computational speed. The research introduces a machine learning-based three-dimensional hybrid fluid-particle-in-cell (PIC) system, which links relativistic plasma behavior to automatic regime transitions. The technique employs fluid approximations for stable areas but activates the PIC solver when Sw…

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

Loss Behavior in Supervised Learning with Entangled States
Alexander Mandl, Johanna Barzen, Marvin Bechtold, Frank Leymann, Lavinia Stiliadou
https://arxiv.org/abs/2509.10141 ht…

Loss Behavior in Supervised Learning with Entangled States
Quantum Machine Learning (QML) aims to leverage the principles of quantum mechanics to speed up the process of solving machine learning problems or improve the quality of solutions. Among these principles, entanglement with an auxiliary system was shown to increase the quality of QML models in applications such as supervised learning. Recent works focus on the information that can be extracted from entangled training samples and their effect on the approximation error of the trained model. Howe…

A Machine Learning Approach for MIDI to Guitar Tablature Conversion
Maximos Kaliakatsos-Papakostas, Gregoris Bastas, Dimos Makris, Dorien Herremans, Vassilis Katsouros, Petros Maragos
https://arxiv.org/abs/2510.10619

A Machine Learning Approach for MIDI to Guitar Tablature Conversion
Guitar tablature transcription consists in deducing the string and the fret number on which each note should be played to reproduce the actual musical part. This assignment should lead to playable string-fret combinations throughout the entire track and, in general, preserve parsimonious motion between successive combinations. Throughout the history of guitar playing, specific chord fingerings have been developed across different musical styles that facilitate common idiomatic voicing combinati…

Efficient Learned Image Compression Through Knowledge Distillation
Fabien Allemand, Attilio Fiandrotti, Sumanta Chaudhuri, Alaa Eddine Mazouz
https://arxiv.org/abs/2509.10366 ht…

Efficient Learned Image Compression Through Knowledge Distillation
Learned image compression sits at the intersection of machine learning and image processing. With advances in deep learning, neural network-based compression methods have emerged. In this process, an encoder maps the image to a low-dimensional latent space, which is then quantized, entropy-coded into a binary bitstream, and transmitted to the receiver. At the receiver end, the bitstream is entropy-decoded, and a decoder reconstructs an approximation of the original image. Recent research sugges…

Physical embedding machine learning force fields for organic systems
Junbao Hu, Dingyu Hou, Jian Jiang
https://arxiv.org/abs/2509.10270 https://arxiv.org/p…

Physical embedding machine learning force fields for organic systems
Machine learning force fields possess unprecedented potential in achieving both accuracy and efficiency in molecular simulations. Nevertheless, their application in organic systems is often hindered by structural collapse during simulation and significant deviations in the prediction of macroscopic properties. Here, two physics-embedded strategies are introduced to overcome these limitations. First, a physics-inspired self-adaptive bond-length sampling method achieves long-timescale stable simu…

USA Tariffs Effect: Machine Learning Insights into the Stock Market
Mridul Patel
https://arxiv.org/abs/2510.10877 https://arxiv.org/pdf/2510.10877

USA Tariffs Effect: Machine Learning Insights into the Stock Market
The imposition of tariffs by President Trump during his second term had far-reaching consequences for global markets, including Australia. This study investigates how both the announcement and subsequent implementation of these tariffs, specifically on 02-Apr-2025, affected the Australian stock market, focusing on the S\&P/ASX 200 index over the period from 21-Jan-2025 to 25-Jul-2025. To accurately capture the significance and behavior of market fluctuations, the exploratory data analysis (EDA)…

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans following Omega-3 Fatty Acid Supplementation
Shudong Sun, Aki Hara, Laurel Johnstone, Brian Hallmark, Joseph C. Watkins, Cynthia A. Thomson, Susan M. Schembre, Susan Sergeant, Jason Umans, Guang Yao, Hao Helen Zhang, Floyd H. Chilton

Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans following Omega-3 Fatty Acid Supplementation
Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in response to n-3 HUFA supplementation. The current observational study aimed to simulate randomized clinical trial (RCT) conditions by matchi…

Detection of Anomalous Behavior in Robot Systems Based on Machine Learning
Mahfuzul I. Nissan, Sharmin Aktar
https://arxiv.org/abs/2509.09953 https://arxiv…

Detection of Anomalous Behavior in Robot Systems Based on Machine Learning
Ensuring the safe and reliable operation of robotic systems is paramount to prevent potential disasters and safeguard human well-being. Despite rigorous design and engineering practices, these systems can still experience malfunctions, leading to safety risks. In this study, we present a machine learning-based approach for detecting anomalies in system logs to enhance the safety and reliability of robotic systems. We collected logs from two distinct scenarios using CoppeliaSim and comparatively…

Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
Katarina Dyreby, Francisco Caldas, Cl\'audia Soares
https://arxiv.org/abs/2510.11242

Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts and the Low Earth Orbit (LEO) space environment. This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a LEO mega-constellation - Starlink. The goal of this work is twofold: (i) compare and analyze the quality of the data against high-precision numerical propagation. (ii) …

Performance of Machine Learning Methods for Gravity Inversion: Successes and Challenges
Vahid Negahdari, Shirin Samadi Bahrami, Seyed Reza Moghadasi, Mohammad Reza Razvan
https://arxiv.org/abs/2510.09632

Performance of Machine Learning Methods for Gravity Inversion: Successes and Challenges
Gravity inversion is the problem of estimating subsurface density distributions from observed gravitational field data. We consider the two-dimensional (2D) case, in which recovering density models from one-dimensional (1D) measurements leads to an underdetermined system with substantially more model parameters than measurements, making the inversion ill-posed and non-unique. Recent advances in machine learning have motivated data-driven approaches for gravity inversion. We first design a convo…

Based on Deep Neural Networks: A Machine Learning-Assisted Channel Estimation Method for MIMO Systems
Haoran He
https://arxiv.org/abs/2510.11891 https://ar…

Based on Deep Neural Networks: A Machine Learning-Assisted Channel Estimation Method for MIMO Systems
This paper proposes a machine learning-assisted channel estimation approach for massive MIMO systems, leveraging DNNs to outperform traditional LS and MMSE methods. In 5G and beyond, accurate channel estimation mitigates pilot contamination and high mobility issues that harm system reliability. The proposed DNN architecture includes multi-layer perceptrons with ReLU activation, 3 hidden layers (256, 128, 64 neurons respectively), uses Adam optimizer (learning rate 1e-4) and MSE loss function. I…

Macroeconomic Forecasting and Machine Learning
Ta-Chung Chi (Kevin), Ting-Han Fan (Kevin), Raffaele M. Ghigliazza (Kevin), Domenico Giannone (Kevin), Zixuan (Kevin), Wang
https://arxiv.org/abs/2510.11008

Macroeconomic Forecasting and Machine Learning
We forecast the full conditional distribution of macroeconomic outcomes by systematically integrating three key principles: using high-dimensional data with appropriate regularization, adopting rigorous out-of-sample validation procedures, and incorporating nonlinearities. By exploiting the rich information embedded in a large set of macroeconomic and financial predictors, we produce accurate predictions of the entire profile of macroeconomic risk in real time. Our findings show that regulariza…

This is an important announcement. Google uses it's ecosystem to gain an advantage, "Announcing Model Context Protocol (MCP) support for Google services"
https://cloud.google.com/blog/products/ai-machine-learning/ann…

Announcing official MCP support for Google services | Google Cloud Blog
Google is announcing official MCP support for Google and Google Cloud services for AI agents.

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Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
Amandip Sangha
https://arxiv.org/abs/2509.09991 https://

Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
This paper presents a machine learning-based approach to estimate the energy consumption of virtual servers without access to physical power measurement interfaces. Using resource utilization metrics collected from guest virtual machines, we train a Gradient Boosting Regressor to predict energy consumption measured via RAPL on the host. We demonstrate, for the first time, guest-only resource-based energy estimation without privileged host access with experiments across diverse workloads, achiev…

Geopolitics, Geoeconomics and Risk:A Machine Learning Approach
Alvaro Ortiz, Tomasa Rodrigo
https://arxiv.org/abs/2510.12416 https://arxiv.org/pdf/2510.124…

Geopolitics, Geoeconomics and Risk:A Machine Learning Approach
We introduce a novel high-frequency daily panel dataset of both markets and news-based indicators -- including Geopolitical Risk, Economic Policy Uncertainty, Trade Policy Uncertainty, and Political Sentiment -- for 42 countries across both emerging and developed markets. Using this dataset, we study how sentiment dynamics shape sovereign risk, measured by Credit Default Swap (CDS) spreads, and evaluate their forecasting value relative to traditional drivers such as global monetary policy and m…

Predicting Crystal Structures and Ionic Conductivity in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential
Jonas B\"ohm, Aur\'elie Champagne
https://arxiv.org/abs/2510.09861

Predicting Crystal Structures and Ionic Conductivity in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential
This work demonstrates the effectiveness of fine-tuning the CHGNet universal machine learning interatomic potential (uMLIP) to investigate ionic transport mechanisms in ternary halide solid electrolytes of the Li$_{3}$YCl$_{6-x}$Br$_{x}$ family (x = 0 to 6), which are promising candidates for solid-state battery applications. We present a strategy for generating ordered structural models from experimentally derived disordered Li$_{3}$YCl$_{6}$ (LYC) and Li$_{3}$YBr$_{6}$ (LYB) structures. These…

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
Daniel Pulido-Cort\'azar, Daniel Gibert, Felip Many\`a
https://arxiv.org/abs/2510.12310

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a machine learning model into making incorrect predictions. This research presents DeepTrust, a novel metaheuristic that arranges flexible classifiers, like deep neural networks, into an ordered sequence where the final decision is made by a single internal model…

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment
Amir Bahador Javadi, Amin Kargarian, Mort Naraghi-Pour
https://arxiv.org/abs/2509.10246 https://

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment
The increasing penetration of renewable energy sources introduces significant uncertainty in power system operations, making traditional deterministic unit commitment approaches computationally expensive. This paper presents a machine learning surrogate modeling approach designed to reformulate the feasible design space of the two-stage stochastic unit commitment (TSUC) problem, reducing its computational complexity. The proposed method uses a support vector machine (SVM) to construct a surroga…

Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
Benedikt B\"ock, Amar Kasibovic, Wolfgang Utschick
https://arxiv.org/abs/2510.12279

Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
Standardized (link-level) channel models such as the 3GPP TDL and CDL models are frequently used to evaluate machine learning (ML)-based physical-layer methods. However, in this work, we argue that a link-level perspective incorporates limiting assumptions, causing unwanted distributional shifts or necessitating impractical online training. An additional drawback is that this perspective leads to (near-)Gaussian channel characteristics. Thus, ML-based models, trained on link-level channel data,…

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Shiwei Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Jianbin Lin, Wenliang Zhong
https://arxiv.org/abs/2509.10161

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative model training without sharing local data. Despite its advantages, FL suffers from substantial communication overhead, which can affect training efficiency. Recent efforts have mitigated this issue by quantizing model updates to reduce communication costs. However, most existing methods apply quantization only after local training, introducing quantization errors into the trained parameters a…

Machine Learning Integrated Near-Infrared Surface-Enhanced Raman Spectroscopy for Accurate Strain-Level Virus Identification
Na Zhang, Ziyang Wang, Xielin Wang, Gabriel A. V\'azquez-Lizardi, Paula Pi\~neiro Varela, Dorleta Jimenez de Aberasturi, David E. Sanchez, Nestor Perea-Lopez, Samuel Lin, Ryeanne Ricker, Edgar Dimitrov, Alexander J. Sredenschek, Kalana D. Halanayake, Yin-Ting Yeh, Julian A. Mintz, Jiarong Ye, Sharon Xiaolei Huang, Huaguang Lu, Elodie Ghedin, Danielle Reifsnyd…

Machine Learning Integrated Near-Infrared Surface-Enhanced Raman Spectroscopy for Accurate Strain-Level Virus Identification
Strain-level identification of viruses is critical for effective public health responses to potential outbreaks, yet current diagnostic methods often lack the necessary speed or sensitivity. Surface-enhanced Raman spectroscopy (SERS) offers great potential for fast and precise virus clarification through unique vibrational fingerprints of biological components. However, existing protocols typically operate outside of the tissue's transparent near-infrared (NIR) window, and are further limited b…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Learning to sample fibers for goodness-of-fit testing
Ivan Gvozdanovi\'c, Sonja Petrovi\'c

Vendi Information Gain for Active Learning and its Application to Ecology
Quan Nguyen, Adji Bousso Dieng
https://arxiv.org/abs/2509.10390 https://arxiv.org…

Vendi Information Gain for Active Learning and its Application to Ecology
While monitoring biodiversity through camera traps has become an important endeavor for ecological research, identifying species in the captured image data remains a major bottleneck due to limited labeling resources. Active learning -- a machine learning paradigm that selects the most informative data to label and train a predictive model -- offers a promising solution, but typically focuses on uncertainty in the individual predictions without considering uncertainty across the entire dataset.…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Learning Shared Representations from Unpaired Data
Amitai Yacobi, Nir Ben-Ari, Ronen Talmon, Uri Shaham

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/14]:
- Evolving Machine Learning: A Survey
Martin, Mukherjee, Baimagambetov, Vanschoren, Polatidis

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
Yinan Wang, Xiaoyang Wang, Zhenyu Wang, Jing Wu, Jian Lv, Han Wang
https://arxiv.org/abs/2509.10293

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
High-pressure crystal structure prediction (CSP) underpins advances in condensed matter physics, planetary science, and materials discovery. Yet, most large atomistic models are trained on near-ambient, equilibrium data, leading to degraded stress accuracy at tens to hundreds of gigapascals and sparse coverage of pressure-stabilized stoichiometries and dense coordination motifs. Here, we introduce OpenCSP, a machine learning framework for CSP tasks spanning ambient to high-pressure conditions. …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/14]:
- Early-Warning of Thunderstorm-Driven Power Outages with a Two-Stage Machine Learning Model
Iryna Stanishevska

Knowledge-Guided Machine Learning Models to Upscale Evapotranspiration in the U.S. Midwest
Aleksei Rozanov, Samikshya Subedi, Vasudha Sharma, Bryan C. Runck
https://arxiv.org/abs/2510.11505

Knowledge-Guided Machine Learning Models to Upscale Evapotranspiration in the U.S. Midwest
Evapotranspiration (ET) plays a critical role in the land-atmosphere interactions, yet its accurate quantification across various spatiotemporal scales remains a challenge. In situ measurement approaches, like eddy covariance (EC) or weather station-based ET estimation, allow for measuring ET at a single location. Agricultural uses of ET require estimates for each field over broad areas, making it infeasible to deploy sensing systems at each location. This study integrates tree-based and knowle…

Elemental Frequency-Based Supervised Classification Approach for the Search of Novel Topological Materials
Zodinpuia Ralte, Ramesh Kumar, Mukhtiyar Singh
https://arxiv.org/abs/2509.09978

Elemental Frequency-Based Supervised Classification Approach for the Search of Novel Topological Materials
The machine learning based approaches efficiently solves the goal of searching the best materials candidate for the targeted properties. The search for topological materials using traditional first-principles and symmetry-based methods often requires lots of computing power or is limited by the crystalline symmetries. In this study, we present frequency-based statistical descriptors for machine learning-driven topological material's classification that is independent of crystallographic symmetr…

A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
\'Angel Ladr\'on, Miguel S\'anchez-Dom\'inguez, Javier Rozal\'en, Fernando R. S\'anchez, Javier de Vicente, Lucas Lacasa, Eusebio Valero, Gonzalo Rubio
https://arxiv.org/abs/2509.10227…

A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatigue cracks to prevent in-flight failures. Robust and precise fatigue life predictors are thus essential to ensure safety. Traditional engineering methods, while reliable, are time consuming and involve complex workflows, including steps such as conducting several Finite Element Method (FEM) simulations, deriving the ex…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Replicable Clustering
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou

Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Ahmed Khaled, Satyen Kale, Arthur Douillard, Chi Jin, Rob Fergus, Manzil Zaheer
https://arxiv.org/abs/2509.10439 …

Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Modern machine learning often requires training with large batch size, distributed data, and massively parallel compute hardware (like mobile and other edge devices or distributed data centers). Communication becomes a major bottleneck in such settings but methods like Local Stochastic Gradient Descent (Local SGD) show great promise in reducing this additional communication overhead. Local SGD consists of three parts: a local optimization process, an aggregation mechanism, and an outer optimize…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Sparse Robust Classification via the Kernel Mean
Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/14]:
- Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth
Xinyu Yuan, Yan Qiao, Meng Li, Zhenchun Wei, Cuiying Feng, Zonghui Wang, Wenzhi Chen

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- An Eulerian Perspective on Straight-Line Sampling
Panos Tsimpos, Youssef Marzouk

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/14]:
- Meta-Learning Adaptive Loss Functions
Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhang

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- What Makes Looped Transformers Perform Better Than Non-Recursive Ones (Provably)
Zixuan Gong, Jiaye Teng, Yong Liu

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/14]:
- OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics
Alexandre Oliveira, Katarina Dyreby, Francisco Caldas, Cl\'audia Soares

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/14]:
- Prompt Optimization Meets Subspace Representation Learning for Few-shot Out-of-Distribution Detec...
Faizul Rakib Sayem, Shahana Ibrahim

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- On Regression in Extreme Regions
Stephan Cl\'emen\c{c}on, Nathan Huet, Anne Sabourin

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[10/14]:
- MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification
Nguyen, Nguyen, Diep, Nguyen, Ho, Metsch, Maurer, Sonntag, Bohnenberger, Hauschild

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalizat...
Peng, Wen, Yang, Fu, Chen, Liu, Wu, Zheng, Sarfraz, Van Gool, Paudel, Stiefelhagen

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Sparse Polyak: an adaptive step size rule for high-dimensional M-estimation
Tianqi Qiao, Marie Maros

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- Sufficient Invariant Learning for Distribution Shift
Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Gang Li, Tianjiao Chen, Mingle Zhou, Min Li, Delong Han, Jin Wan

Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
Saeid Mashhadi, Amirhossein Saghezchi, Vesal Ghassemzadeh Kashani
https://arxiv.org/abs/2510.09465

Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated: next funding within 12 months, patent-stock growth within 24 months, and exit through an initial public offering (IPO) or acquisition within 36 months. Preprocessing is fit on a development window (2010-2019…

Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction
Iftekhar Ahmed, Tanzil Ebad Chowdhury, Biggo Bushon Routh, Nafisa Tasmiya, Shadman Sakib, Adil Ahmed Chowdhury
https://arxiv.org/abs/2510.09493

Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction
Kidneys are the filter of the human body. About 10% of the global population is thought to be affected by Chronic Kidney Disease (CKD), which causes kidney function to decline. To protect in danger patients from additional kidney damage, effective risk evaluation of CKD and appropriate CKD monitoring are crucial. Due to quick and precise detection capabilities, Machine Learning models can help practitioners accomplish this goal efficiently; therefore, an enormous number of diagnosis systems and…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[14/14]:
- The Contingencies of Physical Embodiment Allow for Open-Endedness and Care
Christov-Moore, Juliani, Kiefer, Reggente, Rousse, Safron, Hinrichs, Polani, Damasio

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[13/14]:
- Class-Invariant Test-Time Augmentation for Domain Generalization
Zhicheng Lin, Xiaolin Wu, Xi Zhang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[12/14]:
- Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?
Yun Qu, Qi Wang, Yixiu Mao, Vincent Tao Hu, Bj\"orn Ommer, Xiangyang Ji

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[11/14]:
- mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules
Edwards, Han, Lee, Nguyen, Szymku\'c, Prasad, Jin, Han, Diao, Liu, Peng, Grzybowski, Burke, Ji

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/14]:
- Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction
Weizheng Wang, Baijian Yang, Sungeun Hong, Wenhai Sun, Byung-Cheol Min

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/14]:
- Robust Causal Discovery in Real-World Time Series with Power-Laws
Matteo Tusoni, Giuseppe Masi, Andrea Coletta, Aldo Glielmo, Viviana Arrigoni, Novella Bartolini

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/14]:
- Load Balancing Mixture of Experts with Similarity Preserving Routers
Nabil Omi, Siddhartha Sen, Ali Farhadi

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/7]:
- Are Large Reasoning Models Interruptible?
Tsung-Han Wu, Mihran Miroyan, David M. Chan, Trevor Darrell, Narges Norouzi, Joseph E. Gonzalez

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/7]:
- Network-Optimised Spiking Neural Network (NOS) Scheduling for 6G O-RAN: Spectral Margin and Delay...
Muhammad Bilal, Xiaolong Xu

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF: A Reproducibility Study
Anirudh Ganesh, Jayavardhan Reddy

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/7]:
- DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Chenyu Jiang, Zhenkun Cai, Ye Tian, Zhen Jia, Yida Wang, Chuan Wu

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- On some practical challenges of conformal prediction
Liang Hong, Noura Raydan Nasreddine

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/7]:
- Calibrating Generative Models
Henry D. Smith, Nathaniel L. Diamant, Brian L. Trippe

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/7]:
- Domain-Specific Constitutional AI: Enhancing Safety in LLM-Powered Mental Health Chatbots
Chenhan Lyu, Yutong Song, Pengfei Zhang, Amir M. Rahmani

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/7]:
- ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis
Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili, Azadeh Shakery

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/7]:
- CAMERA: Multi-Matrix Joint Compression for MoE Models via Micro-Expert Redundancy Analysis
Yuzhuang Xu, Xu Han, Yuanchi Zhang, Yixuan Wang, Yijun Liu, Shiyu Ji, Qingfu Zhu, Wanxiang Che

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- Persistent Homology via Ellipsoids
Niklas Canova, Sara Kali\v{s}nik, Aaron Moser, Bastian Rieck, Ana \v{Z}egarac

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/7]:
- Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning
Liu, Obando-Ceron, Lu, He, Wang, Su, Zheng, Castro, Courville, Pan

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- OmniDraft: A Cross-vocabulary, Online Adaptive Drafter for On-device Speculative Decoding
Ramakrishnan, Yuan, Zhuo, Feng, Lin, Su, Zhang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/7]:
- MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation
Tao Zhang, Zhenhai Liu, Yong Xin, Yongjun Jiao

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/7]:
- Posterior Sampling for Continuing Environments
Wanqiao Xu, Shi Dong, Benjamin Van Roy

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust An...
Daniel Pulido-Cort\'azar, Daniel Gibert, Felip Many\`a

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Embedding the Teacher: Distilling vLLM Preferences for Scalable Image Retrieval
Eric He, Akash Gupta, Adian Liusie, Vatsal Raina, Piotr Molenda, Shirom Chabra, Vyas Raina

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- RePro: Training Language Models to Faithfully Recycle the Web for Pretraining
Zichun Yu, Chenyan Xiong

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Attacking Attention of Foundation Models Disrupts Downstream Tasks
Hondamunige Prasanna Silva, Federico Becattini, Lorenzo Seidenari

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- On Regression in Extreme Regions
Stephan Cl\'emen\c{c}on, Nathan Huet, Anne Sabourin

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Multivariate Long-term Time Series Forecasting with Fourier Neural Filter
Chenheng Xu, Dan Wu, Yixin Zhu, Ying Nian Wu

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- DGFusion: Depth-Guided Sensor Fusion for Robust Semantic Perception
Tim Broedermannn, Christos Sakaridis, Luigi Piccinelli, Wim Abbeloos, Luc Van Gool

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- Generative Engine Optimization: How to Dominate AI Search
Mahe Chen, Xiaoxuan Wang, Kaiwen Chen, Nick Koudas

[2025-10-15 Wed (UTC), 90 new articles found for cs.LG Machine Learning]
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Residual-Informed Learning of Solutions to Algebraic Loops
Felix Brandt, Andreas Heuermann, Philip Hannebohm, Bernhard Bachmann
https://arxiv.org/abs/2510.09317 https://

Residual-Informed Learning of Solutions to Algebraic Loops
This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid on…