Tootfinder

From Tiny Machine Learning to Tiny Deep Learning: A Survey
Shriyank Somvanshi, Md Monzurul Islam, Gaurab Chhetri, Rohit Chakraborty, Mahmuda Sultana Mimi, Swagat Ahmed Shuvo, Kazi Sifatul Islam, Syed Aaqib Javed, Sharif Ahmed Rafat, Anandi Dutta, Subasish Das
https://arxiv.org/abs/2506.18927…

From Tiny Machine Learning to Tiny Deep Learning: A Survey
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift toward deploying deep learning models on severely resource-constrained hardware. This survey presents a comprehensive overview of the transition from…

Recommendation systems in e-commerce applications with machine learning methods
Aneta Poniszewska-Maranda, Magdalena Pakula, Bozena Borowska
https://arxiv.org/abs/2506.17287

Recommendation systems in e-commerce applications with machine learning methods
E-commerce platforms are increasingly reliant on recommendation systems to enhance user experience, retain customers, and, in most cases, drive sales. The integration of machine learning methods into these systems has significantly improved their efficiency, personalization, and scalability. This paper aims to highlight the current trends in e-commerce recommendation systems, identify challenges, and evaluate the effectiveness of various machine learning methods used, including collaborative fi…

💫 Machine learning outpaces supercomputers for simulating galaxy evolution coupled with supernova explosion
https://phys.org/news/2025-07-machine-outpaces-supercomputers-simulating-galaxy.html

Machine learning outpaces supercomputers for simulating galaxy evolution coupled with supernova explosion
Researchers have used machine learning to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the origins of our own galaxy, particularly the elements essential for life in the Milky Way.

Amplifying Machine Learning Attacks Through Strategic Compositions
Yugeng Liu, Zheng Li, Hai Huang, Michael Backes, Yang Zhang
https://arxiv.org/abs/2506.18870

Amplifying Machine Learning Attacks Through Strategic Compositions
Machine learning (ML) models are proving to be vulnerable to a variety of attacks that allow the adversary to learn sensitive information, cause mispredictions, and more. While these attacks have been extensively studied, current research predominantly focuses on analyzing each attack type individually. In practice, however, adversaries may employ multiple attack strategies simultaneously rather than relying on a single approach. This prompts a crucial yet underexplored question: When the adver…

Data Virtualization for Machine Learning
Saiful Khan, Joyraj Chakraborty, Philip Beaucamp, Niraj Bhujel, Min Chen
https://arxiv.org/abs/2507.17293 https://…

Data Virtualization for Machine Learning
Nowadays, machine learning (ML) teams have multiple concurrent ML workflows for different applications. Each workflow typically involves many experiments, iterations, and collaborative activities and commonly takes months and sometimes years from initial data wrangling to model deployment. Organizationally, there is a large amount of intermediate data to be stored, processed, and maintained. \emph{Data virtualization} becomes a critical technology in an infrastructure to serve ML workflows. In …

Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
Shuangbao Paul Wang, Lucas Yang, Rahouane Chouchane, Jin Guo, Michael Bailey
https://arxiv.org/abs/2506.17319

Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
In this study, we apply machine learning and software engineering in analyzing air pollution levels in City of Baltimore. The data model was fed with three primary data sources: 1) a biased method of estimating insurance risk used by homeowners loan corporation, 2) demographics of Baltimore residents, and 3) census data estimate of NO2 and PM2.5 concentrations. The dataset covers 650,643 Baltimore residents in 44.7 million residents in 202 major cities in US. The results show that air pollution…

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study
Tingrui Zhang, Honglin Wu, Zekun Jiang, Yingying Wang, Rui Ye, Huiming Ni, Chang Liu, Jin Cao, Xuan Sun, Rong Shao, Xiaorong Wei, Yingchun Sun
https://arxiv.org/abs/2506.18106

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study
Aimed to develop and validate a CT radiomics-based explainable machine learning model for diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pr…

Quantum Machine Learning for Predicting Binding Free Energies in Structure-Based Virtual Screening
Pei-Kun Yang
https://arxiv.org/abs/2507.18425 https://ar…

Quantum Machine Learning for Predicting Binding Free Energies in Structure-Based Virtual Screening
In structure-based virtual screening, it is often necessary to evaluate the binding free energy of protein-ligand complexes by considering not only molecular conformations but also how these structures shift and rotate in space. The number of possible combinations grows rapidly and can become overwhelming. While classical computing has limitations in this context, quantum computing offers a promising alternative due to its inherent parallelism. In this study, we introduce a quantum machine lear…

Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses
Bolin Shen, Eren Erman Ozguven, Yue Zhao, Guang Wang, Yiqun Xie, Yushun Dong
https://arxiv.org/abs/2506.17964

Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses
Florida is particularly vulnerable to hurricanes, which frequently cause substantial economic losses. While prior studies have explored specific contributors to hurricane-induced damage, few have developed a unified framework capable of integrating a broader range of influencing factors to comprehensively assess the sources of economic loss. In this study, we propose a comprehensive modeling framework that categorizes contributing factors into three key components: (1) hurricane characteristics…

Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios
Parisa Ramezani, Seyed Jalaleddin Mousavirad, Mattias O'Nils, Emil Bj\"ornson
https://arxiv.org/abs/2506.17810

Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios
The conventional MUltiple SIgnal Classification (MUSIC) algorithm is effective for angle-of-arrival estimation in the far-field and can be extended for full source localization in the near-field. However, it suffers from high computational complexity, which becomes especially prohibitive in near-field scenarios due to the need for exhaustive 3D grid searches. This paper presents a machine learning-based approach for 3D localization of near-field sources in mixed line-of-sight (LoS)/non-LoS scen…

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Adrian Nugraha Utama, Simon Chun Kiat Goh, Li Hongyu, Wang Xiangyu, Zhou Yanyan, Victor Leong, Manas Mukherjee
https://arxiv.org/abs/2506.17999

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Low-loss waveguides enable efficient light delivery in photonic circuits, which are essential for high-speed optical communications and scalable implementations of photonic quantum technologies. We study the effects of several fabrication and experimental parameters on the waveguide losses of a silicon nitride integrated photonics platform using various machine learning techniques. Compared to more complex machine learning algorithms, our results show that a simple linear regression model with …

A simulation-based training framework for machine-learning applications in ARPES
MengXing Na, Chris Zhou, Sydney K. Y. Dufresne, Matteo Michiardi, Andrea Damascelli
https://arxiv.org/abs/2508.15983

A simulation-based training framework for machine-learning applications in ARPES
In recent years, angle-resolved photoemission spectroscopy (ARPES) has advanced significantly in its ability to probe more observables and simultaneously generate multi-dimensional datasets. These advances present new challenges in data acquisition, processing, and analysis. Machine learning (ML) models can drastically reduce the workload of experimentalists; however, the lack of training data for ML -- and in particular deep learning -- is a significant obstacle. In this work, we introduce an …

B0 -> K*0 tau tau- Decay: Using Machine Learning to Separate Signal from Background
Ziyao Xiong, Qixing Deng, Yidan Sun, Junhua Yang
https://arxiv.org/abs/2506.19501

B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background
This study investigates the rare decay B0 -> K*0 tau+ tau-, which is sensitive to potential violations of lepton flavor universality predicted by the Standard Model. A Monte Carlo simulated dataset containing both signal and the dominant background process B0 -> K*0 D+ D- was used to train and evaluate machine learning classifiers. After feature selection and parameter tuning, two supervised models -- Boosted Decision Trees (BDTs) and Fully Connected Neural Networks (FCNNs) -- were trained. Fea…

Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Siyang Song, Yupeng Huo, Shiqing Tang, Jiaee Cheong, Rui Gao, Michel Valstar, Hatice Gunes
https://arxiv.org/abs/2506.18915

Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Depression is a common mental illness across current human society. Traditional depression assessment relying on inventories and interviews with psychologists frequently suffer from subjective diagnosis results, slow and expensive diagnosis process as well as lack of human resources. Since there is a solid evidence that depression is reflected by various human internal brain activities and external expressive behaviours, early traditional machine learning (ML) and advanced deep learning (DL) mo…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
Xin Gu, Gautam Kamath, Zhiwei Steven Wu

Comparative analysis of machine learning techniques for feature selection and classification of Fast Radio Bursts
Ailton J. B. J\'unior, J\'eferson A. S. Fortunato, Leonardo J. Silvestre, Thonimar V. Alencar, Wiliam S. Hip\'olito-Ricaldi
https://arxiv.org/abs/2506.18854

Comparative analysis of machine learning techniques for feature selection and classification of Fast Radio Bursts
Fast Radio Bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, exhibiting a wide range of physical and observational properties. Distinguishing between repeating and non-repeating FRBs remains a key challenge in understanding their nature. In this work, we apply unsupervised machine learning techniques to classify FRBs based on both primary observables from the CHIME catalog and physically motivated derived features. We evaluate three hybrid pipelines combining dime…

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
R. Alfaro, C. Alvarez, A. Andr\'es, E. Anita-Rangel, M. Araya, J. C. Arteaga-Vel\'azquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistr\'an, A. Carrami\~nana, F. Carre\'on, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. D\'iaz-V\'elez, K. Engel, T. Ergin, C. Espinoza,…

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, we introduce a machine learning approach for gamma-hadron separation. A Multilayer Perceptron shows the best performance, surpassing traditional and other Machine Learni…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Charlotte Jennings (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Andrew Broad (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Lucy Godson (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Emily…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745)…

Advanced For-Loop for QML algorithm search
FuTe Wong
https://arxiv.org/abs/2506.18260 https://arxiv.org/pdf/2506.18260

Advanced For-Loop for QML algorithm search
This paper introduces an advanced framework leveraging Large Language Model-based Multi-Agent Systems (LLMMA) for the automated search and optimization of Quantum Machine Learning (QML) algorithms. Inspired by Google DeepMind's FunSearch, the proposed system works on abstract level to iteratively generates and refines quantum transformations of classical machine learning algorithms (concepts), such as the Multi-Layer Perceptron, forward-forward and backpropagation algorithms. As a proof of conc…

Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
Tara P. A. Tahseen, Lu\'is F. Sim\~oes, Kai Hou Yip, Nikolaos Nikolaou, Jo\~ao M. Mendon\c{c}a, Ingo P. Waldmann
https://arxiv.org/abs/2506.19679

Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
Increasing resolution and coverage of astrophysical and climate data necessitates increasingly sophisticated models, often pushing the limits of computational feasibility. While emulation methods can reduce calculation costs, the neural architectures typically used--optimised via gradient descent--are themselves computationally expensive to train, particularly in terms of data generation requirements. This paper investigates the utility of the Extreme Learning Machine (ELM) as a lightweight, no…

Applications and Challenges of Fairness APIs in Machine Learning Software
Ajoy Das, Gias Uddin, Shaiful Chowdhury, Mostafijur Rahman Akhond, Hadi Hemmati
https://arxiv.org/abs/2508.16377

Applications and Challenges of Fairness APIs in Machine Learning Software
Machine Learning software systems are frequently used in our day-to-day lives. Some of these systems are used in various sensitive environments to make life-changing decisions. Therefore, it is crucial to ensure that these AI/ML systems do not make any discriminatory decisions for any specific groups or populations. In that vein, different bias detection and mitigation open-source software libraries (aka API libraries) are being developed and used. In this paper, we conduct a qualitative study …

Safe Reinforcement Learning-based Automatic Generation Control
Amr S. Mohamed, Emily Nguyen, Deepa Kundur
https://arxiv.org/abs/2507.17868 https://arxiv.or…

Safe Reinforcement Learning-based Automatic Generation Control
Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement …

Enhanced predictions of the Madden-Julian oscillation using the FuXi-S2S machine learning model: Insights into physical mechanisms
Can Cao, Xiaohui Zhong, Lei Chen, Zhiwei Wua, Hao Li
https://arxiv.org/abs/2508.16041

Enhanced predictions of the Madden-Julian oscillation using the FuXi-S2S machine learning model: Insights into physical mechanisms
The Madden-Julian Oscillation (MJO) is the dominant mode of tropical atmospheric variability on intraseasonal timescales, and reliable MJO predictions are essential for protecting lives and mitigating impacts on societal assets. However, numerical models still fall short of achieving the theoretical predictability limit for the MJO due to inherent constraints. In an effort to extend the skillful prediction window for the MJO, machine learning (ML) techniques have gained increasing attention. Th…

Surya - Foundation Model for Heliophysics / SuryaBench - Benchmark Dataset for Advancing Machine Learning in Heliophysics and #SpaceWeather Prediction: https://arxiv.org/abs/2508.14112 / https://arxiv.org/abs/2508.14107 -> Here comes a foundation model for the Sun: https://research.ibm.com/blog/surya-heliophysics-ai-model-sun?previewToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZCI6MTEsImlhdCI6MTc1NTYyMTUwOSwiZXhwIjoxNzU1ODgwNzA5LCJzdWIiOiI0NDY1In0.pv2-edPifhET2uDPQB4hsp4HsBzhZycvMcFlZpjFQKA -> NASA and IBM Unveil AI That Helps Scientists Forecast Solar Storms: https://gizmodo.com/nasa-and-ibm-unveil-ai-that-helps-scientists-forecast-solar-storms-2000645123 - experts recently identified major gaps in their ability to forecast space weather; Surya aims to fill them.

Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations
Elyar Tourani, Brian J. Edwards, Bamin Khomami
https://arxiv.org/abs/2507.17980

Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations
Currently, identification of crystallization pathways in polymers is being carried out using molecular simulation-based data on a preset cut-off point on a single order parameter (OP) to define nucleated or crystallized regions. Aside from sensitivity to cut-off, each of these OPs introduces its own systematic biases. In this study, an integrated machine learning workflow is presented to accurately quantify crystallinity in polymeric systems using atomistic molecular dynamics data. Each atom is…

PickleBall: Secure Deserialization of Pickle-based Machine Learning Models
Andreas D. Kellas, Neophytos Christou, Wenxin Jiang, Penghui Li, Laurent Simon, Yaniv David, Vasileios P. Kemerlis, James C. Davis, Junfeng Yang
https://arxiv.org/abs/2508.15987

PickleBall: Secure Deserialization of Pickle-based Machine Learning Models
Machine learning model repositories such as the Hugging Face Model Hub facilitate model exchanges. However, bad actors can deliver malware through compromised models. Existing defenses such as safer model formats, restrictive (but inflexible) loading policies, and model scanners have shortcomings: 44.9% of popular models on Hugging Face still use the insecure pickle format, 15% of these cannot be loaded by restrictive loading policies, and model scanners have both false positives and false nega…

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
Soojin Park, Su Yeon Kim, Xinyao Zheng, Chioun Lee
https://arxiv.org/abs/2506.18994

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
Educational disparities are rooted in and perpetuate social inequalities across multiple dimensions such as race, socioeconomic status, and geography. To reduce disparities, most intervention strategies focus on a single domain and frequently evaluate their effectiveness by using causal decomposition analysis. However, a growing body of research suggests that single-domain interventions may be insufficient for individuals marginalized on multiple fronts. While interventions across multiple doma…

A Systematic Literature Review of Machine Learning Approaches for Migrating Monolithic Systems to Microservices
Imen Trabelsi, Brahim Mahmoudi, Jean Baptiste Minani, Naouel Moha, Yann-Ga\"el Gu\'eh\'eneuc
https://arxiv.org/abs/2508.15941

A Systematic Literature Review of Machine Learning Approaches for Migrating Monolithic Systems to Microservices
Scalability and maintainability challenges in monolithic systems have led to the adoption of microservices, which divide systems into smaller, independent services. However, migrating existing monolithic systems to microservices is a complex and resource-intensive task, which can benefit from machine learning (ML) to automate some of its phases. Choosing the right ML approach for migration remains challenging for practitioners. Previous works studied separately the objectives, artifacts, techni…

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu, Kei Shibuya, Khadija Zanna, Marzieh Majd, Ryan L Brown, Yufei Shen, Thomas Vaessen, George Kypriotakis, Christopher P Fagundes, Akane Sano
https://arxiv.org/abs/2506.15834

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (EMAs), passive sensing, and contextual features to develop emotion recognition models, which rely on EMA responses as ground truth. Due to this, it is crucial to consider EMA compliance when conducting a successful mHealth study. Utilizing machine learning is one approach that can solve this problem by sending EMAs bas…

DPG loss functions for learning parameter-to-solution maps by neural networks
Pablo Cort\'es Castillo, Wolfgang Dahmen, Jay Gopalakrishnan
https://arxiv.org/abs/2506.18773

DPG loss functions for learning parameter-to-solution maps by neural networks
We develop, analyze, and experimentally explore residual-based loss functions for machine learning of parameter-to-solution maps in the context of parameter-dependent families of partial differential equations (PDEs). Our primary concern is on rigorous accuracy certification to enhance prediction capability of resulting deep neural network reduced models. This is achieved by the use of variationally correct loss functions. Through one specific example of an elliptic PDE, details for establishin…

Dynamics of tidal spiral arms: Machine learning-assisted identification of equations and application to the Milky Way
Marcel Bernet, Pau Ramos, Teresa Antoja, Adrian Price-Whelan, Steven L. Brunton, Tetsuro Asano, Alexandra Gir\'on-Soto
https://arxiv.org/abs/2506.17383

Dynamics of tidal spiral arms: Machine learning-assisted identification of equations and application to the Milky Way
Understanding the spiral arms of the Milky Way (MW) remains a key open question in galactic dynamics. Tidal perturbations, such as the recent passage of the Sagittarius dwarf galaxy (Sgr), could play a significant role in exciting them. We aim to analytically characterize the dynamics of tidally induced spiral arms, including their phase-space signatures. We ran idealized test-particle simulations resembling impulsive satellite impacts, and used the Sparse Identification of Non-linear Dynamics …

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Yisu Wang, Xinjiao Li, Ruilong Wu, Huangxun Chen, Dirk Kutscher
https://arxiv.org/abs/2506.16235

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Training large-scale distributed machine learning models imposes considerable demands on network infrastructure, often resulting in sudden traffic spikes that lead to congestion, increased latency, and reduced throughput, which would ultimately affect convergence times and overall training performance. While gradient compression techniques are commonly employed to alleviate network load, they frequently compromise model accuracy due to the loss of gradient information. This paper introduces N…

Black-box optimization using factorization and Ising machines
Ryo Tamura, Yuya Seki, Yuki Minamoto, Koki Kitai, Yoshiki Matsuda, Shu Tanaka, Koji Tsuda
https://arxiv.org/abs/2507.18003

Black-box optimization using factorization and Ising machines
Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In this review, a factorization machine with quantum annealing or with quadratic-optimization annealing (FMQA) algorithm to realize fast computations of BBO using Ising machines (IMs) is discussed. The FMQA algorithm uses a factorization machine (FM) as a surrogate model for BBO. The FM model can be directly transformed int…

Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning
Andreas Loizou, Dimitrios Tsoumakos
https://arxiv.org/abs/2508.16255 https://

Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning
As the volume and diversity of available datasets continue to increase, assessing data quality has become crucial for reliable and efficient Machine Learning analytics. A modern, game-theoretic approach for evaluating data quality is the notion of Data Shapley which quantifies the value of individual data points within a dataset. State-of-the-art methods to scale the NP-hard Shapley computation also face severe challenges when applied to large-scale datasets, limiting their practical use. In th…

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
Arslan Mazitov, Sofiia Chorna, Guillaume Fraux, Marnik Bercx, Giovanni Pizzi, Sandip De, Michele Ceriotti
https://arxiv.org/abs/2506.19674

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
The development of machine-learning models for atomic-scale simulations has benefited tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. More recently, these databases have made it possible to train universal models that aim at making accurate predictions for arbitrary atomic geometries and compositions. The construction of many of these databases was however in itself aimed at materials discovery…

Machine Learning with Privacy for Protected Attributes
Saeed Mahloujifar, Chuan Guo, G. Edward Suh, Kamalika Chaudhuri
https://arxiv.org/abs/2506.19836 htt…

Machine Learning with Privacy for Protected Attributes
Differential privacy (DP) has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes. Using naive variants of differential privacy in such use cases can result in unnecessary degradation of utility. In this work, we refine the definition of DP to create a more general and flexible framework that we call feature differential privacy (FDP). Our definition is simulation-based and allows for both additio…

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Takashi Hayakawa, Satoshi Asai
https://arxiv.org/abs/2507.17686 …

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the indefinite baseline hazard of the model fails to identify temporal change in the risk set composition due to treatment assignment and unobserved factors among multiple, contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose ab…

Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
Alice Barthe, Mahtab Yaghubi Rad, Michele Grossi, Vedran Dunjko
https://arxiv.org/abs/2506.17089 …

Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
One of the key challenges in quantum machine learning is finding relevant machine learning tasks with a provable quantum advantage. A natural candidate for this is learning unknown Hamiltonian dynamics. Here, we tackle the supervised learning version of this problem, where we are given random examples of the inputs to the dynamics as classical data, paired with the expectation values of some observable after the time evolution, as corresponding output labels. The task is to replicate the corres…

Measuring the effectiveness of code review comments in GitHub repositories: A machine learning approach
Shadikur Rahman, Umme Ayman Koana, Hasibul Karim Shanto, Mahmuda Akter, Chitra Roy, Aras M. Ismael
https://arxiv.org/abs/2508.16053

Measuring the effectiveness of code review comments in GitHub repositories: A machine learning approach
This paper illustrates an empirical study of the working efficiency of machine learning techniques in classifying code review text by semantic meaning. The code review comments from the source control repository in GitHub were extracted for development activity from the existing year for three open-source projects. Apart from that, programmers need to be aware of their code and point out their errors. In that case, it is a must to classify the sentiment polarity of the code review comments to a…

Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging
Atif Bilal Asad, Achyut Paudel, Safal Kshetri, Chenchen Kang, Salik Ram Khanal, Nataliya Shcherbatyuk, Pierre Davadant, R. Paul Schreiner, Santosh Kalauni, Manoj Karkee, Markus Keller
https://arxiv.org/abs/250…

Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging
Nitrogen (N) is one of the most crucial nutrients in vineyards, affecting plant growth and subsequent products such as wine and juice. Because soil N has high spatial and temporal variability, it is desirable to accurately estimate the N concentration of grapevine leaves and manage fertilization at the individual plant level to optimally meet plant needs. In this study, we used in-field hyperspectral images with wavelengths ranging from $400 to 1000nm of four different grapevine cultivars colle…

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Wei Fan, JinYi Yoon, Xiaochang Li, Huajie Shao, Bo Ji
https://arxiv.org/abs/2507.17228

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models. While SL reduces computational overhead on edge devices, it encounters significant challenges in heterogeneous environments where devices vary in computing resources, communication capabilities, environmental conditions, and privacy requirements. Although recent studie…

Lessons from a Big-Bang Integration: Challenges in Edge Computing and Machine Learning
Alessandro Aneggi, Andrea Janes
https://arxiv.org/abs/2507.17270 htt…

Lessons from a Big-Bang Integration: Challenges in Edge Computing and Machine Learning
This experience report analyses a one year project focused on building a distributed real-time analytics system using edge computing and machine learning. The project faced critical setbacks due to a big-bang integration approach, where all components developed by multiple geographically dispersed partners were merged at the final stage. The integration effort resulted in only six minutes of system functionality, far below the expected 40 minutes. Through root cause analysis, the study identifi…

Deep Learning for Blood-Brain Barrier Permeability Prediction
Zihan Yang, Haipeng Gong
https://arxiv.org/abs/2507.18557 https://arxiv.org/pdf/2507.18557

Deep Learning for Blood-Brain Barrier Permeability Prediction
Predicting whether a molecule can cross the blood-brain barrier (BBB) is a key step in early-stage neuropharmaceutical development, directly influencing both research efficiency and success rates in drug discovery. Traditional empirical methods based on physicochemical properties are prone to systematic misjudgements due to their reliance on static rules. Early machine learning models, although data-driven, often suffer from limited capacity, poor generalization, and insufficient interpretabili…

Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks
Aristeidis Sidiropoulos, Christos Chrysanthos Nikolaidis, Theodoros Tsiolakis, Nikolaos Pavlidis, Vasilis Perifanis, Pavlos S. Efraimidis
https://arxiv.org/abs/2508.16150

Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks
Membership Inference Attacks (MIAs) pose a significant privacy risk, as they enable adversaries to determine whether a specific data point was included in the training dataset of a model. While Machine Unlearning is primarily designed as a privacy mechanism to efficiently remove private data from a machine learning model without the need for full retraining, its impact on the susceptibility of models to MIA remains an open question. In this study, we systematically assess the vulnerability of m…

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Sonny T. Jones, Grange M. Simpson, Patrick M. Pilarski, Ashley N. Dalrymple
https://arxiv.org/abs/2507.16983

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Rehabilitation technology is a natural setting to study the shared learning and decision-making of human and machine agents. In this work, we explore the use of Hierarchical Reinforcement Learning (HRL) to develop adaptive control strategies for lower-limb exoskeletons, aiming to enhance mobility and autonomy for individuals with motor impairments. Inspired by prominent models of biological sensorimotor processing, our investigated HRL approach breaks down the complex task of exoskeleton contro…

Active {\Delta}-learning with universal potentials for global structure optimization
Joe Pitfield, Mads-Peter Verner Christiansen, Bj{\o}rk Hammer
https://arxiv.org/abs/2507.18485

Active Δ-learning with universal potentials for global structure optimization
Universal machine learning interatomic potentials (uMLIPs) have recently been formulated and shown to generalize well. When applied out-of-sample, further data collection for improvement of the uMLIPs may, however, be required. In this work we demonstrate that, whenever the envisaged use of the MLIPs is global optimization, the data acquisition can follow an active learning scheme in which a gradually updated uMLIP directs the finding of new structures, which are subsequently evaluated at the d…

Generative AI Models for Learning Flow Maps of Stochastic Dynamical Systems in Bounded Domains
Minglei Yang, Yanfang Liu, Diego del-Castillo-Negrete, Yanzhao Cao, Guannan Zhang
https://arxiv.org/abs/2507.15990

Generative AI Models for Learning Flow Maps of Stochastic Dynamical Systems in Bounded Domains
Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approa…

Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos, Antonio A. Gentile, Osvaldo Simeone
https://arxiv.org/abs/2506.19714 …

Conservative quantum offline model-based optimization
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), which leverages the expressive power of variational quantum circuits to learn accurate surrogate functions by training on a few data points. However, as widely studied in the classical machine learning literature, predictive models may incorrectly e…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks
Pinheiro, de Oliveira, Silva, Saraiva, de Souza, Godoy, Ambrosio, Becker

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth
Milena Petkovic, Natasha Dropka, Xia Tang, Janina Zittel
https://arxiv.org/abs/2506.18774

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth
The Czochralski (Cz) method is a widely used process for growing high-quality single crystals, critical for applications in semiconductors, optics, and advanced materials. Achieving optimal growth conditions requires precise control of process and furnace design parameters. Still, data scarcity -- especially for new materials -- limits the application of machine learning (ML) in predictive modeling and optimization. This study proposes a transfer learning approach to overcome this limitation by…

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
Md Min-Ha-Zul Abedin, Tazqia Mehrub
https://arxiv.org/abs/2507.16952

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable (PE) files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet, under three preprocessing settings: original feature space, Principal Component Analysis (PCA), and Linear Discriminant Analysis (L…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications
Yizhou Xu, Antoine Maillard, Lenka Zdeborov\'a, Florent Krzakala

Eco-Friendly AI: Unleashing Data Power for Green Federated Learning
Mattia Sabella, Monica Vitali
https://arxiv.org/abs/2507.17241 https://

Eco-Friendly AI: Unleashing Data Power for Green Federated Learning
The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) comes with a significant environmental impact, particularly in terms of energy consumption and carbon emissions. This pressing issue highlights the need for innovative solutions to mitigate AI's ecological footprint. One of the key factors influencing the energy consumption of ML model training is the size of the training dataset. ML models are often trained on vast amounts of data continuously generated by sensor…

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
Rachel Hong, Jevan Hutson, William Agnew, Imaad Huda, Tadayoshi Kohno, Jamie Morgenstern
https://arxiv.org/abs/2506.17185

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we ask: What are the legal privacy implications of web-scraped machine learning datasets? In an empirical study of a popular training dataset, we find significant presence of personally identifiable information despite sanitization efforts. Our audit provides concr…

Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science
David A. Egger, Manuel Grumet, Tom\'a\v{s} Bu\v{c}ko
https://arxiv.org/abs/2506.19595

Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science
Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the microscopic effects underlying Raman activity in these systems. These calculations are often performed using the canonical harmonic approximation which cannot capture certain thermal changes in the Raman response. Anharmonic vibrational effects were recently found …

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Shamse Tasnim Cynthia, Nuri Almarimi, Banani Roy
https://arxiv.org/abs/2506.15884

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely underexamined. In this study, we investigated the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. First, we ex…

TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
Tim Langer, Matthias Widra, Volkhard Beyer
https://arxiv.org/abs/2508.16553 https://

TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontrolle…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Vector preference-based contextual bandits under distributional shifts
Apurv Shukla, P. R. Kumar

Machine Learning-Assisted Nano-imaging and Spectroscopy of Phase Coexistence in a Wide-Bandgap Semiconductor
Alyssa Bragg, Fengdeng Liu, Zhifei Yang, Nitzan Hirshberg, Madison Garber, Brayden Lukaskawcez, Liam Thompson, Shane MacDonald, Hayden Binger, Devon Uram, Ashley Bucsek, Bharat Jalan, Alexander McLeod
https://arxiv.org/abs/2507.1767…

Machine Learning-Assisted Nano-imaging and Spectroscopy of Phase Coexistence in a Wide-Bandgap Semiconductor
Wide bandgap semiconductors with high room temperature mobilities are promising materials for high-power electronics. Stannate films provide wide bandgaps and optical transparency, although electron-phonon scattering can limit mobilities. In SrSnO3, epitaxial strain engineering stabilizes a high-mobility tetragonal phase at room temperature, resulting in a threefold increase in electron mobility among doped films. However, strain relaxation in thicker films leads to nanotextured coexistence of …

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones
Sadat, Hossain, Sifat, Rafi, Alvi, Rhaman

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned
Sima Arasteh, Christophe Hauser
https://arxiv.org/abs/2508.15042 https://

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned
In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges, especially regarding the scale of data involved, which was not a factor in traditional methods. Consequently, in spite of the rise of new machine-learning-based approaches in that space, important shortcomings persist regarding their evaluation. First, researchers oft…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
Chengkun Li, Gr\'egoire Clart\'e, Martin J{\o}rgensen, Luigi Acerbi

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networks
Talha Bozkus, Urbashi Mitra

Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
Karan Shah, Attila Cangi
https://arxiv.org/abs/2508.16554 https://

Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under external time-dependent perturbations such as laser fields. In this work, we present a novel approach to accelerate electron dynamics simulations based on real time TDDFT using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and featurization, and high-resolution training data, our model achieves superior accurac…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Active Learning For Repairable Hardware Systems With Partial Coverage
Michael Potter, Beyza Kalkanl{\i}, Deniz Erdo\u{g}mu\c{s}, Michael Everett

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks
Mart\'in Hern\'andez, Enrique Zuazua

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra
https://arxiv.org/abs/2506.16224

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API call sequences. This approach effectively captures distinctive linguistic patterns among malware and benign families, enabling finer-grained classification. We delve into n-gram size selection, featu…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- Fair and efficient contribution valuation for vertical federated learning
Zhenan Fan, Huang Fang, Xinglu Wang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset...
Azmine Toushik Wasi, MD Shafikul Islam, Adipto Raihan Akib, Mahathir Mohammad Bappy

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention
Ayush K. Varshney, Vicen\c{c} Torra

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification
Renato De Leone, Francesca Maggioni, Andrea Spinelli

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Shuangbao Paul Wang, Paul Mullin
https://arxiv.org/abs/2506.19052 https://

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Artificial Intelligence brings innovations into the society. However, bias and unethical exist in many algorithms that make the applications less trustworthy. Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. In …

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology
Arpan Kusari, Wenbo Sun

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/11]:
- Navigating Conflicting Views: Harnessing Trust for Learning
Jueqing Lu, Wray Buntine, Yuanyuan Qi, Joanna Dipnall, Belinda Gabbe, Lan Du

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
Wenxuan Zeng, Tianshi Xu, Yi Chen, Yifan Zhou, Mingzhe Zhang, Jin Tan, Cheng Hong, Meng Li
https://arxiv.org/abs/2507.14519

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
Privacy-preserving machine learning (PPML) based on cryptographic protocols has emerged as a promising paradigm to protect user data privacy in cloud-based machine learning services. While it achieves formal privacy protection, PPML often incurs significant efficiency and scalability costs due to orders of magnitude overhead compared to the plaintext counterpart. Therefore, there has been a considerable focus on mitigating the efficiency gap for PPML. In this survey, we provide a comprehensive …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- SpecExtend: A Drop-in Enhancement for Speculative Decoding of Long Sequences
Jungyoub Cha, Hyunjong Kim, Sungzoon Cho

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Randomized PCA Forest for Outlier Detection
Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj, Peter Schneider-Kamp, Arthur Zimek

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertis...
Tianliang Xu, Ariel Hasell, Sabina Tomkins

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- From Indirect Object Identification to Syllogisms: Exploring Binary Mechanisms in Transformer Cir...
Karim Saraipour, Shichang Zhang

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Beyond Transcription: Mechanistic Interpretability in ASR
Glazer, Segal-Feldman, Segev, Shamsian, Buchnick, Hetz, Fetaya, Keshet, Navon

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Tran...
Xiaoyan Li, Shixin Xu, Faisal Habib, Arvind Gupta, Huaxiong Huang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical ...
Shuo Tan, Rui Liu, Xuesong Han, XianLei Long, Kai Wan, Linqi Song, Yong Li

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks
Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, Shi Jin

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, a...
Zheng, Du, Liao, Zhao, Zhou, Song, Zhang, Liu, Qi, Li, Zhang, Wang, Zhang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- TOC-UCO: a comprehensive repository of tabular ordinal classification datasets
Ayll\'on-Gavil\'an, Guijo-Rubio, G\'omez-Orellana, B\'erchez-Moreno, Vargas-Yun, Guti\'errez

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- LagKV: Lag-Relative Information of the KV Cache Tells Which Tokens Are Important
Manlai Liang, JiaMing Zhang, Xiong Li, Jinlong Li

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation
Bing Yu, Ke Sun, He Wang, Zhouchen Lin, Zhanxing Zhu

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[11/11]:
- Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
Aditya Sengar, Ali Hariri, Daniel Probst, Patrick Barth, Pierre Vandergheynst

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[10/11]:
- FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answ...
Chengyue Huang, Brisa Maneechotesuwan, Shivang Chopra, Zsolt Kira

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/11]:
- Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space...
Yuri Kuratov, Mikhail Arkhipov, Aydar Bulatov, Mikhail Burtsev

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/11]:
- Radio Map Prediction from Aerial Images and Application to Coverage Optimization
Fabian Jaensch, Giuseppe Caire, Beg\"um Demir

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/11]:
- Disentangling representations of retinal images with generative models
Sarah M\"uller, Lisa M. Koch, Hendrik P. A. Lensch, Philipp Berens

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/11]:
- Bohdi: Heterogeneous LLM Fusion with Automatic Data Exploration
Junqi Gao, Zhichang Guo, Dazhi Zhang, Dong Li, Runze Liu, Pengfei Li, Kai Tian, Biqing Qi

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/11]:
- Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
Bao Pham, Gabriel Raya, Matteo Negri, Mohammed J. Zaki, Luca Ambrogioni, Dmitry Krotov

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/11]:
- AutoPDL: Automatic Prompt Optimization for LLM Agents
Claudio Spiess, Mandana Vaziri, Louis Mandel, Martin Hirzel

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/11]:
- One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
Surkov, Wendler, Mari, Terekhov, Deschenaux, West, Gulcehre, Bau

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/11]:
- On the fast convergence of minibatch heavy ball momentum
Raghu Bollapragada, Tyler Chen, Rachel Ward

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Controllable Video Generation with Provable Disentanglement
Shen, Zhu, Li, Xie, Tang, Deka, Liu, Chen, Zhang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Iterative Minimax Games with Coupled Linear Constraints
Huiling Zhang, Zi Xu, Yu-Hong Dai

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Mohammed Elmusrati
https://arxiv.org/abs/2508.15719 https://

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Extracting meaning from uncertain, noisy data is a fundamental problem across time series analysis, pattern recognition, and language modeling. This survey presents a unified mathematical framework that connects classical estimation theory, statistical inference, and modern machine learning, including deep learning and large language models. By analyzing how techniques such as maximum likelihood estimation, Bayesian inference, and attention mechanisms address uncertainty, the paper illustrates …