Tootfinder

Query Efficient Structured Matrix Learning
Noah Amsel, Pratyush Avi, Tyler Chen, Feyza Duman Keles, Chinmay Hegde, Cameron Musco, Christopher Musco, David Persson
https://arxiv.org/abs/2507.19290

Query Efficient Structured Matrix Learning
We study the problem of learning a structured approximation (low-rank, sparse, banded, etc.) to an unknown matrix $A$ given access to matrix-vector product (matvec) queries of the form $x \rightarrow Ax$ and $x \rightarrow A^Tx$. This problem is of central importance to algorithms across scientific computing and machine learning, with applications to fast multiplication and inversion for structured matrices, building preconditioners for first-order optimization, and as a model for differential …

This https://arxiv.org/abs/2410.09516 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Bridging the Gap Between Data-Driven And Theory-Driven Modelling - Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems
Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by enhancing predictive robustness. However, constructing an initial causal graph manually using domain knowledge is time-consuming, particularly in complex time series with numerous variables. To address this, causal discovery algorithms can provide a preliminary ca…

Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies
Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi, Fatemeh Abedini, Atousa Kalantari, Marziye Jafariyazani, Nima Chartab
https://arxiv.org/abs/2506.21067

Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies
Unveiling the evolutionary history of galaxies necessitates a precise understanding of their physical properties. Traditionally, astronomers achieve this through spectral energy distribution (SED) fitting. However, this approach can be computationally intensive and time-consuming, particularly for large datasets. This study investigates the viability of machine learning (ML) algorithms as an alternative to traditional SED-fitting for estimating stellar masses in galaxies. We compare a diverse r…

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…

This https://arxiv.org/abs/2406.11369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

Approximation Algorithms for Smallest Intersecting Balls
We study a general smallest intersecting ball problem and its soft-margin variant in high-dimensional Euclidean spaces for input objects that are compact and convex. These two problems link and unify a series of fundamental problems in computational geometry and machine learning, including smallest enclosing ball, polytope distance, intersection radius, $\ell_1$-loss support vector machine, $\ell_1$-loss support vector data description, and so on. Leveraging our novel framework for solving zero…

Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms
V. Anagnostou, E. Papadimitriou, V. Karakostas, T. Back
https://arxiv.org/abs/2505.17221

Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms
In March 2024, a swarm like seismic activity occurred north of Kefalonia Island, in the central Ionian Islands area. Following a machine-learning aided workflow, we compiled an enhanced seismic catalog of 2495 low to moderate magnitude earthquakes throughout a 2 month period. Spatiotemporal analysis reveals a narrow epicentral distribution of nearly E-W alignment, approximately 5km long, much longer than the length anticipated by common scaling laws for the aftershock area extension of the stro…

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 …

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 …

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 …

Learning Acceleration Algorithms for Fast Parametric Convex Optimization with Certified Robustness
Rajiv Sambharya, Jinho Bok, Nikolai Matni, George Pappas
https://arxiv.org/abs/2507.16264

Learning Acceleration Algorithms for Fast Parametric Convex Optimization with Certified Robustness
We develop a machine-learning framework to learn hyperparameter sequences for accelerated first-order methods (e.g., the step size and momentum sequences in accelerated gradient descent) to quickly solve parametric convex optimization problems with certified robustness. We obtain a strong form of robustness guarantee -- certification of worst-case performance over all parameters within a set after a given number of iterations -- through regularization-based training. The regularization term is …

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
Amoy Ashesh, Harsimran Kaur, Sandeep Aashish
https://arxiv.org/abs/2506.19942

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
We present a machine learning (ML) framework for the detection of wide binary star systems and the identification of deviations from standard gravity using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. We recast the problem of identifying systematic deviations from standard gravity as a supervised anomaly detection. Our approach incorporate…

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
Dmitry Kondratyev, Benedikt Riedel, Yuan-Tang Chou, Miles Cochran-Branson, Noah Paladino, David Schultz, Mia Liu, Javier Duarte, Philip Harris, Shih-Chieh Hsu
https://arxiv.org/abs/2506.20657

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
The increasing computational demand from growing data rates and complex machine learning (ML) algorithms in large-scale scientific experiments has driven the adoption of the Services for Optimized Network Inference on Coprocessors (SONIC) approach. SONIC accelerates ML inference by offloading it to local or remote coprocessors to optimize resource utilization. Leveraging its portability to different types of coprocessors, SONIC enhances data processing and model deployment efficiency for cuttin…

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
Jiayong Qin, Xianyu Zhu, Qiyu Liu, Guangyi Zhang, Zhigang Cai, Jianwei Liao, Sha Hu, Jingshu Peng, Yingxia Shao, Lei Chen
https://arxiv.org/abs/2506.20139

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
A growing trend in the database and system communities is to augment conventional index structures, such as B+-trees, with machine learning (ML) models. Among these, error-bounded Piecewise Linear Approximation ($ε$-PLA) has emerged as a popular choice due to its simplicity and effectiveness. Despite its central role in many learned indexes, the design and analysis of $ε$-PLA fitting algorithms remain underexplored. In this paper, we revisit $ε$-PLA from both theoretical and empirical perspe…

Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Keyi Li, Yuval Kluger, Boris Landa
https://arxiv.org/abs/2507.18520 https://arxiv…

Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Pairwise Euclidean distance calculation is a fundamental step in many machine learning and data analysis algorithms. In real-world applications, however, these distances are frequently distorted by heteroskedastic noise$\unicode{x2014}$a prevalent form of inhomogeneous corruption characterized by variable noise magnitudes across data observations. Such noise inflates the computed distances in a nontrivial way, leading to misrepresentations of the underlying data geometry. In this work, we addre…

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
Wenlong Ji, Yihan Pan, Ruihao Zhu, Lihua Lei
https://arxiv.org/abs/2506.16658 https:/…

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
Multi-armed bandit (MAB) is a widely adopted framework for sequential decision-making under uncertainty. Traditional bandit algorithms rely solely on online data, which tends to be scarce as it must be gathered during the online phase when the arms are actively pulled. However, in many practical settings, rich auxiliary data, such as covariates of past users, is available prior to deploying any arms. We introduce a new setting for MAB where pre-trained machine learning (ML) models are applied t…

Data Compression with Relative Entropy Coding
Gergely Flamich
https://arxiv.org/abs/2506.16309 https://arxiv.org/pdf/2506.16309

Data Compression with Relative Entropy Coding
Over the last few years, machine learning unlocked previously infeasible features for compression, such as providing guarantees for users' privacy or tailoring compression to specific data statistics (e.g., satellite images or audio recordings of animals) or users' audiovisual perception. This, in turn, has led to an explosion of theoretical investigations and insights that aim to develop new fundamental theories, methods and algorithms better suited for machine learning-based compressors. In…

A comparative analysis of machine learning algorithms for predicting probabilities of default
Adrian Iulian Cristescu, Matteo Giordano
https://arxiv.org/abs/2506.19789

A comparative analysis of machine learning algorithms for predicting probabilities of default
Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosti…

PGR-DRC: Pre-Global Routing DRC Violation Prediction Using Unsupervised Learning
Riadul Islam, Dhandeep Challagundla
https://arxiv.org/abs/2507.13355 https…

PGR-DRC: Pre-Global Routing DRC Violation Prediction Using Unsupervised Learning
Leveraging artificial intelligence (AI)-driven electronic design and automation (EDA) tools, high-performance computing, and parallelized algorithms are essential for next-generation microprocessor innovation, ensuring continued progress in computing, AI, and semiconductor technology. Machine learning-based design rule checking (DRC) and lithography hotspot detection can improve first-pass silicon success. However, conventional ML and neural network (NN)-based models use supervised learning and…

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

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

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…

Discrimination of neutron-$\gamma$ in the low energy regime using machine learning for an EJ-276D plastic scintillator
S. Panda (a,b), P. K. Netrakanti (a), S. P. Behera (a,b), R. R. Sahu (a), K. Kumar (a,b), R. Sehgal (a), D. K. Mishra (a,b), V. Jha (a,b)
https://arxiv.org/abs/2506.13802

Discrimination of neutron-$γ$ in the low energy regime using machine learning for an EJ-276D plastic scintillator
In this work, we present results for discrimination of neutron and $γ$ events using a plastic scintillator detector with pulse shape discrimination capabilities. Machine learning (ML) algorithms are used to improve the discriminatory power between neutron and $γ$ events at lower energy ranges which otherwise are not addressed by the conventional pulse shape discrimination techniques. The use of a multilayer perceptron with Bayesian inference (MLPBNN) and support vector machine (SVM) algorithm…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and ...
Debabrota Basu, Debarshi Chanda

Replaced article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
- Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges
Zahraa Al Sahili, Ioannis Patras, Matthew Purver

Optimal Calibrated Signaling in Digital Auctions
Zhicheng Du, Wei Tang, Zihe Wang, Shuo Zhang
https://arxiv.org/abs/2507.17187 https://arxiv.org/pdf/2507.1…

Optimal Calibrated Signaling in Digital Auctions
In digital advertising, online platforms allocate ad impressions through real-time auctions, where advertisers typically rely on autobidding agents to optimize bids on their behalf. Unlike traditional auctions for physical goods, the value of an ad impression is uncertain and depends on the unknown click-through rate (CTR). While platforms can estimate CTRs more accurately using proprietary machine learning algorithms, these estimates/algorithms remain opaque to advertisers. This information as…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Preston G. Waldrop, Dimitrios Psaltis, Tong Zhao
https://arxiv.org/abs/2506.11194 http…

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling
Ray tracing algorithms that compute pulse profiles from rotating neutron stars are essential tools for constraining neutron-star properties with data from missions such as NICER. However, the high computational cost of these simulations presents a significant bottleneck for inference algorithms that require millions of evaluations, such as Markov Chain Monte Carlo methods. In this work, we develop a residual neural network model that accelerates this calculation by predicting the observed flux …

Advancing Quantum State Preparation using LimTDD
Xin Hong, Aochu Dai, Chenjian Li, Sanjiang Li, Shenggang Ying, Mingsheng Ying
https://arxiv.org/abs/2507.17170 https://

Advancing Quantum State Preparation using LimTDD
Quantum state preparation (QSP) is a fundamental task in quantum computing and quantum information processing. It is critical to the execution of many quantum algorithms, including those in quantum machine learning. In this paper, we propose a family of efficient QSP algorithms tailored to different numbers of available ancilla qubits - ranging from no ancilla qubits, to a single ancilla qubit, to a sufficiently large number of ancilla qubits. Our algorithms are based on a novel decision diagra…

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider
https://arxiv.org/abs/2506.10287

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often affect dynamics beyond the experiment's duration. Existing tools like reinforcement learning, supervised learning, and Bayesian optimization address some of these challenges but fail to provide a comprehensive solution. To overcome these limi…

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Yining Pang, Chenghan Li
https://arxiv.org/abs/2506.19871

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithms have demonstrated strong performance in detecting fraudulent claims, the absence of standardized defense mechanisms renders current systems vulnerable to emerging adversarial threats. In this paper, w…

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

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
Guobin Zhao, Pengyu Zhao, Yongchul G. Chung
https://arxiv.org/abs/2506.14845

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
The computational discovery and design of new crystalline materials, particularly metal-organic frameworks (MOFs), heavily relies on high-quality, computation-ready structural data. However, recent studies have revealed significant error rates within existing MOF databases, posing a critical data problem that hinders efficient high-throughput computational screening. While rule-based algorithms like MOSAEC, MOFChecker, and the Chen and Manz method (Chen-Manz) have been developed to address this…

X-ray transferable polyrepresentation learning
Weronika Hryniewska-Guzik, Przemyslaw Biecek
https://arxiv.org/abs/2507.06264 https://…

X-ray transferable polyrepresentation learning
The success of machine learning algorithms is inherently related to the extraction of meaningful features, as they play a pivotal role in the performance of these algorithms. Central to this challenge is the quality of data representation. However, the ability to generalize and extract these features effectively from unseen datasets is also crucial. In light of this, we introduce a novel concept: the polyrepresentation. Polyrepresentation integrates multiple representations of the same modality…

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

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

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
Wishnu Badrawani
https://arxiv.org/abs/2506.10369

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia's inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performance of each ML model, namely shrinkage regression, ensemble learning, and super vector regression, to that of the univariate time series ARIMA and SARIMA models. We examine various out-of-bag sample periods in each ML model to determine the appropriate data-splitting ratios for …

The miniJPAS survey quasar selection V: combined algorithm
Ignasi P\'erez-R\`afols, L. Raul Abramo, Gin\'es Mart\'inez-Solaeche, Nat\'alia V. N. Rodrigues, Matthew M. Pieri, Marina Burjal\`es-del-Amo, Maria Escol\`a-Gallinat, Montserrat Ferr\'e-Abad, Mireia Isern-Vizoso, Jailson Alcaniz, Narciso Benitez, Silvia Bonoli, Saulo Carneiro, Javier Cenarro, David Crist\'obal-Hornillos, Renato Dupke, Alessandro Ederoclite, Rosa Mar\'ia Gonz\'alez Delgado, Siddha…

The miniJPAS survey quasar selection V: combined algorithm
Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or Baryon Acoustic Oscillations. Here, we present the final quasar catalogue, including redshift estimates, from the miniJPAS Data Release constructed using several flavours of machine-learning algorithms. Methods. In this work, we use a machine learning algorithm to classify quasars, optimally combining…

(Exhaustive) Symbolic Regression and model selection by minimum description length
Harry Desmond
https://arxiv.org/abs/2507.13033 https://

(Exhaustive) Symbolic Regression and model selection by minimum description length
Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the…

Application of quantum machine learning using variational quantum classifier in accelerator physics
He-Xing Yin, Zhi-Yuan Hu, Huan-Huan Zeng, Jia-Bao Guan, Ji-ke Wang
https://arxiv.org/abs/2506.06662

Application of quantum machine learning using variational quantum classifier in accelerator physics
Quantum machine learning algorithms aim to take advantage of quantum computing to improve classical machine learning algorithms. In this paper, we have applied a quantum machine learning algorithm, the variational quantum classifier for the first time in accelerator physics. Specifically, we utilized the variational quantum classifier to evaluate the dynamic aperture of a diffraction-limited storage ring. It has been demonstrated that the variational quantum classifier can achieve good accuracy…

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…

Ghost in the Machine: Examining the Philosophical Implications of Recursive Algorithms in Artificial Intelligence Systems
Llewellin RG Jegels
https://arxiv.org/abs/2507.01967

Ghost in the Machine: Examining the Philosophical Implications of Recursive Algorithms in Artificial Intelligence Systems
This paper investigates whether contemporary AI architectures employing deep recursion, meta-learning, and self-referential mechanisms provide evidence of machine consciousness. Integrating philosophical history, cognitive science, and AI engineering, it situates recursive algorithms within a lineage spanning Cartesian dualism, Husserlian intentionality, Integrated Information Theory, the Global Workspace model, and enactivist perspectives. The argument proceeds through textual analysis, compar…

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

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

A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions
Lang Qin, Yuejin Xie, Daili Hua, Xuhui Meng
https://arxiv.org/abs/2506.14638

A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions
Extreme weather events increasingly threaten the insurance and real estate industries, creating conflicts between profitability and homeowner burdens. To address this, we propose the SSC-Insurance Model, which integrates SMOTE, SVM, and C-D-C algorithms to evaluate weather impacts on policies and investments. Our model achieves 88.3% accuracy in Zhejiang and 79.6% in Ireland, identifying a critical threshold (43% weather increase) for insurance viability. Additionally, we develop the TOA-Preser…

From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications
Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
https://arxiv.org/abs/2506.03464

From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications
The convergence of online learning algorithms in games under self-play is a fundamental question in game theory and machine learning. Among various notions of convergence, last-iterate convergence is particularly desirable, as it reflects the actual decisions made by the learners and captures the day-to-day behavior of the learning dynamics. While many algorithms are known to converge in the average-iterate, achieving last-iterate convergence typically requires considerably more effort in both …

Multiple machine-learning as a powerful tool for the star clusters analysis
Denilso Camargo
https://arxiv.org/abs/2506.13951 https://…

Multiple machine-learning as a powerful tool for the star clusters analysis
This work proposes a multiple machine learning method (MMLM) aiming to improve the accuracy and robustness in the analysis of star clusters. The MMLM performance is evaluated by applying it to the reanalysis of the old binary cluster candidate - NGC 1605a and NGC 1605b - found by Camargo (2021) (hereafter C21). The binary cluster candidate is analyzed by employing a set of well established machine learning algorithms applied to the Gaia-EDR3 data. Membership probabilities and open clusters (OCs…

Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback
Yanjie Dong, Haijun Zhang, Gaojie Chen, Xiaoyi Fan, Victor C. M. Leung, Xiping Hu
https://arxiv.org/abs/2506.04113

Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback
Recent advancements have introduced federated machine learning-based channel state information (CSI) compression before the user equipments (UEs) upload the downlink CSI to the base transceiver station (BTS). However, most existing algorithms impose a high communication overhead due to frequent parameter exchanges between UEs and BTS. In this work, we propose a model splitting approach with a shared model at the BTS and multiple local models at the UEs to reduce communication overhead. Moreover…

Just published:
Supplementing CEFR-graded vocabulary lists for language learners by leveraging information on dictionary views, corpus frequency, part-of-speech, and polysemy
A machine-learning method to suggest word candidates for CEFR-graded vocabulary lists.
#CEFR level of previously unlabeled words
#linguistics #CEFR #frequency #dictionary #LanguageLearning

Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
Soumen Garai, Suman Samui
https://arxiv.org/abs/2506.11169

Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
Small-Footprint Keyword Spotting (SF-KWS) has gained popularity in today's landscape of smart voice-activated devices, smartphones, and Internet of Things (IoT) applications. This surge is attributed to the advancements in Deep Learning, enabling the identification of predefined words or keywords from a continuous stream of words. To implement the SF-KWS model on edge devices with low power and limited memory in real-world scenarios, a efficient Tiny Machine Learning (TinyML) framework is essen…

This https://arxiv.org/abs/2407.13213 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model
This paper explores the application of Machine Learning techniques for pricing high-dimensional options within the framework of the Uncertain Volatility Model (UVM). The UVM is a robust framework that accounts for the inherent unpredictability of market volatility by setting upper and lower bounds on volatility and the correlation among underlying assets. By integrating advanced Machine Learning algorithms, we aim to enhance the accuracy and efficiency of option pricing under the UVM, especiall…

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Linda Lu, Ayush Sekhari, Karthik Sridharan
https://arxiv.org/abs/2506.06073 https://

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Machine unlearning addresses the problem of updating a machine learning model/system trained on a dataset $S$ so that the influence of a set of deletion requests $U \subseteq S$ on the unlearned model is minimized. The gold standard definition of unlearning demands that the updated model, after deletion, be nearly identical to the model obtained by retraining. This definition is designed for a worst-case attacker (one who can recover not only the unlearned model but also the remaining data samp…

Machine learning approaches for automatic cleaning of investigative drilling data
Fei Huang (Flinders University), Hongyu Qin (Flinders University), Masoud Manafi (Civil Group), Ben Juett (Civil Group), Ben Evans (Civil Group)
https://arxiv.org/abs/2506.14289

Machine learning approaches for automatic cleaning of investigative drilling data
Investigative drilling (ID) is an innovative measurement while drilling (MWD) technique that has been implemented in various site investigation projects across Australia. While the automated drilling feature of ID substantially reduces noise within drilling data streams, data cleaning remains essential for removing anomalies to enable accurate strata classification and prediction of soil and rock properties. This study employed three machine learning algorithms--IsoForest, one-class SVM, and DB…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
Keru Wu, Yuansi Chen, Wooseok Ha, Bin Yu

Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired
Suman Raj, Swapnil Padhi, Ruchi Bhoot, Prince Modi, Yogesh Simmhan
https://arxiv.org/abs/2506.14857

Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired
Autonomous navigation by drones using onboard sensors combined with machine learning and computer vision algorithms is impacting a number of domains, including agriculture, logistics, and disaster management. In this paper, we examine the use of drones for assisting visually impaired people (VIPs) in navigating through outdoor urban environments. Specifically, we present a perception-based path planning system for local planning around the neighborhood of the VIP, integrated with a global plann…

Searching for actual causes: Approximate algorithms with adjustable precision
Samuel Reyd, Ada Diaconescu, Jean-Louis Dessalles
https://arxiv.org/abs/2507.07857

Searching for actual causes: Approximate algorithms with adjustable precision
Causality has gained popularity in recent years. It has helped improve the performance, reliability, and interpretability of machine learning models. However, recent literature on explainable artificial intelligence (XAI) has faced criticism. The classical XAI and causality literature focuses on understanding which factors contribute to which consequences. While such knowledge is valuable for researchers and engineers, it is not what non-expert users expect as explanations. Instead, these users…

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

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

Runtime Analysis of Evolutionary NAS for Multiclass Classification
Zeqiong Lv, Chao Qian, Yun Liu, Jiahao Fan, Yanan Sun
https://arxiv.org/abs/2506.06019 h…

Runtime Analysis of Evolutionary NAS for Multiclass Classification
Evolutionary neural architecture search (ENAS) is a key part of evolutionary machine learning, which commonly utilizes evolutionary algorithms (EAs) to automatically design high-performing deep neural architectures. During past years, various ENAS methods have been proposed with exceptional performance. However, the theory research of ENAS is still in the infant. In this work, we step for the runtime analysis, which is an essential theory aspect of EAs, of ENAS upon multiclass classification pr…

Nickel- and iron-rich clumps in planetary nebulae: New discoveries and emission-line diagnostics
K. Bouvis, S. Akras, H. Monteiro, L. Konstantinou, P. Boumis, J. Garc\'ia-Rojas, D. R. Gon\c{c}alves, I. Aleman, A. Monreal-Ibero, J. Cami
https://arxiv.org/abs/2507.05357

Nickel- and iron-rich clumps in planetary nebulae: New discoveries and emission-line diagnostics
Integral field spectroscopy (IFS) offers a distinct advantage for studying extended sources by enabling spatially resolved emission maps for several emission lines without the need for specific filters. This study conducts a detailed analysis of iron and nickel emission lines in 12 planetary nebulae (PNe) using integral field unit (IFU) data from MUSE to provide insights into their formation and evolution mechanisms. New diagnostic line ratios, combined with machine-learning algorithms, were us…

Replaced article(s) found for gr-qc. https://arxiv.org/list/gr-qc/new
[2/2]:
- Predicting wide binaries and deviations from standard gravity using machine learning algorithms
Amoy Ashesh, Harsimran Kaur, Sandeep Aashish

This https://arxiv.org/abs/2111.06931 has been replaced.
link: https://scholar.google.com/scholar?q=a

Kaczmarz and Gauss-Seidel Algorithms with Volume Sampling
The method of Alternating Projections (AP) is a fundamental iterative technique with applications to problems in machine learning, optimization and signal processing. Examples include the Gauss-Seidel algorithm which is used to solve large-scale regression problems and the Kaczmarz and projections onto convex sets (POCS) algorithms that are fundamental to iterative reconstruction. Progress has been made with regards to the questions of efficiency and rate of convergence in the randomized settin…

An Observation on Lloyd's k-Means Algorithm in High Dimensions
David Silva-S\'anchez, Roy R. Lederman
https://arxiv.org/abs/2506.14952 https://

An Observation on Lloyd's k-Means Algorithm in High Dimensions
Clustering and estimating cluster means are core problems in statistics and machine learning, with k-means and Expectation Maximization (EM) being two widely used algorithms. In this work, we provide a theoretical explanation for the failure of k-means in high-dimensional settings with high noise and limited sample sizes, using a simple Gaussian Mixture Model (GMM). We identify regimes where, with high probability, almost every partition of the data becomes a fixed point of the k-means algorith…

A Dynamical Systems Perspective on the Analysis of Neural Networks
Dennis Chemnitz, Maximilian Engel, Christian Kuehn, Sara-Viola Kuntz
https://arxiv.org/abs/2507.05164

A Dynamical Systems Perspective on the Analysis of Neural Networks
In this chapter, we utilize dynamical systems to analyze several aspects of machine learning algorithms. As an expository contribution we demonstrate how to re-formulate a wide variety of challenges from deep neural networks, (stochastic) gradient descent, and related topics into dynamical statements. We also tackle three concrete challenges. First, we consider the process of information propagation through a neural network, i.e., we study the input-output map for different architectures. We ex…

This https://arxiv.org/abs/2209.09443 has been replaced.
link: https://scholar.google.com/scholar?q=a

Cryogenic in-memory computing using magnetic topological insulators
Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of the chiral edge state and the topological surface state. The memristive…

Multipass Linear Sketches for Geometric LP-Type Problems
N. Efe \c{C}ekirge, William Gay, David P. Woodruff
https://arxiv.org/abs/2507.11484 https://

Multipass Linear Sketches for Geometric LP-Type Problems
LP-type problems such as the Minimum Enclosing Ball (MEB), Linear Support Vector Machine (SVM), Linear Programming (LP), and Semidefinite Programming (SDP) are fundamental combinatorial optimization problems, with many important applications in machine learning applications such as classification, bioinformatics, and noisy learning. We study LP-type problems in several streaming and distributed big data models, giving $\varepsilon$-approximation linear sketching algorithms with a focus on the h…

Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
Tobias Jenegger, Nicole Hartman, Roman Gernhaeuser, Lukas Heinrich, Laura Fabbietti
https://arxiv.org/abs/2506.09088

Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and gamma rays with an energy resolution in the per cent range after Doppler correction. Precise cluster reconstruction from sparse hit patterns is a crucial requirement. Standard algorithms typically use fixed cluster sizes or geometric thresholds. To enhance perfor…

Quantum Algorithm Software for Condensed Matter Physics
T. Farajollahpour
https://arxiv.org/abs/2506.09308 https://arxiv.org/pdf/2506…

Quantum Algorithm Software for Condensed Matter Physics
This report offers a comprehensive analysis of the evolving landscape of quantum algorithm software specifically tailored for condensed matter physics. It examines fundamental quantum algorithms such as Variational Quantum Eigensolver (VQE), Quantum Phase Estimation (QPE), Quantum Annealing (QA), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Machine Learning (QML) as applied to key condensed matter problems including strongly correlated systems, topological phases, and quantum …

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
James Alcala, Yat Tin Chow, Mahesh Sunkula
https://arxiv.org/abs/2506.07290

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
Since their introduction, anchoring methods in extragradient-type saddlepoint problems have inspired a flurry of research due to their ability to provide order-optimal rates of accelerated convergence in very general problem settings. Such guarantees are especially important as researchers consider problems in artificial intelligence (AI) and machine learning (ML), where large problem sizes demand immense computational power. Much of the more recent works explore theoretical aspects of this new…

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Xinyuan Liu, Jiahui Chen, Bocheng Hu, Yu Sun, Xinyang Chen, Shaoxu Song
https://arxiv.org/abs/2507.10934

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Data quality remains an important challenge in data-driven systems, as errors in tabular data can severely compromise downstream analytics and machine learning performance. Although numerous error detection algorithms have been proposed, the lack of diverse, real-world error datasets limits comprehensive evaluation. Manual error annotation is both time-consuming and inconsistent, motivating the exploration of synthetic error generation as an alternative. In this work, we introduce TableEG, a fr…

Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
Ema Puljak, Maurizio Pierini, Artur Garcia-Saez
https://arxiv.org/abs/2506.00102

Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this work, we propose a tensor network-based strategy for anomaly detection at the LHC and demonstrate its superior performance in identifying new phenomena compared to estab…

This https://arxiv.org/abs/2506.00168 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

A novel sensitivity analysis method for agent-based models stratifies \emph{in-silico} tumor spheroid simulations
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the \textbf{S}imulate, \textbf{S}ummarize, \textbf{R}educe, \textbf{C}luster, and \textbf{A}nalyze (SSRCA) methodology, a machine-learning based …

Prospective Learning in Retrospect
Yuxin Bai, Cecelia Shuai, Ashwin De Silva, Siyu Yu, Pratik Chaudhari, Joshua T. Vogelstein
https://arxiv.org/abs/2507.07965 https://arxiv.org/pdf/2507.07965 https://arxiv.org/html/2507.07965
arXiv:2507.07965v1 Announce Type: new
Abstract: In most real-world applications of artificial intelligence, the distributions of the data and the goals of the learners tend to change over time. The Probably Approximately Correct (PAC) learning framework, which underpins most machine learning algorithms, fails to account for dynamic data distributions and evolving objectives, often resulting in suboptimal performance. Prospective learning is a recently introduced mathematical framework that overcomes some of these limitations. We build on this framework to present preliminary results that improve the algorithm and numerical results, and extend prospective learning to sequential decision-making scenarios, specifically foraging. Code is available at: https://github.com/neurodata/prolearn2.
toXiv_bot_toot

Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
Joona Pankkonen, Lauri Ylinen, Matti Raasakka, Andrea Marchesin, Ilkka Tittonen
https://arxiv.org/abs/2507.07742

Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
Parameterized quantum circuits (PQCs) are pivotal components of variational quantum algorithms (VQAs), which represent a promising pathway to quantum advantage in noisy intermediate-scale quantum (NISQ) devices. PQCs enable flexible encoding of quantum information through tunable quantum gates and have been successfully applied across domains such as quantum chemistry, combinatorial optimization, and quantum machine learning. Despite their potential, PQC performance on NISQ hardware is hindered…

Mirror Descent Using the Tempesta Generalized Multi-parametric Logarithms
Andrzej Cichocki
https://arxiv.org/abs/2506.13984 https://a…

Mirror Descent Using the Tempesta Generalized Multi-parametric Logarithms
In this paper, we develop a wide class Mirror Descent (MD) algorithms, which play a key role in machine learning. For this purpose we formulated the constrained optimization problem, in which we exploits the Bregman divergence with the Tempesta multi-parametric deformation logarithm as a link function. This link function called also mirror function defines the mapping between the primal and dual spaces and is associated with a very-wide (in fact, theoretically infinite) class of generalized tra…

Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Congye Wang, Matthew A. Fisher, Heishiro Kanagawa, Wilson Chen, Chris. J. Oates
https://arxiv.org/abs/2507.00671

Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Sampling algorithms drive probabilistic machine learning, and recent years have seen an explosion in the diversity of tools for this task. However, the increasing sophistication of sampling algorithms is correlated with an increase in the tuning burden. There is now a greater need than ever to treat the tuning of samplers as a learning task in its own right. In a conceptual breakthrough, Wang et al (2025) formulated Metropolis-Hastings as a Markov decision process, opening up the possibility fo…

PhotIQA: A photoacoustic image data set with image quality ratings
Anna Breger, Janek Gr\"ohl, Clemens Karner, Thomas R Else, Ian Selby, Jonathan Weir-McCall, Carola-Bibiane Sch\"onlieb
https://arxiv.org/abs/2507.03478

PhotIQA: A photoacoustic image data set with image quality ratings
Image quality assessment (IQA) is crucial in the evaluation stage of novel algorithms operating on images, including traditional and machine learning based methods. Due to the lack of available quality-rated medical images, most commonly used IQA methods employing reference images (i.e. full-reference IQA) have been developed and tested for natural images. Reported application inconsistencies arising when employing such measures for medical images are not surprising, as they rely on different p…

QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
Nicolas Renaud
https://arxiv.org/abs/2506.09743 https://

QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
In this paper, we present results obtained using QMCTorch, a modular framework for real-space Quantum Monte Carlo (QMC) simulations of small molecular systems. Built on the popular deep learning library PyTorch, QMCTorch is GPU-native and enables the integration of machine learning-inspired components into the wave function ansatz, such as neural network backflow transformations and Jastrow factors, while leveraging efficient optimization algorithms. QMCTorch interfaces with two widely used qua…

Exact and efficient basis pursuit denoising via differential inclusions and a selection principle
Gabriel P. Langlois, J\'er\^ome Darbon
https://arxiv.org/abs/2507.05562

Exact and efficient basis pursuit denoising via differential inclusions and a selection principle
Basis pursuit denoising (BPDN) is a cornerstone of compressive sensing, statistics and machine learning. While various algorithms for BPDN have been proposed, they invariably suffer from drawbacks and must either favor efficiency at the expense of accuracy or vice versa. As such, state-of-the-art algorithms remain ineffective for high-dimensional applications that require accurate solutions within a reasonable amount of computational time. In this work, we address this issue and propose an exac…

This https://arxiv.org/abs/2505.06146 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Algorithms for Boolean Satisfiability
Learning-augmented algorithms are a prominent recent development in beyond worst-case analysis. In this framework, a problem instance is provided with a prediction (``advice'') from a machine-learning oracle, which provides partial information about an optimal solution, and the goal is to design algorithms that leverage this advice to improve worst-case performance. We study the classic Boolean satisfiability (SAT) decision and optimization problems within this framework using two forms of advi…

This https://arxiv.org/abs/2506.00377 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT
The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources,…

Genetic Features for Drug Responses in Cancer -- Investigating an Ensemble-Feature-Selection Approach
Johannes Schl\"uter, Alexander Sch\"onhuth
https://arxiv.org/abs/2507.02818

Genetic Features for Drug Responses in Cancer -- Investigating an Ensemble-Feature-Selection Approach
Predicting drug responses using genetic and transcriptomic features is crucial for enhancing personalized medicine. In this study, we implemented an ensemble of machine learning algorithms to analyze the correlation between genetic and transcriptomic features of cancer cell lines and IC50 values, a reliable metric for drug efficacy. Our analysis involved a reduction of the feature set from an original pool of 38,977 features, demonstrating a strong linear relationship between genetic features a…

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
https://arxiv.org/abs/2506.00374

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generativ…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement ...
Tom Maus, Asma Atamna, Tobias Glasmachers

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
Bosubabu Sambana, Kotamsetty Geethika Devi, Bandi Rajeswara Reddy, Galeti Mohammad Hussain, Gownivalla Siddartha
https://arxiv.org/abs/2506.23504

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and pr…

Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls
Penelope Madysa, Sabrina Appel, Verena Kain, Michael Schenk
https://arxiv.org/abs/2506.03796

Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls
Geoff is a collection of Python packages that form a framework for automation of particle accelerator controls. With particle accelerator laboratories around the world researching machine learning techniques to improve accelerator performance and uptime, a multitude of approaches and algorithms have emerged. The purpose of Geoff is to harmonize these approaches and to minimize friction when comparing or migrating between them. It provides standardized interfaces for optimization problems, utili…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Non-negative matrix factorization algorithms generally improve topic model fits
Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Maja Franz, Melvin Strobl, Leonid Chaichenets, Eileen Kuehn, Achim Streit, Wolfgang Mauerer
https://arxiv.org/abs/2506.09527

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
The field of variational quantum algorithms, in particular quantum machine learning (QML), produced numerous theoretical and empirical insights in recent years. As variational quantum circuits (VQCs) can be represented by Fourier series that contain an exponentially large spectrum in the number of input features, hope for quantum advantage remains. Nevertheless, properties of quantum Fourier models (QFMs) are not yet fully understood, in particular how they could potentially outperform classica…

A novel sensitivity analysis method for agent-based models stratifies \emph{in-silico} tumor spheroid simulations
Edward H. Rohr, John T. Nardini
https://arxiv.org/abs/2506.00168 …

A novel sensitivity analysis method for agent-based models stratifies \emph{in-silico} tumor spheroid simulations
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the \textbf{S}imulate, \textbf{S}ummarize, \textbf{R}educe, \textbf{C}luster, and \textbf{A}nalyze (SSRCA) methodology, a machine-learning based …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- optimizn: a Python Library for Developing Customized Optimization Algorithms
Akshay Sathiya, Rohit Pandey

Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition
Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrovi\'c, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu
https://arxiv.org/abs/2507.01873

Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition
We study differentially private algorithms for graph cut sparsification, a fundamental problem in algorithms, privacy, and machine learning. While significant progress has been made, the best-known private and efficient cut sparsifiers on $n$-node graphs approximate each cut within $\widetilde{O}(n^{1.5})$ additive error and $1+γ$ multiplicative error for any $γ> 0$ [Gupta, Roth, Ullman TCC'12]. In contrast, "inefficient" algorithms, i.e., those requiring exponential time, can achieve an $\wi…

Measurement-based Evaluation of CNN-based Detection and Estimation for ISAC Systems
Steffen Schieler, Sebastian Semper, Christian Schneider, Reiner Thom\"a
https://arxiv.org/abs/2507.01799

Measurement-based Evaluation of CNN-based Detection and Estimation for ISAC Systems
In wireless sensing applications, such as ISAC, one of the first crucial signal processing steps is the detection and estimation targets from a channel estimate. Effective algorithms in this context must be robust across a broad SNR range, capable of handling an unknown number of targets, and computationally efficient for real-time implementation. During the last decade, different Machine Learning methods have emerged as promising solutions, either as standalone models or as complementing exist…

This https://arxiv.org/abs/2312.01602 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Time Series Similarity Measures and Quantum Temporal Kernels
This article presents a quantum computing approach to designing of similarity measures and kernels for classification of stochastic symbolic time series. In the area of machine learning, kernels are important components of various similarity-based classification, clustering, and regression algorithms. An effective strategy for devising problem-specific kernels is leveraging existing generative models of the example space. In this study we assume that a quantum generative model, known as quantum…

Online Electron Reconstruction at CLAS12
Richard Tyson, Gagik Gavalian
https://arxiv.org/abs/2507.05274 https://arxiv.org/pdf/2507.05…

Online Electron Reconstruction at CLAS12
Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle's type. Of particular interest to electroproduction Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning a…

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Gabriele Digregorio, Salvatore Maccarrone, Mario D'Onghia, Luigi Gallo, Michele Carminati, Mario Polino, Stefano Zanero
https://arxiv.org/abs/2506.02660

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an effective solution in both the feature and problem spaces. This issue arises from not addressing the intrinsic nondeterministic nature of malware, namely executing the same sample multiple times may yield significantly different behaviors. Hence, the perturbations computed for a specific behavior may be ineffective for…

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

Memory Access Characterization of Large Language Models in CPU Environment and its Potential Impacts
Spencer Banasik
https://arxiv.org/abs/2506.01827 https…

Memory Access Characterization of Large Language Models in CPU Environment and its Potential Impacts
As machine learning algorithms are shown to be an increasingly valuable tool, the demand for their access has grown accordingly. Oftentimes, it is infeasible to run inference with larger models without an accelerator, which may be unavailable in environments that have constraints such as energy consumption, security, or cost. To increase the availability of these models, we aim to im- prove the LLM inference speed on a CPU-only environment by modifying the cache architecture. To determine what …

This https://arxiv.org/abs/2501.16815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination
This paper introduces two novel criteria: one for feature selection and another for feature elimination in the context of best subset selection, which is a benchmark problem in statistics and machine learning. From the perspective of optimization, we revisit the classical selection and elimination criteria in traditional best subset selection algorithms, revealing that these classical criteria capture only partial variations of the objective function after the entry or exit of features. By form…

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

This https://arxiv.org/abs/2406.12007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
Here we present the results of benchmarking a quantum processor based on trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning algorithms. Using a quantum-enhanced support vector machine algorithm with up to five qubits we perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology. For the first dataset, images are intentionally selected so that they could be classified with 100% accuracy. This all…

This https://arxiv.org/abs/2505.05532 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Design and FPGA Implementation of WOMBAT: A Deep Neural Network Level-1 Trigger System for Jet Substructure Identification and Boosted $H\rightarrow b\bar{b}$ Tagging at the CMS Experiment
This thesis investigates the physics performance, trigger efficiency, and Field Programmable Gate Array (FPGA) implementation of machine learning (ML)-based algorithms for Lorentz-boosted $H\rightarrow b\bar{b}$ tagging within the CMS Level-1 Trigger (L1T) under Phase-1 conditions. The proposed algorithm, WOMBAT (Wide Object ML Boosted Algorithm Trigger), comprises a high-performance Master Model (W-MM) and a quantized, FPGA-synthesizable Apprentice Model (W-AM), benchmarked against the standar…

Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Florian F\"urrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Mu\~noz-Gil
https://arxiv.org/abs/2506.01666

Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alternative, though they are currently restricted to discrete ga…

Trainability of Parametrised Linear Combinations of Unitaries
Nikhil Khatri, Stefan Zohren, Gabriel Matos
https://arxiv.org/abs/2506.22310 https://

Trainability of Parametrised Linear Combinations of Unitaries
A principal concern in the optimisation of parametrised quantum circuits is the presence of barren plateaus, which present fundamental challenges to the scalability of applications, such as variational algorithms and quantum machine learning models. Recent proposals for these methods have increasingly used the linear combination of unitaries (LCU) procedure as a core component. In this work, we prove that an LCU of trainable parametrised circuits is still trainable. We do so by analytically der…