Tootfinder

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- The Complexity of Learning Sparse Superposed Features with Feedback
Akash Kumar

Evolving HPC services to enable ML workloads on HPE Cray EX
Stefano Schuppli, Fawzi Mohamed, Henrique Mendon\c{c}a, Nina Mujkanovic, Elia Palme, Dino Conciatore, Lukas Drescher, Miguel Gila, Pim Witlox, Joost VandeVondele, Maxime Martinasso, Thomas C. Schulthess, Torsten Hoefler
https://arxiv.org/abs/2507.01880

Evolving HPC services to enable ML workloads on HPE Cray EX
The Alps Research Infrastructure leverages GH200 technology at scale, featuring 10,752 GPUs. Accessing Alps provides a significant computational advantage for researchers in Artificial Intelligence (AI) and Machine Learning (ML). While Alps serves a broad range of scientific communities, traditional HPC services alone are not sufficient to meet the dynamic needs of the ML community. This paper presents an initial investigation into extending HPC service capabilities to better support ML workloa…

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Zhanna Kaufman, Madeline Endres, Cindy Xiong Bearfield, Yuriy Brun
https://arxiv.org/abs/2508.00140

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five sta…

And here now is the color composite of the famous images of the #InterstellarComet 3I/ATLAS taken in the night 27/28 August in u, g, r and i: https://noirlab.edu/public/news/noirlab2525/ - the tail measures about 30 arc seconds. Amateur astronomers caught it as well, with a 12-inch telescope, only a few hours later: https://groups.io/g/comets-ml/message/33921

The Maximum Likelihood Degree of Toric Models is Monotonic
Carlos Am\'endola, Janike Oldekop, Maximilian Wiesmann
https://arxiv.org/abs/2507.02719 http…

The Maximum Likelihood Degree of Toric Models is Monotonic
We settle a conjecture by Coons and Sullivant stating that the maximum likelihood (ML) degree of a facial submodel of a toric model is at most the ML degree of the model itself. We discuss the impact on the ML degree from observing zeros in the data. Moreover, we connect this problem to tropical likelihood degenerations, and show how the results can be applied to discrete graphical and quasi-independence models.

Multilevel Bregman Proximal Gradient Descent
Yara Elshiaty, Stefania Petra
https://arxiv.org/abs/2506.03950 https://arxiv.org/pdf/250…

Multilevel Bregman Proximal Gradient Descent
We present the Multilevel Bregman Proximal Gradient Descent (ML-BPGD) method, a novel multilevel optimization framework tailored to constrained convex problems with relative Lipschitz smoothness. Our approach extends the classical multilevel optimization framework (MGOPT) to handle Bregman-based geometries and constrained domains. We provide a rigorous analysis of ML BPGD for multiple coarse levels and establish a global linear convergence rate. We demonstrate the effectiveness of ML BPGD in th…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Extending Model-x Framework to Missing Data
Deniz Koyuncu, Alex Gittens, B\"ulent Yener

Real-time ML-based Defense Against Malicious Payload in Reconfigurable Embedded Systems
Rye Stahle-Smith, Rasha Karakchi
https://arxiv.org/abs/2509.02387 https://

Real-time ML-based Defense Against Malicious Payload in Reconfigurable Embedded Systems
The growing use of FPGAs in reconfigurable systems introducessecurity risks through malicious bitstreams that could cause denial-of-service (DoS), data leakage, or covert attacks. We investigated chip-level hardware malicious payload in embedded systems and proposed a supervised machine learning method to detect malicious bitstreams via static byte-level features. Our approach diverges from existing methods by analyzing bitstreams directly at the binary level, enabling real-time detection witho…

ML-Driven Strong Lens Discoveries: Down to $\theta_E \sim 0.03''$ and $M_\mathrm{halo}< 10^{11} M_\odot$
Ethan Silver, R. Wang, Xiaosheng Huang, A. Bolton, C. Storfer, S. Banka
https://arxiv.org/abs/2507.01943

ML-Driven Strong Lens Discoveries: Down to $θ_E \sim 0.03''$ and $M_\mathrm{halo}< 10^{11} M_\odot$
We present results on extending the strong lens discovery space down to much smaller Einstein radii ($θ_E\lesssim0.03''$) and much lower halo mass ($M_\mathrm{halo}<10^{11}M_\odot$) through the combination of JWST observations and machine learning (ML) techniques. First, we forecast detectable strong lenses with JWST using CosmoDC2 as the lens catalog, and a source catalog down to 29th magnitude. By further incorporating the VELA hydrodynamical simulations of high-redshift galaxies, we simulat…

Google specifically looking for expertise in machine learning and PROGRAMMING LANGUAGES. Papers at conferences like PLDI/ICFP, experience w/ LC, types, CL… ML slowly coming to its senses. (-:
https://www.google.com/about/careers/appli

A Further Comparison of TD-DMRG and ML-MCTDH for Nonadiabatic Dynamics of Exciton Dissociation
Weitang Li, Jiajun Ren, Jun Yan
https://arxiv.org/abs/2509.00456 https://

A Further Comparison of TD-DMRG and ML-MCTDH for Nonadiabatic Dynamics of Exciton Dissociation
Tensor network methods, such as time-dependent density matrix renormalization group (TD-DMRG) and multi-layer multiconfiguration time-dependent Hartree (ML-MCTDH), are powerful tools for simulating quantum dynamics. While both methods are theoretically exact in the limit of large bond dimensions, a recent study reported up to 60% discrepancy in their calculations for exciton dissociation. To resolve this inconsistency, we conduct a systematic comparison using Renormalizer, a unified software fr…

Hi @…,
as you implemented #RFC9421 you may know - which servers in the wild use it?
#Activitypub

AM-DefectNet: Additive Manufacturing Defect Classification Using Machine Learning - A comparative Study
Mohsen Asghari Ilani, Yaser Mike Banad
https://arxiv.org/abs/2509.01769 h…

AM-DefectNet: Additive Manufacturing Defect Classification Using Machine Learning - A comparative Study
Additive Manufacturing (AM) processes present challenges in monitoring and controlling material properties and process parameters, affecting production quality and defect detection. Machine Learning (ML) techniques offer a promising solution for addressing these challenges. In this study, we introduce a comprehensive framework, AM-DefectNet, for benchmarking ML models in melt pool characterization, a critical aspect of AM. We evaluate 15 ML models across 10 metrics using 1514 training and 505 t…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of...
Han Zhou, Jordy Van Landeghem, Teodora Popordanoska, Matthew B. Blaschko

Analysis-ready Generative Unfolding
Anja Butter, Nathan Huetsch, Vinicius Mikuni, Benjamin Nachman, Sofia Palacios Schweitzer
https://arxiv.org/abs/2509.02708 https://

Analysis-ready Generative Unfolding
Machine Learning (ML)-based unfolding methods have enabled high-dimensional and unbinned differential cross section measurements. While a suite of such methods has been proposed, most focus exclusively on the challenge of statistically removing resolution effects. In practice, unfolding methods must also account for impurities and finite acceptance and efficiency effects. In this paper, we extend a class of unfolding methods based on generative ML to include the full suite of effects relevant f…

Hey y'all. My wife recently graduated with a masters in CS (main focus areas being data science and AI/ML) and is still looking for a job as the market is utter shit. We're in the Bay Area for the foreseeable future, so she's currently looking for job opportunities either in the area or fully remote!
#GetFediHired

Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts
Yuxuan Yang
https://arxiv.org/abs/2507.01776

Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts
The integration of machine learning (ML) into spatial design holds immense potential for optimizing space utilization, enhancing functionality, and streamlining design processes. ML can automate tasks, predict performance outcomes, and tailor spaces to user preferences. However, the emotional, cultural, and aesthetic dimensions of design remain crucial for creating spaces that truly resonate with users-elements that ML alone cannot address. The key challenge lies in harmonizing data-driven effi…

ML-based muon identification using a FNAL-NICADD scintillator chamber for the MID subsystem of ALICE 3
Jesus Eduardo Mu\~noz Mendez, Antonio Ortiz, Alom Antonio Paz Jimenez, Paola Vargas Torres, Ruben Alfaro Molina, Laura Helena Gonz\'alez Trueba, Varlen Grabski, Arturo Fernandez Tellez, Hector David Regules Medel, Mario Rodriguez Cahuantzi, Guillermo Tejeda Mu\~noz, Yael Antonio Vasquez Beltran, Juan Carlos Cabanillas Noris, Solangel Rojas Torres, Gergely Gabor Barnafoldi, Daniel …

ML-based muon identification using a FNAL-NICADD scintillator chamber for the MID subsystem of ALICE 3
The ALICE Collaboration is planning to construct a new detector (ALICE 3) aiming at exploiting the potential of the high-luminosity Large Hadron Collider (LHC). The new detector will allow ALICE to participate in LHC Run 5 scheduled from 2036 to 2041. The muon-identifier subsystem (MID) is part of the ALICE 3 reference detector layout. The MID will consist of a standard magnetic iron absorber ($\approx4$ nuclear interaction lengths) followed by muon chambers. The baseline option for the MID cha…

Interplay of energy and charge transfer in WSe2/CrSBr heterostructures
Jos\'e Roberto de Toledo, Caique Serati de Brito, Barbara L. T. Rosa, Alisson R. Cadore, C\'esar Ricardo Rabahi, Paulo E. Faria Junior, Ana Carolina Ferreira de Brito, Talieh S. Ghiasi, Josep Ingla-Ayn\'es, Christian Sch\"uller, Herre S. J. van der Zant, Stephan Reitzenstein, Ingrid D. Barcelos, Florian Dirnberger, Yara Galv\~ao Gobato

Interplay of energy and charge transfer in WSe2/CrSBr heterostructures
Van der Waals heterostructures (vdWHs) composed of transition-metal dichalcogenides (TMDs) and layered magnetic semiconductors offer great opportunities to manipulate exciton and valley properties of TMDs. Here, we present magneto-photoluminescence (PL) studies in a WSe2 monolayer (ML) on a CrSBr crystal, an anisotropic layered antiferromagnetic semiconductor. Our results reveal unique behavior of each of the ML-WSe2 PL peaks under magnetic field that is distinct from the pristine case. An intr…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram

NetGent: Agent-Based Automation of Network Application Workflows
Jaber Daneshamooz, Eugene Vuong, Laasya Koduru, Sanjay Chandrasekaran, Arpit Gupta
https://arxiv.org/abs/2509.00625

NetGent: Agent-Based Automation of Network Application Workflows
We present NetGent, an AI-agent framework for automating complex application workflows to generate realistic network traffic datasets. Developing generalizable ML models for networking requires data collection from network environments with traffic that results from a diverse set of real-world web applications. However, using existing browser automation tools that are diverse, repeatable, realistic, and efficient remains fragile and costly. NetGent addresses this challenge by allowing users to …

Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
Sayan Biswas, Philippe Chartier, Akash Dhasade, Tom Jurien, David Kerriou, Anne-Marie Kerrmarec, Mohammed Lemou, Franklin Tranie, Martijn de Vos, Milos Vujasinovic
https://arxiv.org/abs/2509.01253

Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
In contemporary cloud-based services, protecting users' sensitive data and ensuring the confidentiality of the server's model are critical. Fully homomorphic encryption (FHE) enables inference directly on encrypted inputs, but its practicality is hindered by expensive bootstrapping and inefficient approximations of non-linear activations. We introduce Safhire, a hybrid inference framework that executes linear layers under encryption on the server while offloading non-linearities to the client i…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- Safe machine learning model release from Trusted Research Environments: The SACRO-ML package
Smith, Preen, McCarthy, Albashir, Crespi-Boixader, Mumtaz, Cole, Liley, Migenda, Rogers, Jones

Privacy is not a fool's game. If you give up today everything you did will be lost...
There is always a way or there will be soon.
#privacy

Machine learning-accelerated search of superconductors in B-C-N based compounds and R3Ni2O7-type nickelates
Xiaoying Li, Wenqian Tu, Run Lv, Li'e Liu, Dingfu Shao, Yuping Sun, Wenjian Lu
https://arxiv.org/abs/2509.03081

Machine learning-accelerated search of superconductors in B-C-N based compounds and R3Ni2O7-type nickelates
Superconductor research has traditionally depended on experiments and theoretical approaches. However, the rapid advancement of data-driven methods and machine learning (ML) has opened avenues for accelerating superconductor discovery. Here, we integrated ML with density functional theory (DFT) calculations to efficiently screen conventional B-C-N based superconductors and identify potential high-TC candidates among R3Ni2O7-type bilayer nickelates. We identified 12 new binary and ternary B-C-N …

Exploring Left-Wing Extremism on the Decentralized Web: An Analysis of Lemmygrad.ml
Utkucan Balci, Michael Sirivianos, Jeremy Blackburn
https://arxiv.org/abs/2507.23699 https://…

Exploring Left-Wing Extremism on the Decentralized Web: An Analysis of Lemmygrad.ml
This study investigates the presence of left-wing extremism on the Lemmygrad.ml instance of the decentralized social media platform Lemmy, from its launch in 2019 up to a month after the bans of the subreddits r/GenZedong and r/GenZhou. We conduct a temporal analysis on Lemmygrad.ml's user activity, with also measuring the degree of highly abusive or hateful content. Furthermore, we explore the content of their posts using a transformer-based topic modeling approach. Our findings reveal a subst…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods
Oquendo, Nadir, Jonuzi, Patra, Sinha, Or\'us, Mugel

Google plans to roll out ML-powered age estimation in the US over the next few weeks to detect whether users are under 18 and then enable existing protections (Abner Li/9to5Google)
https://9to5google.com/2025/07/30/google-accounts-age-estimation/

Google starts rolling out ML-powered age estimation in the US
In the US, Google is beginning to use an age estimation model to detect whether Account users are under or over 18. 

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Feature Augmentations for High-Dimensional Learning
Xiaonan Zhu, Bingyan Wang, Jianqing Fan

Machine Learning Pipeline for Software Engineering: A Systematic Literature Review
Samah Kansab
https://arxiv.org/abs/2508.00045 https://arxiv.org/pdf/2508…

Machine Learning Pipeline for Software Engineering: A Systematic Literature Review
The rapid advancement of software development practices has introduced challenges in ensuring quality and efficiency across the software engineering (SE) lifecycle. As SE systems grow in complexity, traditional approaches often fail to scale, resulting in longer debugging times, inefficient defect detection, and resource-heavy development cycles. Machine Learning (ML) has emerged as a key solution, enabling automation in tasks such as defect prediction, code review, and release quality estimati…

Information-Theoretic Decentralized Secure Aggregation with Collusion Resilience
Xiang Zhang, Zhou Li, Shuangyang Li, Kai Wan, Derrick Wing Kwan Ng, Giuseppe Caire
https://arxiv.org/abs/2508.00596

Information-Theoretic Decentralized Secure Aggregation with Collusion Resilience
In decentralized federated learning (FL), multiple clients collaboratively learn a shared machine learning (ML) model by leveraging their privately held datasets distributed across the network, through interactive exchange of the intermediate model updates. To ensure data security, cryptographic techniques are commonly employed to protect model updates during aggregation. Despite growing interest in secure aggregation, existing works predominantly focus on protocol design and computational guar…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Calibration Prediction Interval for Non-parametric Regression and Neural Networks
Kejin Wu, Dimitris N. Politis

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Murayyiam Parvez, Annus Zulfiqar, Roman Beltiukov, Shir Landau Feibish, Walter Willinger, Arpit Gupta, Muhammad Shahbaz
https://arxiv.org/abs/2509.00397

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Machine learning (ML) is increasingly being deployed in programmable data planes (switches and SmartNICs) to enable real-time traffic analysis, security monitoring, and in-network decision-making. Decision trees (DTs) are particularly well-suited for these tasks due to their interpretability and compatibility with data-plane architectures, i.e., match-action tables (MATs). However, existing in-network DT implementations are constrained by the need to compute all input features upfront, forcing …

Weirdest #spam I've seen in a while...
Sent to the MailMate users ML, a long explanation of Morrocan paternity paperwork."All you need to know about the certificate of paternity in Morocco" according to machine translation.
I'm pretty sure that list only allows posts from subscriber, so I guess "walid falcon" actually went through the subscription process to pos…

I love the dates in the timestamps of this page.
Contributing to NetBSD: Hardware
#netbsd

Lessons Learned from Deploying Adaptive Machine Learning Agents with Limited Data for Real-time Cell Culture Process Monitoring
Thanh Tung Khuat, Johnny Peng, Robert Bassett, Ellen Otte, Bogdan Gabrys
https://arxiv.org/abs/2509.02606

Lessons Learned from Deploying Adaptive Machine Learning Agents with Limited Data for Real-time Cell Culture Process Monitoring
This study explores the deployment of three machine learning (ML) approaches for real-time prediction of glucose, lactate, and ammonium concentrations in cell culture processes, using Raman spectroscopy as input features. The research addresses challenges associated with limited data availability and process variability, providing a comparative analysis of pretrained models, just-in-time learning (JITL), and online learning algorithms. Two industrial case studies are presented to evaluate the i…

A Machine Learning-Fueled Modelfluid for Flowsheet Optimization
Martin Bubel, Tobias Seidel, Michael Bortz
https://arxiv.org/abs/2509.02242 https://arxiv.o…

A Machine Learning-Fueled Modelfluid for Flowsheet Optimization
Process optimization in chemical engineering may be hindered by the limited availability of reliable thermodynamic data for fluid mixtures. Remarkable progress is being made in predicting thermodynamic mixture properties by machine learning techniques. The vast information provided by these prediction methods enables new possibilities in process optimization. This work introduces a novel modelfluid representation that is designed to seamlessly integrate these ML-predicted data directly into…

Deep Neural Networks Hunting Ultra-Light Dark Matter
Pavel K\r{u}s, Diana L\'opez Nacir, Federico R. Urban
https://arxiv.org/abs/2506.04100 https://

Deep Neural Networks Hunting Ultra-Light Dark Matter
Ultra-light dark matter (ULDM) is a compelling candidate for cosmological dark matter. If ULDM interacts with ordinary matter, it can induce measurable, characteristic signals in pulsar-timing data because it causes the orbits of pulsars in binary systems to osculate. In this work, we investigate the potential of machine learning (ML) techniques to detect such ULDM signals. To this end, we construct three types of neural networks: an autoencoder, a binary classifier, and a multiclass classifier…

Who knew that French #TGV #CocaCola costs ~1 #EuroCent per ml?

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Bagged Regularized $k$-Distances for Anomaly Detection
Yuchao Cai, Hanfang Yang, Yuheng Ma, Hanyuan Hang

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
Mohammad Firas Sada, John J. Graham, Mahidhar Tatineni, Dmitry Mishin, Thomas A. DeFanti, Frank W\"urthwein
https://arxiv.org/abs/2507.00428

Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor
As machine learning (ML) applications become integral to modern network operations, there is an increasing demand for network programmability that enables low-latency ML inference for tasks such as Quality of Service (QoS) prediction and anomaly detection in cybersecurity. ML models provide adaptability through dynamic weight adjustments, making Programming Protocol-independent Packet Processors (P4)-programmable FPGA SmartNICs an ideal platform for investigating In-Network Machine Learning (IN…

First principles study on the oxidation resistance of two-dimensional intrinsic and defective GeO2
Xixiang Zhang, Xinmei Yu, Liang Ma, Yanfeng Ge, Yong Liu, Wenhui Wan
https://arxiv.org/abs/2509.00756 …

First principles study on the oxidation resistance of two-dimensional intrinsic and defective GeO2
Although two-dimensional (2D) oxide semiconductors exhibit remarkable oxidation resistance compared to conventional 2D materials, the microscopic physical processes that govern this behavior at the atomic scale remains elusive. Using first-principles calculations, we investigated the defect formation and oxidation dynamics of the GeO${_2}$ monolayer (ML). The investigations reveal that the intrinsic GeO${_2}$ ML is resistant to oxidation due to strong electrostatic repulsion between surface oxy…

Machine Learning Classification of COSMOS2020 Galaxies: Quiescent vs. Star-Forming
Vahid Asadi, Nima Chartab, Akram Hasani Zonoozi, Hosein Haghi
https://arxiv.org/abs/2509.03039

Machine Learning Classification of COSMOS2020 Galaxies: Quiescent vs. Star-Forming
Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may struggle to capture complex galaxy properties. This study aims to develop a robust and efficient machine learning (ML) classification method to identify quiescent and star-forming galaxies within the Farmer COSMOS2020 catalog. We utilized JWST wide-field light cones…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization
Daesung Kim, Hye Won Chung

#blohaj in waffle house
https://youtu.be/X1beEuBV7M0

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang, Abdelrahman Ibrahim, Carl Nuzman, Runxin Wang, Kursat Rasim Mestav, Dustin Zhang, Iraj Saniee, Shawn Winston, Gordana Pavlovic, Wei Ding, William J. Hillery, Chenxi Hao, Ram Thirunagari, Jung Chang, Jeehyun Kim, Bartek Kozicki, Dragan Samardzija, Taesang Yoo, Andreas Maeder, Tingfang Ji, Harish Viswanathan

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, …

Multi-objective Evolutionary Algorithms (MOEAs) in PMEDM - A Comparative Study in Pareto Frontier
Mohsen Asghari Ilani, Yaser Mike Banad
https://arxiv.org/abs/2509.01775 https:/…

Multi-objective Evolutionary Algorithms (MOEAs) in PMEDM - A Comparative Study in Pareto Frontier
Electrical discharge machining (EDM) is a crucial process in precision manufacturing, leveraging electro-thermal energy to remove material without electrode contact. In this study, we delve into the realm of Machine Learning (ML) to enhance the efficiency and precision of EDM, particularly focusing on Powder-Mixed Electrical Discharge Machining (PMEDM) with the integration of a vibration system. We comprehensively evaluate four leading ML models - Deep Neural Network (DNN), Extreme Gradient Boo…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Mode...
Zachary C. Brown, David Carlson

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Non-negative matrix factorization algorithms generally improve topic model fits
Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens

PRIME: Pseudo-Random Integrated Multi-Part Entropy for Adaptive Packet Spraying in AI/ML Data centers
Ashkan Sobhani, Sogand Sadrhaghighi, Xingjun Chu
https://arxiv.org/abs/2507.23012

PRIME: Pseudo-Random Integrated Multi-Part Entropy for Adaptive Packet Spraying in AI/ML Data centers
Large-scale distributed training in production data centers place significant demands on network infrastructure. In particular, significant load balancing challenges arise when processing AI/ML workloads, consisting of low-entropy, bursty and long-lived flows. Existing solutions designed for Ethernet, such as Equal-Cost Multi-Path (ECMP) struggle to maintain high network utilization. While major industry players (e.g., Ultra Ethernet Consortium) and parts of academia have proposed packet sprayi…

Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
Michael Cohoon, Debbie Furman
https://arxiv.org/abs/2507.22064

Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
This paper details the machine learning (ML) journey of a group of people focused on software testing. It tells the story of how this group progressed through a ML workflow (similar to the CRISP-DM process). This workflow consists of the following steps and can be used by anyone applying ML techniques to a project: gather the data; clean the data; perform feature engineering on the data; splitting the data into two sets, one for training and one for testing; choosing a machine learning model; t…

Deciphering the Physical Origin of GRB 240825A: A Long GRB Lacking a Bright Supernova
Rahul Gupta, Judith L. Racusin, R. Sanchez-Ramirez, Y. Hu, Andrea Rossi, Maria Dolores Caballero Garcia, Pi Nuessle, Alberto J. Castro-Tirado, Samantha Oates, Pragyan P. Bordoloi, Amar Aryan, Simone Dichiara, Peter Veres, Noel Klingler, Nicola Omodei, Elisabetta Maiorano, Donggeun Tak, S. Shilling, Jose E. Adsuara, P. H. Connell, E. Fernandez Garcia, Guillermo Garcia-Segura, Ankur Ghosh, Ersin G\&quot…

Deciphering the Physical Origin of GRB 240825A: A Long GRB Lacking a Bright Supernova
We present a comprehensive multiwavelength analysis of GRB 240825A, a bright gamma-ray burst (GRB) detected by Fermi and Swift, with a prompt duration ($T_{\rm 90}$ ~ 4 sec in 50-300 keV) near the boundary separating short and long GRBs, prompting a detailed investigation into its classification and progenitor. Using classical prompt metrics (duration, minimum variability timescale (MVT), lag, and spectral hardness) and modern classification techniques (machine-learning (ML) based t-SNE, suppor…

TofuML: A Spatio-Physical Interactive Machine Learning Device for Interactive Exploration of Machine Learning for Novices
Wataru Kawabe, Hiroto Fukuda, Akihisa Shitara, Yuri Nakao, Yusuke Sugano
https://arxiv.org/abs/2508.00252

TofuML: A Spatio-Physical Interactive Machine Learning Device for Interactive Exploration of Machine Learning for Novices
We introduce TofuML, an interactive system designed to make machine learning (ML) concepts more accessible and engaging for non-expert users. Unlike conventional GUI-based systems, TofuML employs a physical and spatial interface consisting of a small device and a paper mat, allowing users to train and evaluate sound classification models through intuitive, toy-like interactions. Through two user studies -- a comparative study against a GUI-based version and a public event deployment -- we inves…

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Tinghuan Li, Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
https://arxiv.org/abs/2506.03209

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in…

AmphiKey: A Dual-Mode Secure Authenticated Key Encapsulation Protocol for Smart Grid
Kazi Hassan Shakib, Muhammad Asfand Hafeez, Arslan Munir
https://arxiv.org/abs/2509.01701 ht…

AmphiKey: A Dual-Mode Secure Authenticated Key Encapsulation Protocol for Smart Grid
AmphiKey, a dual-mode post-quantum/traditional (PQ/T) hybrid authenticated key exchange mechanism (AKEM) has been designed to secure smart grid communications against both classical and quantum threats. AmphiKey offers two distinct operational modes within a single framework: an Authenticated Mode and a Deniable Mode. The Authenticated Mode employs a blackbox approach, combining ephemeral ML-KEM-768 and X25519 with long-term Raccoon DSA keys to provide forward secrecy and strong, non-repudiable…

Safe Security, formerly Lucideus, which quantifies cyber risk using ML, raised a $70M Series C led by Avataar Ventures, bringing its total funding to $170M (Shashank Pathak/Entrackr)
https://entrackr.com/news/safe-security-raises-70-mn-in-ser…

Safe Security raises $70 Mn in Series C led by Avataar Ventures
Cybersecurity startup Safe Security, previously known as Lucideus, has secured $70 million in a Series C round, led bybAvataar Ventures.

SourceSplice: Source Selection for Machine Learning Tasks
Ambarish Singh, Romila Pradhan
https://arxiv.org/abs/2507.22186 https://arxiv.org/pdf/2507.22186

SourceSplice: Source Selection for Machine Learning Tasks
Data quality plays a pivotal role in the predictive performance of machine learning (ML) tasks - a challenge amplified by the deluge of data sources available in modern organizations.Prior work in data discovery largely focus on metadata matching, semantic similarity or identifying tables that should be joined to answer a particular query, but do not consider source quality for high performance of the downstream ML task.This paper addresses the problem of determining the best subset of data sou…

А поясните чем доктору решетки эти так сентиментальны? То, что месяц работы в пизду это я понимаю. Но что-то же его заставило в эти решетки эту кучу времени и так сказать любви вложить, он же вроде мог останавливаться ещё на первом покрасе...
Не знаю, я может злорадный и бессердечный что не могу понять чем эти решетки ржавые так важны?.. Может я просто не достаточно следил и пропустил важный момент какой-то?

Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
Frida Sundfeldt, Bianca Widstam, Mahshid Helali Moghadam, Kuo-Yun Liang, Anders Vesterberg
https://arxiv.org/abs/2507.02607

Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
The digital evolution of connected vehicles and the subsequent security risks emphasize the critical need for implementing in-vehicle cyber security measures such as intrusion detection and response systems. The continuous advancement of attack scenarios further highlights the need for adaptive detection mechanisms that can detect evolving, unknown, and complex threats. The effective use of ML-driven techniques can help address this challenge. However, constraints on implementing diverse attack…

Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Ximeng Liu, Shizhen Zhao, Xinbing Wang
https://arxiv.org/abs/2506.23640

Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Recently, researchers have explored ML-based Traffic Engineering (TE), leveraging neural networks to solve TE problems traditionally addressed by optimization. However, existing ML-based TE schemes remain impractical: they either fail to handle topology changes or suffer from poor scalability due to excessive computational and memory overhead. To overcome these limitations, we propose Geminet, a lightweight and scalable ML-based TE framework that can handle changing topologies. Geminet is built…

Define-ML: An Approach to Ideate Machine Learning-Enabled Systems
Silvio Alonso, Antonio Pedro Santos Alves, Lucas Romao, H\'elio Lopes, Marcos Kalinowski
https://arxiv.org/abs/2506.20621

Define-ML: An Approach to Ideate Machine Learning-Enabled Systems
[Context] The increasing adoption of machine learning (ML) in software systems demands specialized ideation approaches that address ML-specific challenges, including data dependencies, technical feasibility, and alignment between business objectives and probabilistic system behavior. Traditional ideation methods like Lean Inception lack structured support for these ML considerations, which can result in misaligned product visions and unrealistic expectations. [Goal] This paper presents Define-M…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a ...
Pedro Pessoa, Juan Andres Martinez, Vincent Vandenbroucke, Frank Delvigne, Steve Press\'e

Integration of promising piezoelectric and photocatalytic properties in Janus In$XY$ ($X$ = S, Se, Te; $Y$ = Cl, Br, I) monolayers and their heterojunctions
Xinyue Liu, Ziqiang Li, Yanfeng Ge, Yong Liu, Xing Wang, Wenhui Wan
https://arxiv.org/abs/2509.00759

Integration of promising piezoelectric and photocatalytic properties in Janus In$XY$ ($X$ = S, Se, Te; $Y$ = Cl, Br, I) monolayers and their heterojunctions
Two-dimensional (2D) Janus materials show great promise as piezoelectric materials and photocatalysts for water splitting. In this work, we systematically investigated the piezoelectric and photocatalytic properties of the hexagonal Janus InXY (X = S, Se, Te; Y = Cl, Br, I) monolayers (MLs) using first-principles calculations. Except for InSeCl ML, the remaining eight InXY MLs are stable and exhibit exceptionally high in-plane piezoelectric coefficients ($|d_{22}| = $6.07--155.27 pm/V…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse P...
Fabian Schneider, Duc-Lam Duong, Matti Lassas, Maarten V. de Hoop, Tapio Helin

A Practical Flake Segmentation and Indexing Pipeline for Automated 2D Material Stacking
Yutao Li, Logan Sherlock, Ryan Benderson, Daniel Ostrom, Huandong Chen, Kazuhiro Fujita, Abhay Pasupathy
https://arxiv.org/abs/2509.01826

A Practical Flake Segmentation and Indexing Pipeline for Automated 2D Material Stacking
A cost-effective and robust image-processing pipeline is presented for the detection and characterization of exfoliated two-dimensional (2D) material flakes in optical microscope images, designed to facilitate automation in van der Waals heterostructure assembly. The system combines shallow machine learning (ML)-based material classification with a precision-first flake detection algorithm driven by edge morphology and color discontinuity. Step edges are resolved when supported by optical contr…

Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
Haichen Hu, David Simchi-Levi
https://arxiv.org/abs/2509.02476 https://

Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
We study the problem of evaluating the excess risk of classical penalized empirical risk minimization (ERM) with Bregman losses. We show that by leveraging the recently proposed wild refitting procedure (Wainwright, 2025), one can efficiently upper bound the excess risk through the so-called "wild optimism," without relying on the global structure of the underlying function class. This property makes our approach inherently model-free. Unlike conventional analyses, our framework operates with j…

Quantum-based QoE Optimization in Advanced Cellular Networks: Integration and Cloud Gaming Use Case
Fatma Chaouech, Javier Villegas, Ant\'onio Pereira, Carlos Baena, Sergio Fortes, Raquel Barco, Dominic Gribben, Mohammad Dib, Alba Villarino, Aser Cortines, Rom\'an Or\'us
https://arxiv.org/abs/2509.01008

Quantum-based QoE Optimization in Advanced Cellular Networks: Integration and Cloud Gaming Use Case
This work explores the integration of Quantum Machine Learning (QML) and Quantum-Inspired (QI) techniques for optimizing end-to-end (E2E) network services in telecommunication systems, particularly focusing on 5G networks and beyond. The application of QML and QI algorithms is investigated, comparing their performance with classical Machine Learning (ML) approaches. The present study employs a hybrid framework combining quantum and classical computing leveraging the strengths of QML and QI, wit…

Breaking Out from the TESSERACT: Reassessing ML-based Malware Detection under Spatio-Temporal Drift
Theo Chow, Mario D'Onghia, Lorenz Linhardt, Zeliang Kan, Daniel Arp, Lorenzo Cavallaro, Fabio Pierazzi
https://arxiv.org/abs/2506.23814

Breaking Out from the TESSERACT: Reassessing ML-based Malware Detection under Spatio-Temporal Drift
Several recent works focused on the best practices for applying machine learning to cybersecurity. In the context of malware, TESSERACT highlighted the impact of concept drift on detection performance and suggested temporal and spatial constraints to be enforced to ensure realistic time-aware evaluations, which have been adopted by the community. In this paper, we demonstrate striking discrepancies in the performance of learning-based malware detection across the same time frame when evaluated …

Unsupervised Dataset Cleaning Framework for Encrypted Traffic Classification
Kun Qiu, Ying Wang, Baoqian Li, Wenjun Zhu
https://arxiv.org/abs/2509.00701 https://

Unsupervised Dataset Cleaning Framework for Encrypted Traffic Classification
Traffic classification, a technique for assigning network flows to predefined categories, has been widely deployed in enterprise and carrier networks. With the massive adoption of mobile devices, encryption is increasingly used in mobile applications to address privacy concerns. Consequently, traditional methods such as Deep Packet Inspection (DPI) fail to distinguish encrypted traffic. To tackle this challenge, Artificial Intelligence (AI), in particular Machine Learning (ML), has emerged as a…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery
Zhen Qin, Michael B. Wakin, Zhihui Zhu

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Joner Assolin, Gabriel Canto, Diego Kreutz, Eduardo Feitosa, Hendrio Bragan\c{c}a, Angelo Nogueira, Vanderson Rocha
https://arxiv.org/abs/2506.23314

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to simplify ML development by reducing the need for specialized knowledge. However, current AutoML solutions typically operate as black-box systems with limited transparency, interpretability, and experiment traceability. To address these limitations, we present MH-AutoML, a domain-specific framework for Android malware d…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Privacy Auditing Synthetic Data Release through Local Likelihood Attacks
Joshua Ward, Chi-Hua Wang, Guang Cheng

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Neural-ANOVA: Analytical Model Decomposition using Automatic Integration
Steffen Limmer, Steffen Udluft, Clemens Otte

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- Connecting phases of matter to the flatness of the loss landscape in analog variational quantum a...
Kasidit Srimahajariyapong, Supanut Thanasilp, Thiparat Chotibut

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- Sparsing Law: Towards Large Language Models with Greater Activation Sparsity
Luo, Song, Han, Chen, Xiao, Meng, Deng, Wei, Liu, Sun

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Convergence analysis of online algorithms for vector-valued kernel regression
Michael Griebel, Peter Oswald

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Ahmed Sabbah, Radi Jarrar, Samer Zein, David Mohaisen
https://arxiv.org/abs/2507.22772 https://

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and nine machine learning and deep learning algorithms, as well as Large Language Models (LLMs). Various feature types--static, dynamic, hybrid, semantic, and image-based--were considered. The results showed…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
Brian Liu, Rahul Mazumder

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Explicit neural network classifiers for non-separable data
Patr\'icia Mu\~noz Ewald

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings
C. Shi, S. Zhang, W. Lu, R. Song

[2025-09-04 Thu (UTC), 7 new articles found for stat.ML Machine Learning]
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[2025-09-03 Wed (UTC), 20 new articles found for stat.ML Machine Learning]
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[2025-08-04 Mon (UTC), 2 new articles found for stat.ML Machine Learning]
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[2025-07-03 Thu (UTC), 4 new articles found for stat.ML Machine Learning]
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[2025-07-04 Fri (UTC), 5 new articles found for stat.ML Machine Learning]
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Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Active learning for level set estimation under input uncertainty and its extensions
Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation
Chris Cama\~no, Daniel Huang

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Discovering equations from data: symbolic regression in dynamical systems
Beatriz R. Brum, Luiza Lober, Isolde Previdelli, Francisco A. Rodrigues

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- The Pitfalls of Imitation Learning when Actions are Continuous
Max Simchowitz, Daniel Pfrommer, Ali Jadbabaie

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Tensor Completion with Nearly Linear Samples Given Weak Side Information
Christina Lee Yu, Xumei Xi

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Johan Hallberg Szabadv\'ary, Tuwe L\"ofstr\"om, Ulf Johansson, Cecilia S\"onstr\"od, Ernst Ahlberg, Lars Carlsson
https://arxiv.org/abs/2506.21802

Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a prediction. ML with reject option addresses this issue by abstaining from making a prediction if it is likely to be incorrect. In this work, we formalise the approach to ML with reject option in binary classification, deriving theoretical guarantees on the resulting error rate. This is achieved through conformal pred…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm
Stephen Salerno, Yi Li

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
L\'opez, Di Santi, Lefebvre, Mijatovic, Pugnaloni, Mart\'in, Saiah

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps
Chenxuan He, Yuan Gao, Liping Zhu, Jian Huang

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- High-dimensional Contextual Bandit Problem without Sparsity
Junpei Komiyama, Masaaki Imaizumi

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Learning the Simplest Neural ODE
Yuji Okamoto, Tomoya Takeuchi, Yusuke Sakemi

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Estimating oil recovery factor using machine learning: Applications of XGBoost classification
Roustazadeh, Ghanbarian, Male, Shadmand, Taslimitehrani, Lake

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Proofs as Explanations: Short Certificates for Reliable Predictions
Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Efficient uniform approximation using Random Vector Functional Link networks
Palina Salanevich, Olov Schavemaker

[2025-09-02 Tue (UTC), no new articles found for stat.ML Machine Learning]
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