Tootfinder

Do machine learning climate models work in changing climate dynamics?
Maria Conchita Agana Navarro, Geng Li, Theo Wolf, Mar\'ia P\'erez-Ortiz
https://arxiv.org/abs/2509.12147

Do machine learning climate models work in changing climate dynamics?
Climate change is accelerating the frequency and severity of unprecedented events, deviating from established patterns. Predicting these out-of-distribution (OOD) events is critical for assessing risks and guiding climate adaptation. While machine learning (ML) models have shown promise in providing precise, high-speed climate predictions, their ability to generalize under distribution shifts remains a significant limitation that has been underexplored in climate contexts. This research systema…

Analysing Python Machine Learning Notebooks with Moose
Marius Mignard, Steven Costiou, Nicolas Anquetil, Anne Etien
https://arxiv.org/abs/2509.11748 https://

Analysing Python Machine Learning Notebooks with Moose
Machine Learning (ML) code, particularly within notebooks, often exhibits lower quality compared to traditional software. Bad practices arise at three distinct levels: general Python coding conventions, the organizational structure of the notebook itself, and ML-specific aspects such as reproducibility and correct API usage. However, existing analysis tools typically focus on only one of these levels and struggle to capture ML-specific semantics, limiting their ability to detect issues. This pa…

Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin, Ilia Polian, Frank K. Wilhelm
https://arxiv.org/abs/2509.12933 https://

Data-Efficient Quantum Noise Modeling via Machine Learning
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional models often fail to capture the complex error dynamics of real hardware or require prohibitive characterization overhead. We introduce a data-efficient, machine learning-based framework to construct accurate, parameterized noise models for superconducting quantum processors. Our approach circumvents costly character…

EByFTVeS: Efficient Byzantine Fault Tolerant-based Verifiable Secret-sharing in Distributed Privacy-preserving Machine Learning
Zhen Li, Zijian Zhang, Wenjin Yang, Pengbo Wang, Zhaoqi Wang, Meng Li, Yan Wu, Xuyang Liu, Jing Sun, Liehuang Zhu
https://arxiv.org/abs/2509.12899

EByFTVeS: Efficient Byzantine Fault Tolerant-based Verifiable Secret-sharing in Distributed Privacy-preserving Machine Learning
Verifiable Secret Sharing (VSS) has been widespread in Distributed Privacy-preserving Machine Learning (DPML), because invalid shares from malicious dealers or participants can be recognized by verifying the commitment of the received shares for honest participants. However, the consistency and the computation and communitation burden of the VSS-based DPML schemes are still two serious challenges. Although Byzantine Fault Tolerance (BFT) system has been brought to guarantee the consistency and …

The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
Argimiro Arratia, Alejandra Caba\~na, Ernesto Mordecki, Gerard Rovira-Parra
https://arxiv.org/abs/2509.12185

The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
Model selection in non-linear models often prioritizes performance metrics over statistical tests, limiting the ability to account for sampling variability. We propose the use of a statistical test to assess the equality of variances in forecasting errors. The test builds upon the classic Morgan-Pitman approach, incorporating enhancements to ensure robustness against data with heavy-tailed distributions or outliers with high variance, plus a strategy to make residuals from machine learning mode…

MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing
Rahma Nouaji, Stella Bitchebe, Ricardo Macedo, Oana Balmau
https://arxiv.org/abs/2509.10712

MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing
Data loaders are used by Machine Learning (ML) frameworks like PyTorch and TensorFlow to apply transformations to data before feeding it into the accelerator. This operation is called data preprocessing. Data preprocessing plays an important role in the ML training workflow because if it is inefficiently pipelined with the training, it can yield high GPU idleness, resulting in important training delays. Unfortunately, existing data loaders turn out to waste GPU resources, with $76\%$ GPU idlene…

Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response
Morgan D. Sanger, Mertcan Geyin, Brett W. Maurer
https://arxiv.org/abs/2509.10962

Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response
Using machine learning (ML), high performance computing, and a large body of geospatial information, we develop surrogate models to predict soil liquefaction across regional scales. Two sets of models - one global and one specific to New Zealand - are trained by learning to mimic geotechnical models at the sites of in-situ tests. Our geospatial approach has conceptual advantages in that predictions: (i) are anchored to mechanics, which encourages more sensible response and scaling across the do…

Machine Unlearning for Responsible and Adaptive AI in Education
Betty Mayeku, Sandra Hummel, Parisa Memarmoshrefi
https://arxiv.org/abs/2509.10590 https://…

Machine Unlearning for Responsible and Adaptive AI in Education
The concept of Machine Unlearning (MU) has gained popularity in various domains due to its ability to address several issues in Machine Learning (ML) models, particularly those related to privacy, security, bias mitigation, and adaptability. With these abilities, MU is evolving into a promising technology in upholding Responsible AI principles and optimizing ML models' performance. However, despite its promising potential, the concept has not received much attention in the education sector. In …

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning
Lichang Zhu, Laura Schaefer, Leitao Chen, Ben Xu
https://arxiv.org/abs/2509.11863

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning
Predicting and interpreting thermal performance under oscillating flow in porous structures remains a critical challenge due to the complex coupling between fluid dynamics and geometric features. This study introduces a data-driven wGAN-LBM-Nested_CV framework that integrates generative deep learning, numerical simulation based on the lattice Boltzmann method (LBM), and interpretable machine learning to predict and explain the thermal behavior in such systems. A wide range of porous structures …

Application of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors
Vedant Sawal, Hiu Yung Wong
https://arxiv.org/abs/2509.11113 https://

Application of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors
This paper presents a machine learning-based approach to correct inference errors caused by stuck-at faults in fully analog ReRAM-based neuromorphic circuits. Using a Design-Technology Co-Optimization (DTCO) simulation framework, we model and analyze six spatial defect types-circular, circular-complement, ring, row, column, and checkerboard-across multiple layers of a multi-array neuromorphic architecture. We demonstrate that the proposed correction method, which employs a lightweight neural ne…

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Shanmuka Sadhu, Arca Baran, Preeti Pandey, Ayush Kumar
https://arxiv.org/abs/2509.11547 https://

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Machine learning has been extensively used in various applications related to eye-tracking research. Understanding eye movement is one of the most significant subsets of eye-tracking research that reveals the scanning pattern of an individual. Researchers have thoroughly analyzed eye movement data to understand various eye-tracking applications, such as attention mechanisms, navigational behavior, task understanding, etc. The outcome of traditional machine learning algorithms used for decoding …

Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials
A. Fellman, J. Byggm\"astar, F. Granberg, F. Djurabekova, K. Nordlund
https://arxiv.org/abs/2509.11813

Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials
Machine learning (ML) has become a commonplace approach in the development of interatomic potentials for molecular dynamics simulations, and its use also for radiation effect modelling is increasing. In this work, we investigate the effects of training ML potentials to density functional theory data calculated with different pseudopotentials in nickel. We look in detail at the differences that appear in radiation damage simulations. The use of a "harder" pseudopotential with semicore electrons …

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…

A Machine Learning Closure for Polymer Integral Equation Theory
Zhihao Feng, Christian T. Randolph, Tyler B. Martin, Thomas E. Gartner III
https://arxiv.org/abs/2509.11030 https…

A Machine Learning Closure for Polymer Integral Equation Theory
Polymer reference interaction site model (PRISM) theory, a descendent of Ornstein-Zernike liquid state theory, is a powerful tool to predict the structure and thermodynamics of equilibrium polymer systems, but its accuracy and applicability can be limited in some important cases. Typically, these shortcomings are traced to the analytical closure relationships used to solve the integral equations. Here, we propose a machine learning (ML)-based closure relation trained on a dataset of coarse-grai…

Vikings vs. Falcons props, picks, SportsLine Machine Learning Model AI predictions: Robinson over 65.5 yards

https://www.cbssports.com/nfl/news/vikings

Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches
Satyajeet Prabhu
https://arxiv.org/abs/2509.11241 https://arxiv.org/pdf/2509.112…

Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches
Beat and downbeat tracking, jointly referred to as Meter Tracking, is a fundamental task in Music Information Retrieval (MIR). Deep learning models have far surpassed traditional signal processing and classical machine learning approaches in this domain, particularly for Western (Eurogenetic) genres, where large annotated datasets are widely available. These systems, however, perform less reliably on underrepresented musical traditions. Carnatic music, a rich tradition from the Indian subcontin…

Bridging Structure and Activity in Nanocatalysts via Machine Learning and Global Structure Representations
Sofia Zinzani, Francesca Baletto, Kevin Rossi
https://arxiv.org/abs/2509.10985

Bridging Structure and Activity in Nanocatalysts via Machine Learning and Global Structure Representations
Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging application: predicting the mass activity of Pt nanoparticles for the electrochemical oxygen reduction reaction, estimated via a microkinetic model. Accurate models are obtained when leveraging either a nanocatalyst's structure representation accessible at the …

Early Detection of Branched Broomrape (Phelipanche ramosa) Infestation in Tomato Crops Using Leaf Spectral Analysis and Machine Learning
Mohammadreza Narimani, Alireza Pourreza, Ali Moghimi, Parastoo Farajpoor, Hamid Jafarbiglu, Mohsen B. Mesgaran
https://arxiv.org/abs/2509.12074

Early Detection of Branched Broomrape (Phelipanche ramosa) Infestation in Tomato Crops Using Leaf Spectral Analysis and Machine Learning
Branched broomrape (Phelipanche ramosa) is a chlorophyll-deficient parasitic weed that threatens tomato production by extracting nutrients from the host. We investigate early detection using leaf-level spectral reflectance (400-2500 nm) and ensemble machine learning. In a field experiment in Woodland, California, we tracked 300 tomato plants across growth stages defined by growing degree days (GDD). Leaf reflectance was acquired with a portable spectrometer and preprocessed (band denoising, 1 n…

Predicting Stellar Parameters of Massive Stars from Light Curves with Machine Learning
Rachel C. Zhang, Kaze W. K. Wong, Gonzalo Holgado, Matteo Cantiello
https://arxiv.org/abs/2509.12411

Predicting Stellar Parameters of Massive Stars from Light Curves with Machine Learning
High-resolution spectroscopic measurements of OB stars are important for understanding processes like stellar evolution, but require labor-intensive observations. In contrast, photometric missions like the Transiting Exoplanet Survey Satellite (TESS) can monitor hundreds of thousands of stars with a range of temporal resolutions, but do not provide such detailed measurements. With surveys like the Legacy Survey of Space and Time promising unprecedented photometric coverage over the next ten yea…

Enhancing Electromagnetic Calorimeter Signal Reconstruction with Machine Learning-Based Noise Discrimination
Suman Das Gupta, Shamik Ghosh, Laltu Gazi, Shubham Dutta, Alexander Ledovskoy, Satyaki Bhattacharya, Shilpi Jain
https://arxiv.org/abs/2509.11291

Enhancing Electromagnetic Calorimeter Signal Reconstruction with Machine Learning-Based Noise Discrimination
Calorimeters operating in high-radiation environments are susceptible to damage, leading to increased noise that can significantly degrade energy resolution. A common way to mitigate noise is to apply a higher energy threshold on the cells, typically set a few standard deviations above the noise level. However, this method risks discarding cells with genuine energy deposits, worsening the energy resolution. In this paper we explore various machine learning (ML) algorithms that can replace a rig…

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin, Xing Zhang, Benjamin Nebgen, Nicholas Lubbers, Justin S. Smith, Richard Messerly, Sergei Tretiak, Garnet Kin-Lic Chan, Kipton Barros
https://arxiv.org/abs/2509.10872

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly describe bond breaking and making energetics. Commonly used approaches such as Density Functional Theory (DFT) frequently fail for this task due to deficiencies that are well recognized. However, for high-fidelity approaches, creating large datasets of energies and forces for reactive processes to train machine learning …

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, Markku Juntti
https://arxiv.org/abs/2509.11725

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context, infrastructure-mounted cameras can capture rich environmental information that can facilitate beam tracking design. In this work, we develop an efficient attention-enhanced machine learning model for long-term beam tracking built upon convolutional neural networks and gated recurrent units to predict both current and …

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning
Roberto Morabito, Guanghan Wu
https://arxiv.org/abs/2509.10946 https://arxiv.o…

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning
Large Language Models (LLMs) are increasingly used to automate software generation in embedded machine learning workflows, yet their outputs often fail silently or behave unpredictably. This article presents an empirical investigation of failure modes in LLM-powered ML pipelines, based on an autopilot framework that orchestrates data preprocessing, model conversion, and on-device inference code generation. We show how prompt format, model behavior, and structural assumptions influence both succ…

Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction
Yi Lu, Aifan Ling, Chaoqun Wang, Yaxin Xu
https://arxiv.org/abs/2509.10802 https:/…

Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction
In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrat…

Machine learning analysis of Photometric data from the Dark Energy Survey
Elcio Abdalla, Filipe B. Abdalla, Alessandro Marins, Amilcar Queiroz, Rafael M. Ribeiro, Alex S. C. Souza
https://arxiv.org/abs/2508.10191

Machine learning analysis of Photometric data from the Dark Energy Survey
In order to retrieve cosmological parameters from photometric surveys, we need to estimate the distribution of the photometric redshift in the sky with excellent accuracy. We use and apply three different machine learning methods to publicly available Dark Energy Survey data release 2 (DR2): a) Artificial Neural Network for photometric redshifts (ANNz2); b) Gaussian processes for photometric redshifts (GPz); and c) Keras, a deep learning application programming interface in Python. We compare t…

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…

Reconstruction of the Equations of State (EoSs) of Compact Stars using machine and deep learning regression techniques
Ioannis Stergakis
https://arxiv.org/abs/2509.13037 https:/…

Reconstruction of the Equations of State (EoSs) of Compact Stars using machine and deep learning regression techniques
This dissertation focuses on the reconstruction of Equations of State (EoSs) describing the interior of compact stars, using modern machine learning and deep learning methods. The pipeline is based on data from mass-radius (M-R) curves, obtained by numerically solving the Tolman-Oppenheimer-Volkoff equations for a wide range of admissible EoSs. The manuscript is divided into a Theoretical Part (Chs. 1-4) and a Computational Part (Chs. 5-7). The theoretical chapters analyze the properties of neu…

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 …

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…

ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets
Andr\'es L. Su\'arez-Cetrulo, Alejandro Cervantes, David Quintana
https://arxiv.org/abs/2509.11844

ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets
Financial markets are complex, non-stationary systems where the underlying data distributions can shift over time, a phenomenon known as regime changes, as well as concept drift in the machine learning literature. These shifts, often triggered by major economic events, pose a significant challenge for traditional statistical and machine learning models. A fundamental problem in developing and validating adaptive algorithms is the lack of a ground truth in real-world financial data, making it di…

Artificial Intelligence in Breast Cancer Care: Transforming Preoperative Planning and Patient Education with 3D Reconstruction
Mustafa Khanbhai, Giulia Di Nardo, Jun Ma, Vivienne Freitas, Caterina Masino, Ali Dolatabadi, Zhaoxun "Lorenz" Liu, Wey Leong, Wagner H. Souza, Amin Madani
https://arxiv.org/abs/2509.12242

Artificial Intelligence in Breast Cancer Care: Transforming Preoperative Planning and Patient Education with 3D Reconstruction
Effective preoperative planning requires accurate algorithms for segmenting anatomical structures across diverse datasets, but traditional models struggle with generalization. This study presents a novel machine learning methodology to improve algorithm generalization for 3D anatomical reconstruction beyond breast cancer applications. We processed 120 retrospective breast MRIs (January 2018-June 2023) through three phases: anonymization and manual segmentation of T1-weighted and dynamic contras…

E-ROBOT: a dimension-free method for robust statistics and machine learning via Schr\"odinger bridge
Davide La Vecchia, Hang Liu
https://arxiv.org/abs/2509.11532 https://…

E-ROBOT: a dimension-free method for robust statistics and machine learning via Schrödinger bridge
We propose the Entropic-regularized Robust Optimal Transport (E-ROBOT) framework, a novel method that combines the robustness of ROBOT with the computational and statistical benefits of entropic regularization. We show that, rooted in the Schrödinger bridge problem theory, E-ROBOT defines the robust Sinkhorn divergence $\overline{W}_{\varepsilon,λ}$, where the parameter $λ$ controls robustness and $\varepsilon$ governs the regularization strength. Letting $n\in \mathbb{N}$ denote the sample …

Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery
Yogesh Yadav, Sandeep K Yadav, Vivek Vijay, Ambesh Dixit
https://arxiv.org/abs/2509.10532

Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery
The physical and chemical characteristics of cathodes used in batteries are derived from the lithium-ion phosphate cathodes crystalline arrangement, which is pivotal to the overall battery performance. Therefore, the correct prediction of the crystal system is essential to estimate the properties of cathodes. This study applies machine learning classification algorithms for predicting the crystal systems, namely monoclinic, orthorhombic, and triclinic, related to Li P (Mn, Fe, Co, Ni, V) O base…

Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, Tadashi Maeno, Fatih Furkan Akman, Joseph Boudreau, Sankha Dutta, Shengyu Feng, Adolfy Hoisie, Kuan-Chieh Hsu, Raees Khan, Jaehyung Kim, Ozgur O. Kilic, Scott Klasky, Alexei Klimentov, Tatiana Korchuganova, Verena Ingrid Martinez Outschoorn, Paul Nilsson, David K. Park, Norbert Podhorszki, Yihui Ren, John Rembrandt Steele, Fr\'ed\'eric Suter, Sairam Sri Vatsavai, Torre Wenaus,…

Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
The collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimat…

NFL player props, odds, lines: Week 2, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP

https://www.cbssports.com/nfl/news/nfl-pla…

Electromagnetic Simulations of Antennas on GPUs for Machine Learning Applications
Murat Temiz, Vemund Bakken
https://arxiv.org/abs/2508.10713 https://arxiv…

Electromagnetic Simulations of Antennas on GPUs for Machine Learning Applications
This study proposes an antenna simulation framework powered by graphics processing units (GPUs) based on an open-source electromagnetic (EM) simulation software (gprMax) for machine learning applications of antenna design and optimization. Furthermore, it compares the simulation results with those obtained through commercial EM software. The proposed software framework for machine learning and surrogate model applications will produce antenna data sets consisting of a large number of antenna si…

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…

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
JingChun Wang, Meenu Upadhyay, Eric D. Boittier, Kham Lek Chaton, Valerii Andreichev, Mike Devereux, Shimoni Patel, Sena Aydin, Kai T\"opfer, Markus Meuwly
https://arxiv.org/abs/2509.11639

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freed…

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…

Quantum Architecture Search for Solving Quantum Machine Learning Tasks
Michael K\"olle, Simon Salfer, Tobias Rohe, Philipp Altmann, Claudia Linnhoff-Popien
https://arxiv.org/abs/2509.11198

Quantum Architecture Search for Solving Quantum Machine Learning Tasks
Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains error-prone and limited in scale, Variational Quantum Circuits offer a noise-resilient framework suitable for today's devices. The performance of these circuits strongly depends on the underlying architecture of their parameterized quantum components. Identifying efficient, hardware-compatible quantum circuit architec…

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
Elaheh Kazemi-Khasragh, Roc\'io Mercado, Carlos Gonzalez, Maciej Haranczyk
https://arxiv.org/abs/2509.11874

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
Copolymers are highly versatile materials with a vast range of possible chemical compositions. By using computational methods for property prediction, the design of copolymers can be accelerated, allowing for the prioritization of candidates with favorable properties. In this study, we utilized two distinct representations of molecular ensembles to predict the seven different physical polymer properties copolymers using machine learning: we used a random forest (RF) model to predict polymer pro…

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…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Multi-task and few-shot learning in virtual flow metering
Kristian L{\o}vland, Bjarne Grimstad, Lars S. Imsland

Optimizing Code Embeddings and ML Classifiers for Python Source Code Vulnerability Detection
Talaya Farasat, Joachim Posegga
https://arxiv.org/abs/2509.13134 https://

Optimizing Code Embeddings and ML Classifiers for Python Source Code Vulnerability Detection
In recent years, the growing complexity and scale of source code have rendered manual software vulnerability detection increasingly impractical. To address this challenge, automated approaches leveraging machine learning and code embeddings have gained substantial attention. This study investigates the optimal combination of code embedding techniques and machine learning classifiers for vulnerability detection in Python source code. We evaluate three embedding techniques, i.e., Word2Vec, CodeBE…

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…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/6]:
- Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resoluti...
Zheng, Wang, Yuan, Ning, He, You, Yang, Zhou

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…

The Quantum Control Hierarchy: When Physics-Informed Design Meets Machine Learning
Atta ur Rahman, M. Y. Abd-Rabbou, Cong-feng Qiao
https://arxiv.org/abs/2509.12832 https://

The Quantum Control Hierarchy: When Physics-Informed Design Meets Machine Learning
We address a wide spectrum of quantum control strategies, including various open-loop protocols and advanced adaptive methods. These methodologies apply to few-qubit scenarios and naturally scale to larger N-qubit systems. We benchmark them across fundamental quantum tasks: entanglement preservation/generation, and directed quantum transport in a disordered quantum walk. All simulations are performed in a challenging environment featuring non-Markov colored noise, imperfections, and the Markov …

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…

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…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/7]:
- Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
Ruofan Lu, Yintong Huo, Meng Zhang, Yichen Li, Michael R. Lyu

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design
Yuhao Sun, Yihua Zhang, Gaowen Liu, Hongtao Xie, Sijia Liu
https://arxiv.org/abs/2508.10065

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design
With the increasing demand for the right to be forgotten, machine unlearning (MU) has emerged as a vital tool for enhancing trust and regulatory compliance by enabling the removal of sensitive data influences from machine learning (ML) models. However, most MU algorithms primarily rely on in-training methods to adjust model weights, with limited exploration of the benefits that data-level adjustments could bring to the unlearning process. To address this gap, we propose a novel approach that le…

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems
Stepan Kulibaba, Artem Dzhalilov, Roman Pakhomov, Oleg Svidchenko, Alexander Gasnikov, Aleksei Shpilman
https://arxiv.org/abs/2508.10177

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems
Recent Large Language Model (LLM)-based AutoML systems demonstrate impressive capabilities but face significant limitations such as constrained exploration strategies and a severe execution bottleneck. Exploration is hindered by one-shot methods lacking diversity and Monte Carlo Tree Search (MCTS) approaches that fail to recombine strong partial solutions. The execution bottleneck arises from lengthy code validation cycles that stifle iterative refinement. To overcome these challenges, we intro…

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…

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits
Michael K\"olle, Leonhard Klingert, Julian Sch\"onberger, Philipp Altmann, Tobias Rohe, Claudia Linnhoff-Popien
https://arxiv.org/abs/2509.11190

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits
Quantum computing is an emerging field in computer science that has seen considerable progress in recent years, especially in machine learning. By harnessing the principles of quantum physics, it can surpass the limitations of classical algorithms. However, variational quantum circuits (VQCs), which rely on adjustable parameters, often face the barren plateau phenomenon, hindering optimization. The Lottery Ticket Hypothesis (LTH) is a recent concept in classical machine learning that has led to…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/7]:
- Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex ...
Diying Yang, Yingwei Hou, Weigang Wu

Atomic-scale phase-field modeling with universal machine learning potentials
Kairi Masuda, Yu Kumagai
https://arxiv.org/abs/2509.12648 https://arxiv.org/pd…

Atomic-scale phase-field modeling with universal machine learning potentials
Atomic-scale phase-field modeling formulates the probability densities of atomic vibrations as Gaussian distributions and derives a free energy functional using variational Gaussian theory and interatomic potentials. This framework permits per-Gaussian decomposition of the free energy, providing a description of local thermodynamic states with atomic resolution. However, existing formulations are limited to classical pairwise interatomic potentials, restricting their applicability to specific m…

Some Robustness Properties of Label Cleaning
Chen Cheng, John Duchi
https://arxiv.org/abs/2509.11379 https://arxiv.org/pdf/2509.11379

Some Robustness Properties of Label Cleaning
We demonstrate that learning procedures that rely on aggregated labels, e.g., label information distilled from noisy responses, enjoy robustness properties impossible without data cleaning. This robustness appears in several ways. In the context of risk consistency -- when one takes the standard approach in machine learning of minimizing a surrogate (typically convex) loss in place of a desired task loss (such as the zero-one mis-classification error) -- procedures using label aggregation obtai…

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,…

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 …

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…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Generalized Dirichlet Energy and Graph Laplacians for Clustering Directed and Undirected Graphs
Harry Sevi, Gwendal Debaussart-Joniec, Malik Hacini, Matthieu Jonckheere, Argyris Kalogeratos

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[7/11]:
- Quantum Architecture Search for Solving Quantum Machine Learning Tasks
Michael K\"olle, Simon Salfer, Tobias Rohe, Philipp Altmann, Claudia Linnhoff-Popien

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…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- pySigLib -- Fast Signature-Based Computations on CPU and GPU
Daniil Shmelev, Cristopher Salvi

Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO$_3$
Jonathan Schmidt, Nicola A. Spaldin
https://arxiv.org/abs/2508.10735 https://

Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO$_3$
We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of $^{18}$O substitution on the properties of quantum paraelectric SrTiO$_3$ (STO). We find that calculations including both quantum and anharmonic effects are able to reproduce the experimentally observed isotope effect, in which replacement of $^{16}$O by $^{18}$O induces the ferroelectric state, and demonstrate that the ferroelectric phase transition in ST$^{18}$O ca…

Mitigating Exponential Mixed Frequency Growth through Frequency Selection and Dimensional Separation in Quantum Machine Learning
Michael Poppel, David Bucher, Maximilian Zorn, Nico Kraus, Jonas Stein, Claudia Linnhoff-Popien
https://arxiv.org/abs/2508.10533

Mitigating Exponential Mixed Frequency Growth through Frequency Selection and Dimensional Separation in Quantum Machine Learning
To leverage the potential computational speedup of quantum computing (QC), research in quantum machine learning (QML) has gained increasing prominence. Angle encoding techniques in QML models have been shown to generate truncated Fourier series, offering asymptotically universal function approximation capabilities. By selecting efficient feature maps (FMs) within quantum circuits, one can leverage the exponential growth of Fourier frequencies for improved approximation. In multi-dimensional set…

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…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Jackknife Variance Estimation for H\'ajek-Dominated Generalized U-Statistics
Jakob R. Juergens

SoK: Data Minimization in Machine Learning
Robin Staab, Nikola Jovanovi\'c, Kimberly Mai, Prakhar Ganesh, Martin Vechev, Ferdinando Fioretto, Matthew Jagielski
https://arxiv.org/abs/2508.10836

SoK: Data Minimization in Machine Learning
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulations like GDPR and CPRA. Violations of this principle have substantial real-world consequences, with regulatory actions resulting in fines reaching hundreds of millions of dollars. Notably, the relevance of data minimization is particularly pronounced in machine learning (ML) applications, which typically rely on large…

Deep Learning in Classical and Quantum Physics
Timothy Heightman, Marcin P{\l}odzie\'n
https://arxiv.org/abs/2508.10666 https://arxiv.org/pdf/2508.1066…

Deep Learning in Classical and Quantum Physics
Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic complexity of quantum systems, DL enables efficient exploration of large parameter spaces, extraction of patterns from experimental data, and data-driven guidance for research directions. These capabilities already support tasks such as refining quantum control p…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/7]:
- EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
Xu, Wang, Xu, Xu, Wang, Deng, Yao, Zheng, Chen, Zhang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Viola, Qu, Metzger, Ke, Becker, Schindler, Obukhov

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…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/7]:
- Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Ethan Goan, Dimitri Perrin, Kerrie Mengersen, Clinton Fookes

Quantum Wavefront Correction via Machine Learning for Satellite-to-Earth CV-QKD
Nathan K. Long, Ziqing Wang, Benjamin P. Dix-Matthews, Alex Frost, John Wallis, Kenneth J. Grant, Robert Malaney
https://arxiv.org/abs/2508.10326

Quantum Wavefront Correction via Machine Learning for Satellite-to-Earth CV-QKD
State-of-the-art free-space continuous-variable quantum key distribution (CV-QKD) protocols use phase reference pulses to modulate the wavefront of a real local oscillator at the receiver, thereby compensating for wavefront distortions caused by atmospheric turbulence. It is normally assumed that the wavefront distortion in these phase reference pulses is identical to the wavefront distortion in the quantum signals, which are multiplexed during transmission. However, in many real-world deployme…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- Quantized Neural Networks for Microcontrollers: A Comprehensive Review of Methods, Platforms, and...
Hamza A. Abushahla, Dara Varam, Ariel J. N. Panopio, Mohamed I. AlHajri

[2025-09-17 Wed (UTC), 2 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/7]:
- Extended UCB Policies for Multi-armed Bandit Problems
Keqin Liu, Tianshuo Zheng, Zhi-Hua Zhou

[2025-09-16 Tue (UTC), 14 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/6]:
- Quantum Noise Tomography with Physics-Informed Neural Networks
Antonin Sulc
http…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/6]:
- PeruMedQA: Benchmarking Large Language Models (LLMs) on Peruvian Medical Exams -- Dataset Constru...
Rodrigo M. Carrillo-Larco, Jesus Lov\'on Melgarejo, Manuel Castillo-Cara, Gusseppe Bravo-Rocca

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/6]:
- Predictable Compression Failures: Why Language Models Actually Hallucinate
Leon Chlon, Ahmed Karim, Maggie Chlon

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/6]:
- Physics-informed neural network solves minimal surfaces in curved spacetime
Koji Hashimoto, Koichi Kyo, Masaki Murata, Gakuto Ogiwara, Norihiro Tanahashi

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- Trial-Level Time-frequency EEG Desynchronization as a Neural Marker of Pain
D. A. Blanco-Mora, A. Dierolf, J. Gon\c{c}alves, M. van Der Meulen

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…

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.…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Machine Learning for Cloud Detection in IASI Measurements: A Data-Driven SVM Approach with Physic...
Chiara Zugarini, Cristina Sgattoni, Luca Sgheri

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…

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…

Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
Tianqi Zhao, Russa Biswas, Megha Khosla
https://arxiv.org/abs/2509.12094 http…

Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
Graph machine learning models often achieve similar overall performance yet behave differently at the node level, failing on different subsets of nodes with varying reliability. Standard evaluation metrics such as accuracy obscure these fine grained differences, making it difficult to diagnose when and where models fail. We introduce NodePro, a node profiling framework that enables fine-grained diagnosis of model behavior by assigning interpretable profile scores to individual nodes. These scor…

Quantum Machine Learning, Quantitative Trading, Reinforcement Learning, Deep Learning
Jun-Hao Chen, Yu-Chien Huang, Yun-Cheng Tsai, Samuel Yen-Chi Chen
https://arxiv.org/abs/2509.09176

Quantum Machine Learning, Quantitative Trading, Reinforcement Learning, Deep Learning
The convergence of quantum-inspired neural networks and deep reinforcement learning offers a promising avenue for financial trading. We implemented a trading agent for USD/TWD by integrating Quantum Long Short-Term Memory (QLSTM) for short-term trend prediction with Quantum Asynchronous Advantage Actor-Critic (QA3C), a quantum-enhanced variant of the classical A3C. Trained on data from 2000-01-01 to 2025-04-30 (80\% training, 20\% testing), the long-only agent achieves 11.87\% return over aroun…

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

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
[4/4]:
- Performance of universal machine-learned potentials with explicit long-range interactions in biom...
Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Mathias Niepert

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/6]:
- A Parametric Contextual Online Learning Theory of Brokerage
Fran\c{c}ois Bachoc, Tommaso Cesari, Roberto Colomboni

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

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- SegDAC: Segmentation-Driven Actor-Critic for Visual Reinforcement Learning
Alexandre Brown, Glen Berseth

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Tim R\"az
https://arxiv.org/abs/2509.11847 https://arxiv.org/pdf/2509…

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of p…

PIPES: A Meta-dataset of Machine Learning Pipelines
Cynthia Moreira Maia, Lucas B. V. de Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz
https://arxiv.org/abs/2509.09512 http…

PIPES: A Meta-dataset of Machine Learning Pipelines
Solutions to the Algorithm Selection Problem (ASP) in machine learning face the challenge of high computational costs associated with evaluating various algorithms' performances on a given dataset. To mitigate this cost, the meta-learning field can leverage previously executed experiments shared in online repositories such as OpenML. OpenML provides an extensive collection of machine learning experiments. However, an analysis of OpenML's records reveals limitations. It lacks diversity in pipeli…