Tootfinder

A 7-mag. #comet with a 3° tail nobody saw coming is grazing southern skies today: "SWAN 25B" (preliminary name) was discovered by amateurs in spacecraft images and seems to be in its 0.5 au perihelion today. Whether it's undergoing an outburst or will stay bright remains TBD: here's an image from Chile while a lot of discussion can be found in the threads https://groups.io/g/comets-ml/topic/115202009 and https://groups.io/g/comets-ml/topic/115202920

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Keyi Li, Yuval Kluger, Boris Landa

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Hangyi Jia, Yuxi Qian, Hanwen Tong, Xinhui Wu, Lin Chen, Feng Wei
https://arxiv.org/abs/2509.09321

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Recent advances in large language models (LLMs) have enabled the emergence of general-purpose agents for automating end-to-end machine learning (ML) workflows, including data analysis, feature engineering, model training, and competition solving. However, existing benchmarks remain limited in task coverage, domain diversity, difficulty modeling, and evaluation rigor, failing to capture the full capabilities of such agents in realistic settings. We present TAM Bench, a diverse, realistic, and st…

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

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

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

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

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Uncertainty Estimation using Variance-Gated Distributions
H. Martin Gillis, Isaac Xu, Thomas Trappenberg

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

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

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

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

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

from my link log —
A new Concurrent ML in Guile Scheme.
https://www.wingolog.org/archives/2017/06/29/a-new-concurrent-ml
saved 2025-07-26

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
Brandon Baker, Elliott Brossard, Chenwei Xie, Zihao Ye, Deen Liu, Yijun Xie, Arthur Zwiegincew, Nitya Kumar Sharma, Gaurav Jain, Eugene Retunsky, Mike Halcrow, Derek Denny-Brown, Istvan Cseri, Tyler Akidau, Yuxiong He
https://arxiv.org/abs/2508.05904…

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
Snowflake revolutionized data analytics with an elastic architecture that decouples compute and storage, enabling scalable solutions supporting data architectures like data lake, data warehouse, data lakehouse, and data mesh. Building on this foundation, Snowflake has advanced its AI Data Cloud vision by introducing Snowpark, a managed turnkey solution that supports data engineering and AI and ML workloads using Python and other programming languages. This paper outlines Snowpark's design obj…

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

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

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

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Haoyu Dong, Pengkun Zhang, Mingzhe Lu, Yanzhen Shen, Guolin Ke
https://arxiv.org/abs/2509.06806

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machine learning (ML) tasks, that is, to leverage many-shot demonstrations purely via in-context learning (ICL) without gradient descent. We introduce MachineLearningLM, a portable continued-pretraining framework that equips a general-purpose LLM with robust in-context ML capability while preserving its general knowledge an…

From #lemmy #uk

A Living Review Pipeline for AI/ML Applications in Accelerator Physics
Adnan Ghribi
https://arxiv.org/abs/2510.09376 https://arxiv.org/pdf/2510.09376

A Living Review Pipeline for AI/ML Applications in Accelerator Physics
We present an open-source pipeline for generating a \emph{living review} of artificial intelligence (AI) and machine learning (ML) applications in accelerator physics and technologies. Traditional review articles provide static snapshots that are quickly outdated by the rapid pace of research. The presented system automatically harvests publications from multiple bibliographic sources (arXiv, InspireHEP, HAL, OpenAlex, Crossref, and Springer), deduplicates entries, applies semantic filtering to…

Poland says it has shot down drones that entered its airspace during Russian attack on Ukraine (CNN)
https://www.cnn.com/2025/09/09/europe/poland-scramble-jets-russian-drone-reports-intl-hnk-ml
http://www.memeorandum.com/250909/p180#a250909p180

Sagt mir bitte, dass der Preis ein Tipfehler ist.
Floris Santal (Aftershave Lotion, 100 ml) https://www.galaxus.ch/de/s6/product/floris-santal-aftershave-lotion-100-ml-aftershave-15970692

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

OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Youssef Esseddiq Ouatiti, Mohammed Sayagh, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2508.08545

OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Developers insert logging statements in source code to capture relevant runtime information essential for maintenance and debugging activities. Log level choice is an integral, yet tricky part of the logging activity as it controls log verbosity and therefore influences systems' observability and performance. Recent advances in ML-based log level prediction have leveraged large language models (LLMs) to propose log level predictors (LLPs) that demonstrated promising performance improvements (AU…

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

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

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

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

Physical Design Exploration of a Wire-Friendly Domain-Specific Processor for Angstrom-Era Nodes
Lorenzo Ruotolo, Lara Orlandic, Pengbo Yu, Moritz Brunion, Daniele Jahier Pagliari, Dwaipayan Biswas, Giovanni Ansaloni, David Atienza, Julien Ryckaert, Francky Catthoor, Yukai Chen
https://arxiv.org/abs/2508.07110…

Physical Design Exploration of a Wire-Friendly Domain-Specific Processor for Angstrom-Era Nodes
This paper presents the physical design exploration of a domain-specific processor (DSIP) architecture targeted at machine learning (ML), addressing the challenges of interconnect efficiency in advanced Angstrom-era technologies. The design emphasizes reduced wire length and high core density by utilizing specialized memory structures and SIMD (Single Instruction, Multiple Data) units. Five configurations are synthesized and evaluated using the IMEC A10 nanosheet node PDK. Key physical design m…

Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example
Yahya Sherif Solayman Mohamed Saleh, Ahmed Mohammed Dabbous, Lama Alkhaled, Hum Yan Chai, Muhammad Ehsan Rana, Hamam Mokayed
https://arxiv.org/abs/2508.08892

Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example
One of the fastest-growing domains in AI is healthcare. Given its importance, it has been the interest of many researchers to deploy ML models into the ever-demanding healthcare domain to aid doctors and increase accessibility. Delivering reliable models, however, demands a sizable amount of data, and the recent COVID-19 pandemic served as a reminder of the rampant and scary nature of healthcare that makes training models difficult. To alleviate such scarcity, many published works attempted to …

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

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

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

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

Computations and ML for surjective rational maps
Ilya Karzhemanov
https://arxiv.org/abs/2510.08093 https://arxiv.org/pdf/2510.08093

Computations and ML for surjective rational maps
The present note studies \emph{surjective rational endomorphisms} $f: \mathbb{P}^2 \dashrightarrow \mathbb{P}^2$ with \emph{cubic} terms and the indeterminacy locus $I_f \ne \emptyset$. We develop an experimental approach, based on some Python programming and Machine Learning, towards the classification of such maps; a couple of new explicit $f$ is constructed in this way. We also prove (via pure projective geometry) that a general non-regular cubic endomorphism $f$ of $\mathbb{P}^2$ is surject…

Joint Optimisation of Load Balancing and Energy Efficiency for O-RAN Deployments
Mohammed M. H. Qazzaz, Abdelaziz Salama, Maryam Hafeez, Syed A. R. Zaidi
https://arxiv.org/abs/2509.09343

Joint Optimisation of Load Balancing and Energy Efficiency for O-RAN Deployments
Open Radio Access Network (O-RAN) architecture provides an intrinsic capability to exploit key performance monitoring (KPM) within Radio Intelligence Controller (RIC) to derive network optimisation through xApps. These xApps can leverage KPM knowledge to dynamically switch on/off the associated RUs where such a function is supported over the E2 interface. Several existing studies employ artificial intelligence (AI)/Machine Learning (ML) based approaches to realise such dynamic sleeping for incr…

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization
Moinuddin Muhammad Imtiaz Bhuiyan, Kazi Ekramul Hoque, Rakibul Islam, Md. Mahbubur Rahman Tusher, Najmul Hassan, Yoichi Tomioka, Satoshi Nishimura, Jungpil Shin, Abu Saleh Musa Miah
https://arxiv.org/abs/2510.05835

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization
This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To overcome these issues, we propose a machine learning-based model that automatically and accurately identifies code smells, offering a scalable solution for software quality analysis. The novelty of our approach lies in the use of eight diverse ML algorithms, incl…

Unusual ferromagnetic band evolution and high Curie temperature in monolayer 1T-CrTe2 on bilayer graphene
Kyoungree Park, Ji-Eun Lee, Dongwook Kim, Yong Zhong, Camron Farhang, Hyobeom Lee, Hayoon Im, Woojin Choi, Seha Lee, Seungrok Mun, Kyoo Kim, Jun Woo Choi, Hyejin Ryu, Jing Xia, Heung-Sik Kim, Choongyu Hwang, Ji Hoon Shim, Zhi-Xun Shen, Sung-Kwan Mo, Jinwoong Hwang

Unusual ferromagnetic band evolution and high Curie temperature in monolayer 1T-CrTe2 on bilayer graphene
2D van der Waals ferromagnets hold immense promise for spintronic applications due to their controllability and versatility. Despite their significance, the realization and in-depth characterization of ferromagnetic materials in atomically thin single layers, close to the true 2D limit, has been scarce. Here, a successful synthesis of monolayer (ML) 1T-CrTe2 is reported on a bilayer graphene (BLG) substrate via molecular beam epitaxy. Using angle-resolved photoemission spectroscopy and magneto-…

Amazon Ads Multi-Touch Attribution
Randall Lewis, Florian Zettelmeyer, Brett R. Gordon, Cristobal Garib, Johannes Hermle, Mike Perry, Henrique Romero, German Schnaidt
https://arxiv.org/abs/2508.08209

Amazon Ads Multi-Touch Attribution
Amazon's new Multi-Touch Attribution (MTA) solution allows advertisers to measure how each touchpoint across the marketing funnel contributes to a conversion. This gives advertisers a more comprehensive view of their Amazon Ads performance across objectives when multiple ads influence shopping decisions. Amazon MTA uses a combination of randomized controlled trials (RCTs) and machine learning (ML) models to allocate credit for Amazon conversions across Amazon Ads touchpoints in proportion to th…

Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning
Manuel Gallego, Sebasti\'an Roca-Jerat, David Zueco, Jes\'us Carrete
https://arxiv.org/abs/2508.06441

Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning
Machine-learning (ML) ansätze have greatly expanded the accuracy and reach of variational quantum Monte Carlo (QMC) calculations, in particular when exploring the manifold quantum phenomena exhibited by spin systems. However, the scalability of QMC is still compromised by several other bottlenecks, and specifically those related to the actual evaluation of observables based on random deviates that lies at the core of the approach. Here we show how the set-transformer architecture can be used t…

scrape me harder metadaddy 🥺
https://cyberpunk.lol/@FediPact/115000125449696514

CryptGNN: Enabling Secure Inference for Graph Neural Networks
Pritam Sen, Yao Ma, Cristian Borcea
https://arxiv.org/abs/2509.09107 https://arxiv.org/pdf/25…

CryptGNN: Enabling Secure Inference for Graph Neural Networks
We present CryptGNN, a secure and effective inference solution for third-party graph neural network (GNN) models in the cloud, which are accessed by clients as ML as a service (MLaaS). The main novelty of CryptGNN is its secure message passing and feature transformation layers using distributed secure multi-party computation (SMPC) techniques. CryptGNN protects the client's input data and graph structure from the cloud provider and the third-party model owner, and it protects the model paramete…

Taking the Garbage Out of Data-Driven Prediction Across Climate Timescales
Jason C. Furtado, Maria J. Molina, Marybeth C. Arcodia, Weston Anderson, Tom Beucler, John A. Callahan, Laura M. Ciasto, Vittorio A. Gensini, Michelle L'Heureux, Kathleen Pegion, Jhayron S. P\'erez-Carrasquilla, Maike Sonnewald, Ken Takahashi, Baoqiang Xiang, Brian G. Zimmerman
h…

Taking the Garbage Out of Data-Driven Prediction Across Climate Timescales
Artificial intelligence (AI) -- and specifically machine learning (ML) -- applications for climate prediction across timescales are proliferating quickly. The emergence of these methods prompts a revisit to the impact of data preprocessing, a topic familiar to the climate community, as more traditional statistical models work with relatively small sample sizes. Indeed, the skill and confidence in the forecasts produced by data-driven models are directly influenced by the quality of the datasets…

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors
Benjamin Parpillon, Anthony Badea, Danush Shekar, Christian Gingu, Giuseppe Di Guglielmo, Tom Deline, Adam Quinn, Michele Ronchi, Benjamin Weiss, Jennet Dickinson, Jieun Yoo, Corrinne Mills, Daniel Abadjiev, Eric You, Mira Littmann, Karri DiPetrillo, Arghya Ranjan Das, Mia Liu, David Jiang, Mark S. Neubauer, Morris Swartz, Petar Maksimovic, Alice Bean, Chinar Syal, Doug Berry, Nhan …

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors
We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped…

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…

Human vs. machine -- 1:3. Joint analysis of classical and ML-based summary statistics of the Lyman-$\alpha$ forest
S. Chang, P. Nayak, M. Walther, D. Gruen
https://arxiv.org/abs/2508.03264

Human vs. machine -- 1:3. Joint analysis of classical and ML-based summary statistics of the Lyman-$α$ forest
In order to compress and more easily interpret Lyman-$α$ forest (Ly$α$F) datasets, summary statistics, e.g. the power spectrum, are commonly used. However, such summaries unavoidably lose some information, weakening the constraining power on parameters of interest. Recently, machine learning (ML)-based summary approaches have been proposed as an alternative to human-defined statistical measures. This raises a question: can ML-based summaries contain the full information captured by traditiona…

#Firefox sends marketing pushes
I don't get it... I use it daily, so you can't send me the thing second time!

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Latency-Aware Contextual Bandit: Application to Cryo-EM Data Collection
Lai Wei, Ambuj Tewari, Michael A. Cianfrocco

The Fair Game: Auditing & Debiasing AI Algorithms Over Time
Debabrota Basu, Udvas Das
https://arxiv.org/abs/2508.06443 https://arxiv.org/pdf/2508.06443…

The Fair Game: Auditing & Debiasing AI Algorithms Over Time
An emerging field of AI, namely Fair Machine Learning (ML), aims to quantify different types of bias (also known as unfairness) exhibited in the predictions of ML algorithms, and to design new algorithms to mitigate them. Often, the definitions of bias used in the literature are observational, i.e. they use the input and output of a pre-trained algorithm to quantify a bias under concern. In reality,these definitions are often conflicting in nature and can only be deployed if either the ground t…

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

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

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estim...
Lemos, Sharief, Malkin, Salhi, Stone, Perreault-Levasseur, Hezaveh

SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks
Jing Chen, Onat Gungor, Zhengli Shang, Tajana Rosing
https://arxiv.org/abs/2509.08091

SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks
The rapid proliferation of the Internet of Things (IoT) continues to expose critical security vulnerabilities, necessitating the development of efficient and robust intrusion detection systems (IDS). Machine learning-based intrusion detection systems (ML-IDS) have significantly improved threat detection capabilities; however, they remain highly susceptible to adversarial attacks. While numerous defense mechanisms have been proposed to enhance ML-IDS resilience, a systematic approach for selecti…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation
Ho Ming Lee, Katrien Antonio, Benjamin Avanzi, Lorenzo Marchi, Rui Zhou

LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
Po-Han Lee, Yu-Cheng Lin, Chan-Tung Ku, Chan Hsu, Pei-Cing Huang, Ping-Hsun Wu, Yihuang Kang
https://arxiv.org/abs/2508.07221

LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
Estimating individualized treatment effects from observational data presents a persistent challenge due to unmeasured confounding and structural bias. Causal Machine Learning (causal ML) methods, such as causal trees and doubly robust estimators, provide tools for estimating conditional average treatment effects. These methods have limited effectiveness in complex real-world environments due to the presence of latent confounders or those described in unstructured formats. Moreover, reliance on …

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Generalization Bound for Diffusion Models using Random Features
Esha Saha, Giang Tran

Quantum machine unlearning
Junjian Su, Runze He, Guanghui Li, Sujuan Qin, Zhimin He, Haozhen Situ, Fei Gao
https://arxiv.org/abs/2509.06086 https://arxiv.o…

Quantum machine unlearning
Quantum Machine Learning (QML) integrates quantum computation with classical Machine Learning (ML) and holds the potential to achieve the quantum advantage for specific tasks. In classical ML, Machine Unlearning (MU) is a crucial strategy for removing the influence of specified training data from a model, to meet regulatory requirements and mitigate privacy risks. However, both the risk of training-data membership leakage remains underexplored in QML. This motivates us to propose Quantum Machin…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- LLM meets ML: Data-efficient Anomaly Detection on Unstable Logs
Fatemeh Hadadi, Qinghua Xu, Domenico Bianculli, Lionel Briand

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

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

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning
Md. Saiful Bari Siddiqui, Md Mohaiminul Islam, Md. Golam Rabiul Alam

An ML-based Approach to Predicting Software Change Dependencies: Insights from an Empirical Study on OpenStack
Arabat, Ali, Sayagh, Mohammed, Hassine, Jameleddine
https://arxiv.org/abs/2508.05034 …

An ML-based Approach to Predicting Software Change Dependencies: Insights from an Empirical Study on OpenStack
As software systems grow in complexity, accurately identifying and managing dependencies among changes becomes increasingly critical. For instance, a change that leverages a function must depend on the change that introduces it. Establishing such dependencies allows CI/CD pipelines to build and orchestrate changes effectively, preventing build failures and incomplete feature deployments. In modern software systems, dependencies often span multiple components across teams, creating challenges fo…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Analytic theory of dropout regularization
Francesco Mori, Francesca Mignacco
…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures
Adrien Bolland, Gaspard Lambrechts, Damien Ernst

To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera, Roberto Pellungrini, Bruno Lepri, Andrea Passerini
https://arxiv.org/abs/2510.08314

To Ask or Not to Ask: Learning to Require Human Feedback
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machine Learning (ML) model to pass difficult cases to a human expert. However, LtD treats humans and ML models as mutually exclusive decision-makers, restricting the expert contribution to mere predictions. To address this limitation, we propose Learning to Ask (LtA), a new framework that handles both when and how to inco…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- On Rate-Optimal Partitioning Classification from Observable and from Privatised Data
Bal\'azs Csan\'ad Cs\'aji, L\'aszl\'o Gy\"orfi, Ambrus Tam\'as, Harro Walk

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Toric geometry of ReLU neural networks
Yaoying Fu
https://

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

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

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- An Empirical Analysis of the Laplace and Neural Tangent Kernels
Ronaldas Paulius Lencevi\v{c}ius

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- A Mixed-Methods Analysis of Repression and Mobilization in Bangladesh's July Revolution Using Mac...
Md. Saiful Bari Siddiqui, Anupam Debashis Roy

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Nonnegative matrix factorization and the principle of the common cause
E. Khalafyan, A. E. Allahverdyan, A. Hovhannisyan

[2025-09-12 Fri (UTC), 3 new articles found for stat.ML Machine Learning]
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[2025-08-13 Wed (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
[2/2]:
- A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
Efthymios Costa, Ioanna Papatsouma, Angelos Markos

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Mixtures of Gaussian Process Experts with SMC$^2$
Teemu H\"ark\"onen, Sara Wade, Kody Law, Lassi Roininen

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Model-free generalized fiducial inference
Jonathan P Williams
https://…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- Domain Generalization: A Tale of Two ERMs
Yilun Zhu, Naihao Deng, Naichen Shi, Aditya Gangrade, Clayton Scott

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- Group Policy Gradient
Junhua Chen, Zixi Zhang, Hantao Zhong, Rika Antonova

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
Zheng, Venkatasubramanian, Li, Braverman, Ke, Hou, Jin, Agrawal

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Yu Zhu

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

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Optimal Learning via Moderate Deviations Theory
Arnab Ganguly, Tobias Sutter
…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Gradient Descent with Large Step Sizes: Chaos and Fractal Convergence Region
Shuang Liang, Guido Mont\'ufar

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Discrimination in machine learning algorithms
Roberta Pappad\`a, Francesco Pauli

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Orthogonal Procrustes problem preserves correlations in synthetic data
Oussama Ounissi, Nicklas J\"averg\r{a}rd, Adrian Muntean

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state ...
Wojnowicz, Gili, Rath, Miller, Miller, Hancock, O'Donovan, Elkin-Frankston, Bruny\'e, Hughes

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

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robu...
Andrea Coccaro, Marco Letizia, Humberto Reyes-Gonzalez, Riccardo Torre

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias
Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed, Jonathan Weare

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences
Arpan Mukherjee, Marcello Bullo, Deniz G\"und\"uz

[2025-09-11 Thu (UTC), 4 new articles found for stat.ML Machine Learning]
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[2025-08-11 Mon (UTC), 6 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]:
- 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

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

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

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

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

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

Decorrelated feature importance from local sample weighting
Benedikt Fr\"ohlich, Alison Durst, Merle Behr
https://arxiv.org/abs/2508.06337 https://arx…

Decorrelated feature importance from local sample weighting
Feature importance (FI) statistics provide a prominent and valuable method of insight into the decision process of machine learning (ML) models, but their effectiveness has well-known limitations when correlation is present among the features in the training data. In this case, the FI often tends to be distributed among all features which are in correlation with the response-generating signal features. Even worse, if multiple signal features are in strong correlation with a noise feature, while…

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

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Baron, Oreshkin, Ma, Zhang, Torkkola, Mahoney, Wilson, Konstantinova

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Efficient Probabilistic Visualization of Local Divergence of 2D Vector Fields with Independent Ga...
Ouermi, Li, Moreland, Pugmire, Johnson, Athawale

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]:
- BOOST: Bayesian Optimization with Optimal Kernel and Acquisition Function Selection Technique
Joon-Hyun Park, Mujin Cheon, Jeongsu Wi, Dong-Yeun Koh

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Asymptotic Classification Error for Heavy-Tailed Renewal Processes
Xinhui Rong, Victor Solo

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers
Dingling Yao, Filip Tronarp, Nathanael Bosch

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cogni...
Fahad Mostafa, Kannon Hossain, Hafiz Khan

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- Localized Diffusion Models
Georg A. Gottwald, Shuigen Liu, Youssef Marzouk, Sebastian Reich, Xin T. Tong

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- Two-Stage Nuisance Function Estimation for Causal Mediation Analysis
Chang Liu, AmirEmad Ghassami

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Network inference via process motifs for lagged correlation in linear stochastic processes
Alice C. Schwarze, Sara M. Ichinaga, Bingni W. Brunton