Tootfinder

Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security
Pascal Debus, Maximilian Wendlinger, Kilian Tscharke, Daniel Herr, Cedric Br\"ugmann, Daniel Ohl de Mello, Juris Ulmanis, Alexander Erhard, Arthur Schmidt, Fabian Petsch
https://arxiv.org/abs/2507.08623

Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security
Quantum Machine Learning (QML) systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers of quantum computing. Despite a growing body of research on individual attack vectors - ranging from adversarial poisoning and evasion to circuit-level backdoors, side-channel leakage, and model extraction - these threats are often analyzed in isolation, with unrealistic assumptions about attacker capabilities and syst…

Enhance the machine learning algorithm performance in phishing detection with keyword features
Zijiang Yang
https://arxiv.org/abs/2508.09765 https://arxiv.…

Enhance the machine learning algorithm performance in phishing detection with keyword features
Recently, we can observe a significant increase of the phishing attacks in the Internet. In a typical phishing attack, the attacker sets up a malicious website that looks similar to the legitimate website in order to obtain the end-users' information. This may cause the leakage of the sensitive information and the financial loss for the end-users. To avoid such attacks, the early detection of these websites' URLs is vital and necessary. Previous researchers have proposed many machine learning a…

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

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

Dead Zone of Accountability: Why Social Claims in Machine Learning Research Should Be Articulated and Defended
Tianqi Kou, Dana Calacci, Cindy Lin
https://arxiv.org/abs/2508.08739

Dead Zone of Accountability: Why Social Claims in Machine Learning Research Should Be Articulated and Defended
Many Machine Learning research studies use language that describes potential social benefits or technical affordances of new methods and technologies. Such language, which we call "social claims", can help garner substantial resources and influence for those involved in ML research and technology production. However, there exists a gap between social claims and reality (the claim-reality gap): ML methods often fail to deliver the claimed functionality or social impacts. This paper investigates …

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…

Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets
Jeremy Proz, Martin Huber
https://arxiv.org/abs/2508.09885 https://

Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets
Collusion and capacity withholding in electricity wholesale markets are important mechanisms of market manipulation. This study applies a refined machine learning-based cartel detection algorithm to two cartel cases in the Italian electricity market and evaluates its out-of-sample performance. Specifically, we consider an ensemble machine learning method that uses statistical screens constructed from the offer price distribution as predictors for the incidence of collusion among electricity pro…

Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
Khashayar Namdar, Pin-Chien Wang, Tushar Raju, Steven Zheng, Fiona Li, Safwat Tahmin Khan
https://arxiv.org/abs/2509.09127

Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
Anti-money laundering (AML) actions and measurements are among the priorities of financial institutions, for which machine learning (ML) has shown to have a high potential. In this paper, we propose a comprehensive and systematic approach for developing ML pipelines to identify high-risk bank clients in a dataset curated for Task 1 of the University of Toronto 2023-2024 Institute for Management and Innovation (IMI) Big Data and Artificial Intelligence Competition. The dataset included 195,789 c…

#Springer Nature book on #machinelearning is full of made-up #citations

Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
https://arxiv.org/abs/2508.09370

Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Low-mass stars and brown dwarfs -- spectral types (SpTs) M0 and later -- play a significant role in studying stellar and substellar processes and demographics, reaching down to planetary-mass objects. Currently, the classification of these sources remains heavily reliant on visual inspection of spectral features, equivalent width measurements, or narrow-/wide-band spectral indices. Recent advances in machine learning (ML) methods offer automated approaches for spectral typing, which are becomin…

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
Reihaneh Karimi, Mahdiyar Mousavi-Sadr, Mohammad H. Zhoolideh Haghighi, Fatemeh S. Tabatabaei
https://arxiv.org/abs/2508.09689

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series photometric dataset from the Kepler Space Telescope, comprising 3,198 flux measurements across 5,074 stars. We investigate the performance of four supervised classification algorithms, namely Random Forest, k-Nearest Neighbors (KNN), Decision Tree, and Logisti…

That’s not necessarily the only way to use machine learning to assist coding. It is, however, one of the “magic diet pills” promises at the heart of the current hype bubble.
Whatever the dev tools of the future look like, until we have Lieutenant-Commander Data, difficult human thought will be at the heart of writing code.
6/

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
Mark E. Turiansky, John L. Lyons, Noam Bernstein
https://arxiv.org/abs/2508.09113 https://

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
The optical properties of defects in solids produce rich physics, from gemstone coloration to single-photon emission for quantum networks. Essential to describing optical transitions is electron-phonon coupling, which can be predicted from first principles but requires computationally expensive evaluation of all phonon modes in simulation cells containing hundreds of atoms. We demonstrate that this bottleneck can be overcome using machine learning interatomic potentials with negligible accuracy…

Exploring Molecular Odor Taxonomies for Structure-based Odor Predictions using Machine Learning
Akshay Sajan, Stijn Sluis, Reza Haydarlou, Sanne Abeln, Pasquale Lisena, Raphael Troncy, Caro Verbeek, Inger Leemans, Halima Mouhib
https://arxiv.org/abs/2508.09217

Exploring Molecular Odor Taxonomies for Structure-based Odor Predictions using Machine Learning
One of the key challenges to predict odor from molecular structure is unarguably our limited understanding of the odor space and the complexity of the underlying structure-odor relationships. Here, we show that the predictive performance of machine learning models for structure-based odor predictions can be improved using both, an expert and a data-driven odor taxonomy. The expert taxonomy is based on semantic and perceptual similarities, while the data-driven taxonomy is based on clustering co…

Performance Benchmarking of Machine Learning Models for Terahertz Metamaterial Absorber Prediction
Nafisa Anjum, Robiul Hasan
https://arxiv.org/abs/2508.08611 https://

Performance Benchmarking of Machine Learning Models for Terahertz Metamaterial Absorber Prediction
This study presents a polarization-insensitive ultra-broadband terahertz metamaterial absorber based on vanadium dioxide (VO2) and evaluates machine learning methods for predicting its absorption performance. The structure consists of a VO2 metasurface, a MF2 dielectric spacer, and a gold ground plane. It achieves more than 90% absorption between 5.72 and 11.11 THz, covering a 5.38 THz bandwidth with an average absorptance of 98.15%. A dataset of 9,018 samples was generated from full-wave simul…

Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Zhanghan Wang, Ding Ding, Hang Zhu, Haibin Lin, Aurojit Panda
https://arxiv.org/abs/2508.09505

Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU. Distributed models are generally produced by programmers who take a sequential model specification and apply several distribution strategies to distribute state and computation across GPUs. Unfortunately, bugs can be introduced in the process, and a distributed model implementation's outputs might differ from the sequential model'…

Ironically eponymous.
Springer Nature Book on Machine Learning is Full of Made-up Citations
'Based on a tip from a reader, we checked 18 of the 46 citations in the book. Two-thirds of them either did not exist or had substantial errors. And three researchers cited in the book confirmed the works they supposedly authored were fake or the citation contained substantial errors.'

Springer Nature book on machine learning is full of made-up citations
Would you pay $169 for an introductory ebook on machine learning with citations that appear to be made up? If not, you might want to pass on purchasing Mastering Machine Learning: From Basics to Ad…

Vector-Centric Machine Learning Systems: A Cross-Stack Approach
Wenqi Jiang
https://arxiv.org/abs/2508.08469 https://arxiv.org/pdf/2508.08469

Vector-Centric Machine Learning Systems: A Cross-Stack Approach
Today, two major trends are shaping the evolution of ML systems. First, modern AI systems are becoming increasingly complex, often integrating components beyond the model itself. A notable example is Retrieval-Augmented Generation (RAG), which incorporates not only multiple models but also vector databases, leading to heterogeneity in both system components and underlying hardware. Second, with the end of Moore's Law, achieving high system efficiency is no longer feasible without accounting for…

Debiased machine learning for combining probability and non-probability survey data
Shaun Seaman
https://arxiv.org/abs/2508.08948 https://arxiv.org/pdf/250…

Debiased machine learning for combining probability and non-probability survey data
We consider the problem of estimating the finite population mean $\bar{Y}$ of an outcome variable $Y$ using data from a nonprobability sample and auxiliary information from a probability sample. Existing double robust (DR) estimators of this mean $\bar{Y}$ require the estimation of two nuisance functions: the conditional probability of selection into the nonprobability sample given covariates $X$ that are observed in both samples, and the conditional expectation of $Y$ given $X$. These nuisance…

Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters
Muaz Al Halabi (ed), Marcel Bajdel (ed), Jeroen Peter Bormans (ed), Hannah Bossi (ed), Maria Calmon Behling (ed), Florian Ehmann (ed), Niklas Gotz (ed), Jerome Jung (ed), Rafet Kavak (ed), Mario Kr\"uger (ed), Robin Lakos (ed), Annemarie Lauterbach (ed), Patrick Mccormack (ed), Akhil Mithran (ed), Daniel Murnane (ed), Mathis Nolte (ed), Tim Rogoschinski (ed), Jan Scharf (ed), Oddharak Tyagi (ed), Liv V{\aa}ge (e…

Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters
In both high-energy physics and industry applications, a crowd-sourced approach to difficult problems is becoming increasingly common. These innovative approaches are ideal for the development of future facilities where the simulations can be publicly distributed, such as the Electron-Ion Collider (EIC). In this paper, we discuss a so-called ``Power Week" where graduate students were able to learn about machine learning while also contributing to an unsolved problem at a future facility. Here, …

Mitigating Language Barriers in Education: Developing Multilingual Digital Learning Materials with Machine Translation
Lucie Pol\'akov\'a, Martin Popel, V\v{e}ra Kloudov\'a, Michal Nov\'ak, Mariia Anisimova, Ji\v{r}\'i Balhar
https://arxiv.org/abs/2509.09473

Mitigating Language Barriers in Education: Developing Multilingual Digital Learning Materials with Machine Translation
The EdUKate project combines digital education, linguistics, translation studies, and machine translation to develop multilingual learning materials for Czech primary and secondary schools. Launched through collaboration between a major Czech academic institution and the country's largest educational publisher, the project is aimed at translating up to 9,000 multimodal interactive exercises from Czech into Ukrainian, English, and German for an educational web portal. It emphasizes the developme…

https://snarxiv.org/vs-arxiv/
This needs a version for the computational linguistics/machine-learning quartier of arXiv
I'm guessing at about 60% accurate in the string theory/supercollider / early time physics papers there, "better than a monkey" as the robot tells me
Via/blame

Machine Learning for Evolutionary Graph Theory
Guoli Yang, Matteo Cavaliere, Mingtao Zhang, Giovanni Masala, Adam Miles, Mengzhu Wang
https://arxiv.org/abs/2507.08363

Machine Learning for Evolutionary Graph Theory
The stability of communities - whether biological, social, economic, technological or ecological depends on the balance between cooperation and cheating. While cooperation strengthens communities, selfish individuals, or "cheaters," exploit collective benefits without contributing. If cheaters become too prevalent, they can trigger the collapse of cooperation and of the community, often in an abrupt manner. A key challenge is determining whether the risk of such a collapse can be detected in ad…

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Morteza Kimiaei, Vyacheslav Kungurtsev, Brian Olimba
https://arxiv.org/abs/2508.06906

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Integer and mixed-integer nonlinear programming (INLP, MINLP) are central to logistics, energy, and scheduling, but remain computationally challenging. This survey examines how machine learning and reinforcement learning can enhance exact optimization methods - particularly branch-and-bound (BB), without compromising global optimality. We cover discrete, continuous, and mixed-integer formulations, and highlight applications such as crew scheduling, vehicle routing, and hydropower planning. We i…

Wir wollen im @… mit annif https://annif.org/ experimentieren und suchen dafür Verstärkung im Team:

5G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AI
Joseph H. R. Isaac, Harish Saradagam, Nallamothu Pardhasaradhi
https://arxiv.org/abs/2508.09152

5G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AI
With the advent of 5G networks and technologies, ensuring the integrity and performance of packet core traffic is paramount. During network analysis, test files such as Packet Capture (PCAP) files and log files will contain errors if present in the system that must be resolved for better overall network performance, such as connectivity strength and handover quality. Current methods require numerous person-hours to sort out testing results and find the faults. This paper presents a novel AI/ML-…

Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Chiara Faccio, Margherita Porcelli, Francesco Rinaldi, Martin Stoll
https://arxiv.org/abs/2508.09738

Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Graph-based machine learning has seen an increased interest over the last decade with many connections to other fields of applied mathematics. Learning based on partial differential equations, such as the phase-field Allen-Cahn equation, allows efficient handling of semi-supervised learning approaches on graphs. The numerical solution of the graph Allen-Cahn equation via a convexity splitting or the Merriman-Bence-Osher (MBO) scheme, albeit being a widely used approach, requires the calculation…

Learning the bulk and interfacial physics of liquid-liquid phase separation
Silas Robitschko, Florian Samm\"uller, Matthias Schmidt, Robert Evans
https://arxiv.org/abs/2507.08395

Learning the bulk and interfacial physics of liquid-liquid phase separation
We use simulation-based supervised machine learning and classical density functional theory to investigate bulk and interfacial phenomena associated with phase coexistence in binary mixtures. For a prototypical symmetrical Lennard-Jones mixture our trained neural density functional yields accurate liquid-liquid and liquid-vapour binodals together with predictions for the variation of the associated interfacial tensions across the entire fluid phase diagram. From the latter we determine the cont…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/6]:
- LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning
Han Yu, Huiyuan Yang, Akane Sano

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Uncertainty propagation in feed-forward neural network models
Jeremy Diamzon, Daniele Venturi

Knowledge-guided machine learning for disentangling Pacific sea surface temperature variability across timescales
Kyle J. C. Hall, Maria J. Molina, Emily F. Wisinski, Gerald A. Meehl, Antonietta Capotondi
https://arxiv.org/abs/2508.08490

Knowledge-guided machine learning for disentangling Pacific sea surface temperature variability across timescales
Global weather patterns and regimes are heavily influenced by the dominant modes of Pacific sea surface temperature (SST) variability, including the El Niño-Southern Oscillation (ENSO), Tropical Pacific Decadal Variability (TPDV), North Pacific Meridional Mode (NPMM), and the Pacific Decadal Oscillation (PDO). However, separating these modes of variability remains challenging due to their spatial overlap and possible nonlinear coupling, which violates the assumptions of traditional linear meth…

HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data
Kaitao Lai
https://arxiv.org/abs/2507.08058 https://

HybridQC: Machine Learning-Augmented Quality Control for Single-Cell RNA-seq Data
HybridQC is an R package that streamlines quality control (QC) of single-cell RNA sequencing (scRNA-seq) data by combining traditional threshold-based filtering with machine learning-based outlier detection. It provides an efficient and adaptive framework to identify low-quality cells in noisy or shallow-depth datasets using techniques such as Isolation Forest, while remaining compatible with widely adopted formats such as Seurat objects. The package is lightweight, easy to install, and suita…

MCTED: A Machine-Learning-Ready Dataset for Digital Elevation Model Generation From Mars Imagery
Rafa{\l} Osadnik, Pablo G\'omez, Eleni Bohacek, Rickbir Bahia
https://arxiv.org/abs/2509.08027

MCTED: A Machine-Learning-Ready Dataset for Digital Elevation Model Generation From Mars Imagery
This work presents a new dataset for the Martian digital elevation model prediction task, ready for machine learning applications called MCTED. The dataset has been generated using a comprehensive pipeline designed to process high-resolution Mars orthoimage and DEM pairs from Day et al., yielding a dataset consisting of 80,898 data samples. The source images are data gathered by the Mars Reconnaissance Orbiter using the CTX instrument, providing a very diverse and comprehensive coverage of the …

A Scalable Machine Learning Approach Enabled RIS Optimization with Implicit Channel Estimation
Bile Peng, Vahid Jamali, Eduard Jorswieck
https://arxiv.org/abs/2508.07265 https:/…

A Scalable Machine Learning Approach Enabled RIS Optimization with Implicit Channel Estimation
The reconfigurable intelligent surface (RIS) is considered as a key enabler of the next-generation mobile radio systems. While attracting extensive interest from academia and industry due to its passive nature and low cost, scalability of RIS elements and requirement for channel state information (CSI) are two major difficulties for the RIS to become a reality. In this work, we introduce an unsupervised machine learning (ML) enabled optimization approach to configure the RIS. The dedicated neur…

Machine Learning Confirms GW231123 is a "Lite" Intermediate Mass Black Hole Merger
Chayan Chatterjee, Kaylah McGowan, Suyash Deshmukh, Karan Jani
https://arxiv.org/abs/2509.09161

Machine Learning Confirms GW231123 is a "Lite" Intermediate Mass Black Hole Merger
The LIGO-Virgo-KAGRA Collaboration recently reported GW231123, a black hole merger with total mass of around 190-265 solar mass. This event adds to the growing evidence of "lite" intermediate mass black hole (IMBH) discoveries of post-merger black holes >100 solar mass. GW231123 posed several data analysis challenges owing to waveform-model systematics and presence of noise artifacts called glitches. We present the first comprehensive machine learning analysis to further validate this event, st…

Subsampling Factorization Machine Annealing
Yusuke Hama, Tadashi Kadowaki
https://arxiv.org/abs/2508.08778 https://arxiv.org/pdf/2508.08778

Subsampling Factorization Machine Annealing
Quantum computing and machine learning are state-of-the-art technologies which have been investigated intensively in both academia and industry. The hybrid technology of these two ingredients is expected to be a powerful tool to solve complex problems in many branches of science and engineering such as combinatorial optimization problems and accelerate the creation of next-generation technologies. In this work, we develop an algorithm to solve a black-box optimization problem by improving Facto…

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Abdul Basit, Maha Nawaz, Saim Rehman, Muhammad Shafique
https://arxiv.org/abs/2508.07731

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action prediction, performed in real time on edge AI hardware. Achieving this on resource-constrained devices presents challenges in balancing model complexity, computational efficiency, and latency. We present CognitiveArm, an EEG-driven, brain-controlled prosthetic system implemented on embedded AI hardware, achieving real-t…

Commanders vs. Packers NFL props, SportsLine Machine Learning Model AI picks: Jordan Love Over 223.5 passing

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

"Springer Nature book on machine learning is full of made-up citations" The book," Mastering Machine Learning: From Basics to Advanced", costs $169.
Yet more reason to be skeptical about #AI salespeople.

Springer Nature book on machine learning is full of made-up citations
Would you pay $169 for an introductory ebook on machine learning with citations that appear to be made up? If not, you might want to pass on purchasing Mastering Machine Learning: From Basics to Ad…

Leveraging Large Language Models for Classifying App Users' Feedback
Yasaman Abedini, Abbas Heydarnoori
https://arxiv.org/abs/2507.08250 https://

Leveraging Large Language Models for Classifying App Users' Feedback
In recent years, significant research has been conducted into classifying application (app) user feedback, primarily relying on supervised machine learning algorithms. However, fine-tuning more generalizable classifiers based on existing labeled datasets remains an important challenge, as creating large and accurately labeled datasets often requires considerable time and resources. In this paper, we evaluate the capabilities of four advanced LLMs, including GPT-3.5-Turbo, GPT-4, Flan-T5, and Ll…

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…

Machines Learn Number Fields, But How? The Case of Galois Groups
Kyu-Hwan Lee, Seewoo Lee
https://arxiv.org/abs/2508.06670 https://arxiv.org/pdf/2508.06670…

Machines Learn Number Fields, But How? The Case of Galois Groups
By applying interpretable machine learning methods such as decision trees, we study how simple models can classify the Galois groups of Galois extensions over $\mathbb{Q}$ of degrees 4, 6, 8, 9, and 10, using Dedekind zeta coefficients. Our interpretation of the machine learning results allows us to understand how the distribution of zeta coefficients depends on the Galois group, and to prove new criteria for classifying the Galois groups of these extensions. Combined with previous results, thi…

A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme
Simon R. Goode, Sara A. Webb, Jeff Cooke, Jielai Zhang, James Freeburn, Amy Lien, Mohsen Shamohammadi, Alexandra Rosenthal, Laura N. Driessen, Christopher Fluke, Ashish Mahabal, Anais M\"oller, Dougal Dobie, Adam Batten, Natasha Van Bemmel
h…

A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme
Optical transient surveys continue to generate increasingly large datasets, prompting the introduction of machine-learning algorithms to search for quality transient candidates efficiently. Existing machine-learning infrastructure can be leveraged in novel ways to search these datasets for new classes of transients. We present a machine-learning accelerated search pipeline for the Deeper, Wider, Faster (DWF) programme designed to identify high-quality astrophysical transient candidates that con…

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Bruno Alexandre Rosa, Hil\'ario Oliveira, Luiz Rodrigues, Eduardo Araujo Oliveira, Rafael Ferreira Mello
https://arxiv.org/abs/2507.08487

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Essays are considered a valuable mechanism for evaluating learning outcomes in writing. Textual cohesion is an essential characteristic of a text, as it facilitates the establishment of meaning between its parts. Automatically scoring cohesion in essays presents a challenge in the field of educational artificial intelligence. The machine learning algorithms used to evaluate texts generally do not consider the individual characteristics of the instances that comprise the analysed corpus. In this…

Making Effective Decisions: Machine Learning and the Ecogame in 1970
Catherine Mason
https://arxiv.org/abs/2508.07027 https://arxiv.org/pdf/2508.07027

Making Effective Decisions: Machine Learning and the Ecogame in 1970
This paper considers Ecogame, an innovative art project of 1970, whose creators believed in a positive vision of a technological future; an understanding, posited on cybernetics, of a future that could be participatory via digital means, and therefore more democratised. Using simulation and early machine learning techniques over a live network, Ecogame combined the power of visual art with cybernetic concepts of adaptation, feedback, and control to propose that behaviour had implications for th…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Thompson Exploration with Best Challenger Rule in Best Arm Identification
Jongyeong Lee, Junya Honda, Masashi Sugiyama

Collective Communication Profiling of Modern-day Machine Learning Workloads
Jit Gupta, Andrew Li, Tarun Banka, Ariel Cohen, T. Sridhar, Raj Yavatkar
https://arxiv.org/abs/2507.07117

Collective Communication Profiling of Modern-day Machine Learning Workloads
Machine Learning jobs, carried out on large number of distributed high performance systems, involve periodic communication using operations like AllReduce, AllGather, and Broadcast. These operations may create high bandwidth and bursty traffic patterns, leading to network congestion and packet loss, thus impacting the performance of these jobs. Hence it is imperative to analyze these patterns, which can be helpful in provisioning network resources depending on the type of machine learning workl…

RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach
Bile Peng, Karl-Ludwig Besser, Shanpu Shen, Finn Siegismund-Poschmann, Ramprasad Raghunath, Daniel M. Mittleman, Vahid Jamali, Eduard A. Jorswieck
https://arxiv.org/abs/2508.07909

RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach
Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfigurable intelligent surfaces (RISs). In this paper, we jointly optimize base station (BS) precoding and RIS configuration with unsupervised machine learning (ML), which looks for the optimal solution autonomously. In particular, we propose a dedicated neural network (NN) architecture RISnet inspired by domain knowledge…

PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets
Bartlomiej Chybowski, Shima Abdullateef, Hollan Haule, Alfredo Gonzalez-Sulser, Javier Escudero
https://arxiv.org/abs/2508.07253

PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets
Reliable seizure detection is critical for diagnosing and managing epilepsy, yet clinical workflows remain dependent on time-consuming manual EEG interpretation. While machine learning has shown promise, existing approaches often rely on dataset-specific optimisations, limiting their real-world applicability and reproducibility. Here, we introduce an innovative, open-source machine-learning framework that enables robust and generalisable seizure detection across varied clinical datasets. We eva…

Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study
Anna Rosenberg, John Kennedy, Zohar Keidar, Yehoshua Y. Zeevi, Guy Gilboa
https://arxiv.org/abs/2507.08735 …

Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study
Solving computer vision problems through machine learning, one often encounters lack of sufficient training data. To mitigate this we propose the use of ensembles of weak learners based on spectral total-variation (STV) features (Gilboa 2014). The features are related to nonlinear eigenfunctions of the total-variation subgradient and can characterize well textures at various scales. It was shown (Burger et-al 2016) that, in the one-dimensional case, orthogonal features are generated, whereas in…

Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Benjamin Laufer, Hamidah Oderinwale, Jon Kleinberg
https://arxiv.org/abs/2508.06811 https://

Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Many have observed that the development and deployment of generative machine learning (ML) and artificial intelligence (AI) models follow a distinctive pattern in which pre-trained models are adapted and fine-tuned for specific downstream tasks. However, there is limited empirical work that examines the structure of these interactions. This paper analyzes 1.86 million models on Hugging Face, a leading peer production platform for model development. Our study of model family trees -- networks th…

Optimal Autonomous MLIP Dataset Building
Vincent G. Fletcher, Albert P. Bart\'ok, Livia B. P\'artay
https://arxiv.org/abs/2508.08864 https://arxiv.…

Optimal Autonomous MLIP Dataset Building
We propose a novel approach for constructing training databases for Machine Learning Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely appealing due to its ease of automation, its suitability for iterative learning, and its independence from prior knowledge of stable phases, avoiding bias towards pre-existing structural data. The approach uses Nested Sampling (NS) to explore the configuration space…

Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
Andrea Ponte, Luca Demetrio, Luca Oneto, Ivan Tesfai Ogbu, Battista Biggio, Fabio Roli
https://arxiv.org/abs/2508.09652

Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined in isolation, missing opportunities to reduce data complexity and strengthen defenses against adversarial EXEmples, carefully crafted programs designed to evade detection. Hence, in this work we investigate the influence that signature-based detection exerts on model training, when they are included inside the training…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- fastkqr: A Fast Algorithm for Kernel Quantile Regression
Qian Tang, Yuwen Gu, Boxiang Wang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data C...
Bill Tang, \c{C}a\u{g}{\i}l Ko\c{c}yi\u{g}it, Eric Rice, Phebe Vayanos

Virtual Observatory and machine learning for the study of low-mass objects in photometric and spectroscopic surveys
Pedro Mas-Buitrago
https://arxiv.org/abs/2508.07998 https://

Virtual Observatory and machine learning for the study of low-mass objects in photometric and spectroscopic surveys
Low-mass objects are ubiquitous in our Galaxy. Their low temperature provides them with complex atmospheres characterised by the presence of strong molecular absorption bands which, together with their faintness, have made their accurate characterisation a great challenge for astronomers over the last decades. M dwarfs account for 75% of the census of stars within 10 pc of the Sun, and their suitability as targets in the search for Earth-like planets has led many research groups to focus on the…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- On learning functions over biological sequence space: relating Gaussian process priors, regulariz...
Samantha Petti, Carlos Mart\'i-G\'omez, Justin B. Kinney, Juannan Zhou, David M. McCandlish

Enhancing the Scalability of Classical Surrogates for Real-World Quantum Machine Learning Applications
Philip Anton Hernicht, Alona Sakhnenko, Corey O'Meara, Giorgio Cortiana, Jeanette Miriam Lorenz
https://arxiv.org/abs/2508.06131

Enhancing the Scalability of Classical Surrogates for Real-World Quantum Machine Learning Applications
Quantum machine learning (QML) presents potential for early industrial adoption, yet limited access to quantum hardware remains a significant bottleneck for deployment of QML solutions. This work explores the use of classical surrogates to bypass this restriction, which is a technique that allows to build a lightweight classical representation of a (trained) quantum model, enabling to perform inference on entirely classical devices. We reveal prohibiting high computational demand associated wit…

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
David Schlangen, Sherzod Hakimov, Jonathan Jordan, Philipp Sadler
https://arxiv.org/abs/2507.08491

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation. The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available. The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose r…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Expe...
Qianglin Wen, Chengchun Shi, Yang Ying, Niansheng Tang, Hongtu Zhu

Bears vs. Vikings NFL props, odds, SportsLine Machine Learning Model AI prediction: Williams under 218.5 yards

https://www.cbssports.com/nfl/news/bears-v

Machine Learning-driven Multiscale MD Workflows: The Mini-MuMMI Experience
Lo\"ic Pottier, Konstantia Georgouli, Timothy S. Carpenter, Fikret Aydin, Jeremy O. B. Tempkin, Dwight V. Nissley, Frederick H. Streitz, Thomas R. W. Scogland, Peer-Timo Bremer, Felice C. Lightstone, Helgi I. Ing\'olfsson
https://arxiv.org/abs/25…

Machine Learning-driven Multiscale MD Workflows: The Mini-MuMMI Experience
Computational models have become one of the prevalent methods to model complex phenomena. To accurately model complex interactions, such as detailed biomolecular interactions, scientists often rely on multiscale models comprised of several internal models operating at difference scales, ranging from microscopic to macroscopic length and time scales. Bridging the gap between different time and length scales has historically been challenging but the advent of newer machine learning (ML) approache…

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering
Junling Fan, David Koblah, Domenic Forte
https://arxiv.org/abs/2508.08462 ht…

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering
Integrated circuits (ICs) are essential to modern electronic systems, yet they face significant risks from physical reverse engineering (RE) attacks that compromise intellectual property (IP) and overall system security. While IC camouflage techniques have emerged to mitigate these risks, existing approaches largely focus on localized gate modifications, neglecting comprehensive deception strategies. To address this gap, we present a machine learning (ML)-driven methodology that integrates cryp…

Processing of synthetic data in AI development for healthcare and the definition of personal data in EU law
Vibeke Binz Vallevik, Anne Kjersti C. Befring, Severin Elvatun, Jan Franz Nygaard
https://arxiv.org/abs/2508.08353

Processing of synthetic data in AI development for healthcare and the definition of personal data in EU law
Artificial intelligence (AI) has the potential to transform healthcare, but it requires access to health data. Synthetic data that is generated through machine learning models trained on real data, offers a way to share data while preserving privacy. However, uncertainties in the practical application of the General Data Protection Regulation (GDPR) create an administrative burden, limiting the benefits of synthetic data. Through a systematic analysis of relevant legal sources and an empirical …

Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning
Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera, Ruben Tolosana, Javier Ortega-Garcia
https://arxiv.org/abs/2509.07879

Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning
Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a novel multitask learning process that involves training simultaneously two models: the original or Audited Model, and a secondary model, referred to as the MINT Model, responsible for identifying the data used for training the Audited Model. This novel multi-task learning approach has been designed to incorporate the au…

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
Xenia Konti, Yi Shen, Zifan Wang, Karl Henrik Johansson, Michael J. Pencina, Nicoleta J. Economou-Zavlanos, Michael M. Zavlanos
https://arxiv.org/abs/2509.08942

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
The performance of machine learning (ML) models critically depends on the quality and representativeness of the training data. In applications with multiple heterogeneous data generating sources, standard ML methods often learn spurious correlations that perform well on average but degrade performance for atypical or underrepresented groups. Prior work addresses this issue by optimizing the worst-group performance. However, these approaches typically assume that the underlying data distribution…

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
Georg Kruse, Rodrigo Coelho, Andreas Rosskopf, Robert Wille, Jeanette Miriam Lorenz
https://arxiv.org/abs/2507.07593

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
At the interception between quantum computing and machine learning, Quantum Reinforcement Learning (QRL) has emerged as a promising research field. Due to its novelty, a standardized and comprehensive collection for QRL algorithms has not yet been established. Researchers rely on numerous software stacks for classical Reinforcement Learning (RL) as well as on various quantum computing frameworks for the implementation of the quantum subroutines of their QRL algorithms. Inspired by the CleanRL l…

A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management
Julian Oelhaf, Georg Kordowich, Mehran Pashaei, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
https://arxiv.org/abs/2509.09053

A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management
The integration of renewable and distributed energy resources reshapes modern power systems, challenging conventional protection schemes. This scoping review synthesizes recent literature on machine learning (ML) applications in power system protection and disturbance management, following the PRISMA for Scoping Reviews framework. Based on over 100 publications, three key objectives are addressed: (i) assessing the scope of ML research in protection tasks; (ii) evaluating ML performance across …

NFL player props, odds, bets: Week 1, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP

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

Topological Machine Learning with Unreduced Persistence Diagrams
Nicole Abreu, Parker B. Edwards, Francis Motta
https://arxiv.org/abs/2507.07156 https://…

Topological Machine Learning with Unreduced Persistence Diagrams
Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persistence diagram. Computing persistence diagrams is often the most computationally demanding step in such a pipeline, however. To explore this, we introduce several methods to generate topological feature vectors from unreduced boundary matrices. We compared the performance of pipelines trained on vectorizations of unred…

Quantum Algorithms for Projection-Free Sparse Convex Optimization
Jianhao He, John C. S. Lui
https://arxiv.org/abs/2507.08543 https://

Quantum Algorithms for Projection-Free Sparse Convex Optimization
This paper considers the projection-free sparse convex optimization problem for the vector domain and the matrix domain, which covers a large number of important applications in machine learning and data science. For the vector domain $\mathcal{D} \subset \mathbb{R}^d$, we propose two quantum algorithms for sparse constraints that finds a $\varepsilon$-optimal solution with the query complexity of $O(\sqrt{d}/\varepsilon)$ and $O(1/\varepsilon)$ by using the function value oracle, reducing a fa…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
Liu, Du, Yang, Wu, Wang, Hu, Wang, Liu, Sun, Boukerche, Leung

[2025-08-14 Thu (UTC), 4 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
[2/4]:
- Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Mad...
Khadir, Stevenson, Li, Krick, Burrows, Hall, Posey, Shen

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- Tame Riemannian Stochastic Approximation
Johannes Aspman, Vyacheslav Kungurtsev, Reza Roohi Seraji

[2025-08-13 Wed (UTC), 5 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
[6/6]:
- Representation biases: will we achieve complete understanding by analyzing representations?
Andrew Kyle Lampinen, Stephanie C. Y. Chan, Yuxuan Li, Katherine Hermann

[2025-07-14 Mon (UTC), 6 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
[5/6]:
- Cryo-em images are intrinsically low dimensional
Luke Evans, Octavian-Vlad Murad, Lars Dingeldein, Pilar Cossio, Roberto Covino, Marina Meila

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/6]:
- Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
Pascal Fran\c{c}ois, Genevi\`eve Gauthier, Fr\'ed\'eric Godin, Carlos Octavio P\'erez Mendoza

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/6]:
- Audio-3DVG: Unified Audio -- Point Cloud Fusion for 3D Visual Grounding
Cao-Dinh, Le-Duc, Dao, Tat, Ngo, Nguyen, Khanh, Nguyen-Tang

Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
Joseph Paillard, Antoine Collas, Denis A. Engemann, Bertrand Thirion
https://arxiv.org/abs/2508.08724

Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
Recent advances in machine learning have greatly expanded the repertoire of predictive methods for medical imaging. However, the interpretability of complex models remains a challenge, which limits their utility in medical applications. Recently, model-agnostic methods have been proposed to measure conditional variable importance and accommodate complex non-linear models. However, they often lack power when dealing with highly correlated data, a common problem in medical imaging. We introduce H…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inferenc...
Jialin Zhao, Yingtao Zhang, Carlo Vittorio Cannistraci

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Pa...
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer, Chetra Mang, Mouadh Yagoubi

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Data-driven system identification using quadratic embeddings of nonlinear dynamics
Stefan Klus, Joel-Pascal Ntwali N'konzi

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Answer Generation for Questions With Multiple Information Sources in E-Commerce
Anand A. Rajasekar, Nikesh Garera

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma

Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning
Mominul Rubel, Adam Meyers, Gabriel Nicolosi
https://arxiv.org/abs/2509.08759

Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning
We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, amplitudes, and phase shifts of the series as trainable parameters. This design allows the model to create a problem-specific spectral basis adaptable to both periodic and nonperiodic functions. Unlike previous Fourier-inspired NN models, the …

Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Daniel Richards Arputharaj, Charlotte Rodriguez, Angelo Rodio, Giovanni Neglia
https://arxiv.org/abs/2509.08980

Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Training large-scale machine learning models incurs substantial carbon emissions. Federated Learning (FL), by distributing computation across geographically dispersed clients, offers a natural framework to leverage regional and temporal variations in Carbon Intensity (CI). This paper investigates how to reduce emissions in FL through carbon-aware client selection and training scheduling. We first quantify the emission savings of a carbon-aware scheduling policy that leverages slack time -- perm…

Strategic Incentivization for Locally Differentially Private Federated Learning
Yashwant Krishna Pagoti, Arunesh Sinha, Shamik Sural
https://arxiv.org/abs/2508.07138 https://

Strategic Incentivization for Locally Differentially Private Federated Learning
In Federated Learning (FL), multiple clients jointly train a machine learning model by sharing gradient information, instead of raw data, with a server over multiple rounds. To address the possibility of information leakage in spite of sharing only the gradients, Local Differential Privacy (LDP) is often used. In LDP, clients add a selective amount of noise to the gradients before sending the same to the server. Although such noise addition protects the privacy of clients, it leads to a degrada…

"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
Gianlucca Zuin, Adriano Veloso
https://arxiv.org/abs/2509.09073 https:/…

"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
Creating models from past observations and ensuring their effectiveness on new data is the essence of machine learning. However, selecting models that generalize well remains a challenging task. Related to this topic, the Rashomon Effect refers to cases where multiple models perform similarly well for a given learning problem. This often occurs in real-world scenarios, like the manufacturing process or medical diagnosis, where diverse patterns in data lead to multiple high-performing solutions.…

Replicable Reinforcement Learning with Linear Function Approximation
Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
https://arxiv.org/abs/2509.08660

Replicable Reinforcement Learning with Linear Function Approximation
Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learning has formalized replicability as the demand that an algorithm produce identical outcomes when executed twice on different samples from the same distribution. Provably replicable algorithms are especially interesting for reinforcement learning (RL), where algorithms are known to be unstable in practice. While replicab…

Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping
Nikasadat Emami, Amirhossein Khalilian-Gourtani, Jianghao Qian, Antoine Ratouchniak, Xupeng Chen, Yao Wang, Adeen Flinker
https://arxiv.org/abs/2509.08703

Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping
Identifying cortical regions critical for speech is essential for safe brain surgery in or near language areas. While Electrical Stimulation Mapping (ESM) remains the clinical gold standard, it is invasive and time-consuming. To address this, we analyzed intracranial electrocorticographic (ECoG) data from 16 participants performing speech tasks and developed machine learning models to directly predict if the brain region underneath each ECoG electrode is critical. Ground truth labels indicating…

Approaching Maximal Information Extraction in Low-Signal Regimes via Multiple Instance Learning
Atakan Azakli, Bernd Stelzer
https://arxiv.org/abs/2508.07114 https://

Approaching Maximal Information Extraction in Low-Signal Regimes via Multiple Instance Learning
In this work, we propose a new machine learning (ML) methodology to obtain more precise predictions for some parameters of interest in a given hypotheses testing problem. Our proposed method also allows ML models to have more discriminative power in cases where it is extremely challenging for state-of-the-art classifiers to have any level of accurate predictions. This method can also allow us to systematically decrease the error from ML models in their predictions. In this paper, we provide a m…

Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Jonas Schmidinger, Viacheslav Barkov, Sebastian Vogel, Martin Atzmueller, Gerard B M Heuvelink
https://arxiv.org/abs/2509.09408

Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Machine learning and geostatistics are two fundamentally different frameworks for predicting and spatially mapping soil properties. Geostatistics leverages the spatial structure of soil properties, while machine learning captures the relationship between available environmental features and soil properties. We propose a hybrid framework that enriches ML with spatial context through engineering of 'spatial lag' features from ordinary kriging. We call this approach 'kriging prior regression' (KpR…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/2]:
- A Masked Representation Learning to Model Cardiac Functions Using Multiple Physiological Signals
Seong-A Park, Jong-Eui Chae, Sungdong Kim, Hyung-Chul Lee, Hyun-Lim Yang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex ...
Diying Yang, Yingwei Hou, Weigang Wu

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Alireza Sameh, Mehrdad Rostami, Mourad Oussalah, Vahid Farrahi
https://arxiv.org/abs/2509.08606

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Purpose: We compared the performance of deep learning (DL) and classical machine learning (ML) algorithms for the classification of 24-hour movement behavior into sleep, sedentary, light intensity physical activity (LPA), and moderate-to-vigorous intensity physical activity (MVPA). Methods: Open-access data from 151 adults wearing a wrist-worn accelerometer (Axivity-AX3) was used. Participants were randomly divided into training, validation, and test sets (121, 15, and 15 participants each). Ra…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Traversal Learning: A Lossless And Efficient Distributed Learning Framework
Erdenebileg Batbaatar, Jeonggeol Kim, Yongcheol Kim, Young Yoon

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati, Aditya Balu, Chao Liu, Soumik Sarkar
https://arxiv.org/abs/2509.09485

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model parameters and gradient information are susceptible to privacy leakage. Although Differentially Private SGD (DPSGD) addresses privacy concerns, its static noise mechanism impacts the error bounds for model performance. Additionally, with the exponential increase in model parameters, efficient learning of these models using s…

Graph Learning
Feng Xia, Ciyuan Peng, Jing Ren, Falih Gozi Febrinanto, Renqiang Luo, Vidya Saikrishna, Shuo Yu, Xiangjie Kong
https://arxiv.org/abs/2507.05636

Graph Learning
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Gra…