Tootfinder

Gravitational lensing rarely produces high-mass outliers to the compact binary population
Amanda Farah, Jose Mar\'ia Ezquiaga, Maya Fishbach, Daniel Holz
https://arxiv.org/abs/2507.07964

Gravitational lensing rarely produces high-mass outliers to the compact binary population
All gravitational-wave signals are inevitably gravitationally lensed by intervening matter as they propagate through the Universe. When a gravitational-wave signal is magnified, it appears to have originated from a closer, more massive system. Thus, high-mass outliers to the gravitational-wave source population are often proposed as natural candidates for strongly lensed events. However, when using a data-driven method for identifying population outliers, we find that high-mass outliers are not…

ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
Bingxin Xu, Zhen Dong, Oussama Elachqar, Yuzhang Shang
https://arxiv.org/abs/2509.09679

ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
Large language models require massive memory footprints, severely limiting deployment on consumer hardware. Quantization reduces memory through lower numerical precision, but extreme 2-bit quantization suffers from catastrophic performance loss due to outliers in activations. Rotation-based methods such as QuIP and QuaRot apply orthogonal transforms to eliminate outliers before quantization, using computational invariance: $\mathbf{y} = \mathbf{Wx} = (\mathbf{WQ}^T)(\mathbf{Qx})$ for orthogonal…

Loss Functions for Detecting Outliers in Panel Data
Charles D. Coleman, Thomas Bryan
https://arxiv.org/abs/2509.07014 https://arxiv.org/pdf/2509.07014

Loss Functions for Detecting Outliers in Panel Data
The detection of outliers is of critical importance in the assurance of data quality. Outliers may exist in observed data or in data derived from these observed data, such as estimates and forecasts. An outlier may indicate a problem with its data generation process or may simply be a true statement about the world. Without making any distributional assumptions, this paper proposes the use of loss functions to detect these outliers in panel data. An unsigned loss function is derived axiomatical…

Taking the Weight Off: Mitigating Parameter Bias from Catastrophic Outliers in 3$\times$2pt Analysis
Carolyn McDonald Mill, C. Danielle Leonard, Markus Michael Rau, Cora Uhlemann, Shahab Joudaki
https://arxiv.org/abs/2509.08052

Taking the Weight Off: Mitigating Parameter Bias from Catastrophic Outliers in 3$\times$2pt Analysis
Stage IV cosmological surveys will map the universe with unprecedented precision, reducing statistical uncertainties to levels where unmodelled systematics can significantly bias inference. In particular, photometric redshift (photo-z) errors and intrinsic alignments (IA) must be robustly accounted for to ensure accurate inference of cosmological parameters. The increasing depth of Stage IV surveys exacerbates these challenges by producing low signal-to-noise galaxy populations prone to inaccur…

Conceptual winsorizing: An application to the social cost of carbon
Richard S. J. Tol
https://arxiv.org/abs/2508.07384 https://arxiv.org/pdf/2508.07384

Conceptual winsorizing: An application to the social cost of carbon
There are many published estimates of the social cost of carbon. Some are clear outliers, the result of poorly constrained models. Percentile winsorizing is an option, but I here propose conceptual winsorizing: The social cost of carbon is either a willingness to pay, which cannot exceed the ability to pay, or a proposed carbon tax, which cannot raise more revenue than all other taxes combined. Conceptual winsorizing successfully removes high outliers. It slackens as economies decarbonize, slow…

Convergence rates for regularized unbalanced optimal transport: the discrete case
Luca Nenna, Paul Pegon, Louis Tocquec
https://arxiv.org/abs/2507.07917 ht…

Convergence rates for regularized unbalanced optimal transport: the discrete case
Unbalanced optimal transport (UOT) is a natural extension of optimal transport (OT) allowing comparison between measures of different masses. It arises naturally in machine learning by offering a robustness against outliers. The aim of this work is to provide convergence rates of the regularized transport cost and plans towards their original solution when both measures are weighted sums of Dirac masses.

Robustness and accuracy of mean opinion scores with hard and soft outlier detection
Dietmar Saupe, Tim Bleile
https://arxiv.org/abs/2509.06554 https://arxi…

Robustness and accuracy of mean opinion scores with hard and soft outlier detection
In subjective assessment of image and video quality, observers rate or compare selected stimuli. Before calculating the mean opinion scores (MOS) for these stimuli from the ratings, it is recommended to identify and deal with outliers that may have given unreliable ratings. Several methods are available for this purpose, some of which have been standardized. These methods are typically based on statistics and sometimes tested by introducing synthetic ratings from artificial outliers, such as ra…

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
Tuo Zhang, Ning Li, Xin Yuan, Wenchao Xu, Quan Chen, Song Guo, Haijun Zhang
https://arxiv.org/abs/2508.07329

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
With the breakthrough progress of large language models (LLMs) in natural language processing and multimodal tasks, efficiently deploying them on resource-constrained edge devices has become a critical challenge. The Mixture of Experts (MoE) architecture enhances model capacity through sparse activation, but faces two major difficulties in practical deployment: (1) The presence of numerous outliers in activation distributions leads to severe degradation in quantization accuracy for both activat…

funOCLUST: Clustering Functional Data with Outliers
Katharine M. Clark, Paul D. McNicholas
https://arxiv.org/abs/2508.00110 https://arxiv.org/pdf/2508.0011…

funOCLUST: Clustering Functional Data with Outliers
Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the functional setting is proposed to address these issues. The approach leverages the OCLUST framework, creating a robust method to cluster curves and trim outliers. The methodology is evaluated on both simulated and real-world functional datasets, demonstrating strong performance in clustering and outlier identificatio…

Facility Location and $k$-Median with Fair Outliers
Rajni Dabas, Samir Khuller, Emilie Rivkin
https://arxiv.org/abs/2508.02572 https://arxiv.org/pdf/2508.0…

Facility Location and $k$-Median with Fair Outliers
Classical clustering problems such as \emph{Facility Location} and \emph{$k$-Median} aim to efficiently serve a set of clients from a subset of facilities -- minimizing the total cost of facility openings and client assignments in Facility Location, and minimizing assignment (service) cost under a facility count constraint in $k$-Median. These problems are highly sensitive to outliers, and therefore researchers have studied variants that allow excluding a small number of clients as outliers to …

A test statistic, $h^*$, for outlier analysis
Johan F. Hoorn, Johnny K. W. Ho
https://arxiv.org/abs/2508.06792 https://arxiv.org/pdf/2508.06792

A test statistic, $h^*$, for outlier analysis
Outlier analysis is a critical tool across diverse domains, from clinical decision-making to cybersecurity and talent identification. Traditional statistical outlier detection methods, such as Grubb's test and Dixon's Q, are predicated on the assumption of normality and often fail to reckon the meaningfulness of exceptional values within non-normal datasets. In this paper, we introduce the h* statistic, a novel parametric, frequentist approach for evaluating global outliers without the normalit…

A new coefficient to measure agreement between continuous variables
Ronny Vallejos, Felipe Osorio, Clemente Ferrer
https://arxiv.org/abs/2507.07913 https:/…

A new coefficient to measure agreement between continuous variables
Assessing agreement between two instruments is crucial in clinical studies to evaluate the similarity between two methods measuring the same subjects. This paper introduces a novel coefficient, termed rho1, to measure agreement between continuous variables, focusing on scenarios where two instruments measure experimental units in a study. Unlike existing coefficients, rho1 is based on L1 distances, making it robust to outliers and not relying on nuisance parameters. The coefficient is derived f…

Exploration of groups and outliers in Gaia RVS stellar spectra with metric learning
Yarden Eilat Bloch, Dovi Poznanski, Nick L. J. Cox, Emmanuel Bernhard, Iain McDonald, Manuela Rauch, Albert Zijlstra
https://arxiv.org/abs/2508.00071

Exploration of groups and outliers in Gaia RVS stellar spectra with metric learning
The Gaia mission is transforming our view of the Milky Way by providing distances towards a billion stars, and much more. The third data release includes nearly a million spectra from its Radial Velocity Spectrometer (RVS). Identifying unexpected features in such vast datasets presents a significant challenge. It is impossible to visually inspect all of the spectra and difficult to analyze them in a comprehensive way. In order to supplement traditional analysis approaches, and in order to facil…

Singular values of sparse random rectangular matrices: Emergence of outliers at criticality
Ioana Dumitriu, Hai-Xiao Wang, Zhichao Wang, Yizhe Zhu
https://arxiv.org/abs/2508.01456

Singular values of sparse random rectangular matrices: Emergence of outliers at criticality
Consider the random bipartite Erdős-Rényi graph $\mathbb{G}(n, m, p)$, where each edge with one vertex in $V_{1}=[n]$ and the other vertex in $V_{2} =[m]$ is connected with probability $p$, and $n=\lfloor γm\rfloor$ for a constant aspect ratio $γ\geq 1$. It is well known that the empirical spectral measure of its centered and normalized adjacency matrix converges to the Marčenko-Pastur (MP) distribution. However, largest and smallest singular values may not converge to the right and left e…

The use of financial and sustainability ratios to map a sector. An approach using compositional data
Elena Rond\'os-Casas (University of Girona), Germ\`a Coenders (University of Girona), Miquel Carreras-Sim\'o (University of Girona), N\'uria Arimany-Serrat (University of Vic-Central University of Catalonia)
https://arxiv.org/ab…

The use of financial and sustainability ratios to map a sector. An approach using compositional data
Purpose: The article aims to visualise in a single graph fish and meat processing company groups in Spain with respect to long-term solvency, energy, waste and water intensity and gender employment gap. Design/methodology/approach: The selected financial, environmental and social indicators are ratios, which require specific statistical analysis methods to prevent severe skewness and outliers. We use the compositional data methodology and the principal-component analysis biplot. Findings: F…

A Practical and Secure Byzantine Robust Aggregator
De Zhang Lee, Aashish Kolluri, Prateek Saxena, Ee-Chien Chang
https://arxiv.org/abs/2506.23183 https://

A Practical and Secure Byzantine Robust Aggregator
In machine learning security, one is often faced with the problem of removing outliers from a given set of high-dimensional vectors when computing their average. For example, many variants of data poisoning attacks produce gradient vectors during training that are outliers in the distribution of clean gradients, which bias the computed average used to derive the ML model. Filtering them out before averaging serves as a generic defense strategy. Byzantine robust aggregation is an algorithmic pri…

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
Jad Yehya, Mansour Benbakoura, C\'edric Allain, Beno\^it Malezieux, Matthieu Kowalski, Thomas Moreau
https://arxiv.org/abs/2509.07523

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
Identifying recurring patterns and rare events in large-scale signals is a fundamental challenge in fields such as astronomy, physical simulations, and biomedical science. Convolutional Dictionary Learning (CDL) offers a powerful framework for modeling local structures in signals, but its use for detecting rare or anomalous events remains largely unexplored. In particular, CDL faces two key challenges in this setting: high computational cost and sensitivity to artifacts and outliers. In this pa…

Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Ryan DeWolfe
https://arxiv.org/abs/2509.02334 https://arxiv.org/p…

Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a partition). For vector data, Hierarchical Density Based Spatial Clustering for Applications with Noise (HDBSCAN) has emerged as a leading clustering algorithm that allows for outlier points that do not belong to any cluster. The first step in HDBSCAN is to redefine the distance between vectors in such a way that single-…

Area-based epigraph and hypograph indices for functional outlier detection
Belen Pulido, Alba M. Franco-Pereira, Rosa E. Lillo, Fabian Scheipl
https://arxiv.org/abs/2507.05701

Area-based epigraph and hypograph indices for functional outlier detection
Detecting outliers in Functional Data Analysis is challenging because curves can stray from the majority in many different ways. The Modified Epigraph Index (MEI) and Modified Hypograph Index (MHI) rank functions by the fraction of the domain on which one curve lies above or below another. While effective for spotting shape anomalies, their construction limits their ability to flag magnitude outliers. This paper introduces two new metrics, the Area-Based Epigraph Index (ABEI) and Area-Based Hyp…

Crosslisted article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- Loss Functions for Detecting Outliers in Panel Data
Charles D. Coleman, Thomas Bryan

Dynamic State Estimation of Power System Utilizing Cauchy Kernel-Based Maximum Mixture Correntropy UKF over Beluga Whale-Bat Optimization
Duc Viet Nguyen, Haiquan Zhao, Jinhui Hu
https://arxiv.org/abs/2509.01163

Dynamic State Estimation of Power System Utilizing Cauchy Kernel-Based Maximum Mixture Correntropy UKF over Beluga Whale-Bat Optimization
Non-Gaussian noise, outliers, sudden load changes, and bad measurement data are key factors that diminish the accuracy of dynamic state estimation in power systems. Additionally, unscented Kalman filters (UKF) based on correntropy criteria utilize bandwidth-sensitive Gaussian kernels, which may lead to singular matrices in the Cholesky decomposition. To overcome all the above problems, in this paper, a robust UKF based on Cauchy kernel maximum mixture correntropy (CKMMC) criteria over hybrid Be…

Microstates : Do the outliers worth
Olivier Sire
https://arxiv.org/abs/2506.16080 https://arxiv.org/pdf/2506.16080

Microstates : Do the outliers worth
This note addresses the relevance of rare events in system dynamics, inspired by Jill North reflections on the origin of the arrow of time in thermodynamics. After identifying the existence of rare events, characterized by a Pareto distribution, within a simple gas particle simulation, we investigate their impact on entropy evolution. These rare events are associated with microstates that locally decrease entropy, in contrast to the overall entropy increase observed in the bulk of the system. W…

Robust fuzzy clustering with cellwise outliers
Giorgia Zaccaria, Lorenzo Benzakour, Luis A. Garc\'ia-Escudero, Francesca Greselin, Agust\'in Mayo-\'Iscar
https://arxiv.org/abs/2508.03310

Robust fuzzy clustering with cellwise outliers
Fuzzy clustering is a technique for identifying subgroups in heterogeneous populations by quantifying unit membership degrees. The magnitude of the latter depends on the desired level of fuzzification, based on the purpose of the analysis. We combine the advantages of fuzzy clustering with a robust approach able to detecting cellwise outliers, i.e., anomalous cells in a data matrix. The proposed methodology is formulated within a probabilistic framework and estimated via an Expectation-Maximiza…

Iteratively Saturated Kalman Filtering
Alan Yang, Stephen Boyd
https://arxiv.org/abs/2507.00272 https://arxiv.org/pdf/2507.00272

Iteratively Saturated Kalman Filtering
The Kalman filter (KF) provides optimal recursive state estimates for linear-Gaussian systems and underpins applications in control, signal processing, and others. However, it is vulnerable to outliers in the measurements and process noise. We introduce the iteratively saturated Kalman filter (ISKF), which is derived as a scaled gradient method for solving a convex robust estimation problem. It achieves outlier robustness while preserving the KF's low per-step cost and implementation simplicity…

A Fast Parallel Median Filtering Algorithm Using Hierarchical Tiling
Louis Sugy (NVIDIA)
https://arxiv.org/abs/2507.19926 https://arxiv.org/pdf/2507.19926

A Fast Parallel Median Filtering Algorithm Using Hierarchical Tiling
Median filtering is a non-linear smoothing technique widely used in digital image processing to remove noise while retaining sharp edges. It is particularly well suited to removing outliers (impulse noise) or granular artifacts (speckle noise). However, the high computational cost of median filtering can be prohibitive. Sorting-based algorithms excel with small kernels but scale poorly with increasing kernel diameter, in contrast to constant-time methods characterized by higher constant factors…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Xiang Lan, Tim Menzies, Bowen Xu
https://arxiv.org/abs/2506.20444 http…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Vulnerability detection is crucial for identifying security weaknesses in software systems. However, the effectiveness of machine learning models in this domain is often hindered by low-quality training datasets, which contain noisy, mislabeled, or imbalanced samples. This paper proposes a novel dataset maps-empowered approach that systematically identifies and mitigates hard-to-learn outliers, referred to as "bad seeds", to improve model training efficiency. Our approach can categorize trainin…

Robust Analysis for Resilient AI System
Yu Wang, Ran Jin, Lulu Kang
https://arxiv.org/abs/2509.06172 https://arxiv.org/pdf/2509.06172

Robust Analysis for Resilient AI System
Operational hazards in Manufacturing Industrial Internet (MII) systems generate severe data outliers that cripple traditional statistical analysis. This paper proposes a novel robust regression method, DPD-Lasso, which integrates Density Power Divergence with Lasso regularization to analyze contaminated data from AI resilience experiments. We develop an efficient iterative algorithm to overcome previous computational bottlenecks. Applied to an MII testbed for Aerosol Jet Printing, DPD-Lasso pro…

A Fast Iterative Robust Principal Component Analysis Method
Timbwaoga Aime Judicael Ouermi, Jixian Li, Chris R. Johnson
https://arxiv.org/abs/2506.16013 ht…

A Fast Iterative Robust Principal Component Analysis Method
Principal Component Analysis (PCA) is widely used for dimensionality reduction and data analysis. However, PCA results are adversely affected by outliers often observed in real-world data. Existing robust PCA methods are often computationally expensive or exhibit limited robustness. In this work, we introduce a Fast Iterative Robust (FIR) PCA method by efficiently estimating the inliers center location and covariance. Our approach leverages Incremental PCA (IPCA) to iteratively construct a subs…

The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models
Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mizanur Rahman, Mir Tafseer Nayeem, Enamul Hoque
https://arxiv.org/abs/2508.09716

The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models
Information visualizations are powerful tools that help users quickly identify patterns, trends, and outliers, facilitating informed decision-making. However, when visualizations incorporate deceptive design elements-such as truncated or inverted axes, unjustified 3D effects, or violations of best practices-they can mislead viewers and distort understanding, spreading misinformation. While some deceptive tactics are obvious, others subtly manipulate perception while maintaining a facade of legi…

Robust Alignment via Partial Gromov-Wasserstein Distances
Xiaoyun Gong, Sloan Nietert, Ziv Goldfeld
https://arxiv.org/abs/2506.21507 https://

Robust Alignment via Partial Gromov-Wasserstein Distances
The Gromov-Wasserstein (GW) problem provides a powerful framework for aligning heterogeneous datasets by matching their internal structures in a way that minimizes distortion. However, GW alignment is sensitive to data contamination by outliers, which can greatly distort the resulting matching scheme. To address this issue, we study robust GW alignment, where upon observing contaminated versions of the clean data distributions, our goal is to accurately estimate the GW alignment cost between th…

The Blind Spot of BGP Anomaly Detection: Why LSTM Autoencoders Fail on Real-World Outages
Samuel Oluwafemi Adebayo
https://arxiv.org/abs/2506.17821 https:/…

The Blind Spot of BGP Anomaly Detection: Why LSTM Autoencoders Fail on Real-World Outages
Deep learning has significant potential to make the Internet's Border Gateway Protocol (BGP) secure by detecting anomalous routing activity. However, all but a few of these approaches rely on the implicit assumption that anomalies manifest as noisy, high-complexity outliers from some normal baseline. This work challenges this assumption by investigating if a best-in-class detection model built on this assumption can effectively deal with real-world security events' diverse signatures. We employ…

A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
Ivan Letteri
https://arxiv.org/abs/2507.14960 https://

A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of…

A Self-scaled Approximate $\ell_0$ Regularization Robust Model for Outlier Detection
Pengyang Song, Jue Wang
https://arxiv.org/abs/2506.22277 https://

A Self-scaled Approximate $\ell_0$ Regularization Robust Model for Outlier Detection
Robust regression models in the presence of outliers have significant practical relevance in areas such as signal processing, financial econometrics, and energy management. Many existing robust regression methods, either grounded in statistical theory or sparse signal recovery, typically rely on the explicit or implicit assumption of outlier sparsity to filter anomalies and recover the underlying signal or data. However, these methods often suffer from limited robustness or high computational c…

Visualization-Driven Illumination for Density Plots
Xin Chen, Yunhai Wang, Huaiwei Bao, Kecheng Lu, Jaemin Jo, Chi-Wing Fu, Jean-Daniel Fekete
https://arxiv.org/abs/2507.17265

Visualization-Driven Illumination for Density Plots
We present a novel visualization-driven illumination model for density plots, a new technique to enhance density plots by effectively revealing the detailed structures in high- and medium-density regions and outliers in low-density regions, while avoiding artifacts in the density field's colors. When visualizing large and dense discrete point samples, scatterplots and dot density maps often suffer from overplotting, and density plots are commonly employed to provide aggregated views while revea…

Local perturbations of block Toeplitz matrices
Lars Koekenbier, Hermann Schulz-Baldes
https://arxiv.org/abs/2506.12757 https://arxiv.…

Local perturbations of block Toeplitz matrices
This work is about the asymptotic spectral theory of tridiagonal Toeplitz matrices with matrix entries, with periodicity broken on a finite number of entries. Varying the ranks of these perturbations allow to interpolate between open boundary and circulant Toeplitz matrices. While the continuous part of the limit spectrum only depends on these ranks and no other aspect of the perturbation, the outliers of the spectrum depend continuously on the local perturbation. The proof is essentially based…

SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games
Shuaiqi Sun, Congde Yuan, Haoqiang Yang, Mengzhuo Guo, Guiying Wei, Jiangbo Tian
https://arxiv.org/abs/2506.10487

SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games
In digital gaming, long-term user lifetime value (LTV) prediction is essential for monetization strategy, yet presents major challenges due to delayed payment behavior, sparse early user data, and the presence of high-value outliers. While existing models typically rely on either short-cycle observations or strong distributional assumptions, such approaches often underestimate long-term value or suffer from poor robustness. To address these issues, we propose SHort-cycle auxiliary with Order-pr…

FPT Constant Approximation Algorithms for Colorful Sum of Radii
Shuilian Liu, Gregory Gutin, Yicheng Xu, Yong Zhang
https://arxiv.org/abs/2506.13191 https:…

FPT Constant Approximation Algorithms for Colorful Sum of Radii
We study the colorful sum of radii problem, where the input is a point set $P$ partitioned into classes $P_1, P_2, \dots, P_ω$, along with per-class outlier bounds $m_1, m_2, \dots, m_ω$, summing to $m$. The goal is to select a subset $\mathcal{C} \subseteq P$ of $k$ centers and assign points to centers in $\mathcal{C}$, allowing up to $m_i$ unassigned points (outliers) from each class $P_i$, while minimizing the sum of cluster radii. The radius of a cluster is defined as the maximum distance…

Leonessa: An Extremely Metal-poor Galaxy Undergoing Secular Chemical Evolution
Joseph A. Breneman, Kristen B. W. McQuinn, Alexander Menchaca, Danielle A. Berg, O. Grace Telford, Max J. B. Newman, Andrew Dolphin, Gregory R. Zeimann
https://arxiv.org/abs/2508.09248

Leonessa: An Extremely Metal-poor Galaxy Undergoing Secular Chemical Evolution
Extremely metal-poor (XMP) galaxies are systems with gas-phase oxygen abundances below $\sim$5% Solar metallicity (12+log(O/H)$\le$7.35). These galaxies populate the metal-poor end of the mass-metallicity and luminosity-metallicity relations (MZR and LZR, respectively). Recent studies have found XMP galaxies in the nearby Universe to be outliers on the LZR, where they show enhanced luminosities relative to other galaxies of similar gas-phase oxygen abundance. Here, we present a study of the rec…

An empirical comparison of some outlier detection methods with longitudinal data
Marcello D'Orazio
https://arxiv.org/abs/2507.21203 https://arxiv.org/p…

An empirical comparison of some outlier detection methods with longitudinal data
This note investigates the problem of detecting outliers in longitudinal data. It compares well-known methods used in official statistics with proposals from the fields of data mining and machine learning that are based on the distance between observations or binary partitioning trees. This is achieved by applying the methods to panel survey data related to different types of statistical units. Traditional methods are quite simple, enabling the direct identification of potential outliers, but t…

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Cassandra Tong Ye, Shamus Li, Tyler King, Kristina Monakhova
https://arxiv.org/abs/2507.14760

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Deep learning models often hallucinate, producing realistic artifacts that are not truly present in the sample. This can have dire consequences for scientific and medical inverse problems, such as MRI and microscopy denoising, where accuracy is more important than perceptual quality. Uncertainty quantification techniques, such as conformal prediction, can pinpoint outliers and provide guarantees for image regression tasks, improving reliability. However, existing methods utilize a linear consta…

Robust Data Fusion via Subsampling
Jing Wang, HaiYing Wang, Kun Chen
https://arxiv.org/abs/2508.12048 https://arxiv.org/pdf/2508.12048

Robust Data Fusion via Subsampling
Data fusion and transfer learning are rapidly growing fields that enhance model performance for a target population by leveraging other related data sources or tasks. The challenges lie in the various potential heterogeneities between the target and external data, as well as various practical concerns that prevent a naïve data integration. We consider a realistic scenario where the target data is limited in size while the external data is large but contaminated with outliers; such data contami…

On Tight Robust Coresets for $k$-Medians Clustering
Lingxiao Huang, Zhenyu Jiang, Yi Li, Xuan Wu
https://arxiv.org/abs/2507.11260 https://

On Tight Robust Coresets for $k$-Medians Clustering
This paper considers coresets for the robust $k$-medians problem with $m$ outliers, and new constructions in various metric spaces are obtained. Specifically, for metric spaces with a bounded VC or doubling dimension $d$, the coreset size is $O(m) + \tilde{O}(kd\varepsilon^{-2})$, which is optimal up to logarithmic factors. For Euclidean spaces, the coreset size is $O(m\varepsilon^{-1}) + \tilde{O}(\min\{k^{4/3}\varepsilon^{-2},k\varepsilon^{-3}\})$, improving upon a recent result by Jiang and …

A Mathematical Optimization Approach to Multisphere Support Vector Data Description
V\'ictor Blanco, Inmaculada Espejo, Ra\'ul P\'aez, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2507.11106

A Mathematical Optimization Approach to Multisphere Support Vector Data Description
We present a novel mathematical optimization framework for outlier detection in multimodal datasets, extending Support Vector Data Description approaches. We provide a primal formulation, in the shape of a Mixed Integer Second Order Cone model, that constructs Euclidean hyperspheres to identify anomalous observations. Building on this, we develop a dual model that enables the application of the kernel trick, thus allowing for the detection of outliers within complex, non-linear data structures.…

Outlier-robust Bayesian Multivariate Analysis with Correlation-intact Sandwich Mixture
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
https://arxiv.org/abs/2508.18004 https://

Outlier-robust Bayesian Multivariate Analysis with Correlation-intact Sandwich Mixture
Handling outliers is a fundamental challenge in multivariate data analysis, as outliers may distort structures of correlation or conditional independence. Although robust Bayesian inference has been extensively studied for univariate settings, theoretical results ensuring posterior robustness in multivariate models are scarce. We propose a novel scale mixture of multivariate normals called correlation-intact sandwich mixture, where the scale parameters are real-valued and follow the unfolded lo…

Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence
Saida Mancer, Abdelhakim Necir, Djamel Meraghni
https://arxiv.org/abs/2507.15744

Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence
By introducing a weight function into the density power divergence, we develop a new class of robust and smooth estimators for the tail index of Pareto-type distributions, offering improved efficiency in the presence of outliers. These estimators can be viewed as a robust generalization of both weighted least squares and kernel-based tail index estimators. We establish the consistency and asymptotic normality of the proposed class. A simulation study is conducted to assess their finite-sample p…

Dynamic allocation: extremes, tail dependence, and regime Shifts
Yin Luo, Sheng Wang, Javed Jussa
https://arxiv.org/abs/2506.12587 https://

Dynamic allocation: extremes, tail dependence, and regime Shifts
By capturing outliers, volatility clustering, and tail dependence in the asset return distribution, we build a sophisticated model to predict the downside risk of the global financial market. We further develop a dynamic regime switching model that can forecast real-time risk regime of the market. Our GARCH-DCC-Copula risk model can significantly improve both risk- and alpha-based global tactical asset allocation strategies. Our risk regime has strong predictive power of quantitative equity fac…

Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks
Mary Chriselda Antony Oliver, Emmanuel Hartman, Tom Needham
https://arxiv.org/abs/2508.10888

Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks
The Gromov-Wasserstein (GW) variant of optimal transport, designed to compare probability densities defined over distinct metric spaces, has emerged as an important tool for the analysis of data with complex structure, such as ensembles of point clouds or networks. To overcome certain limitations, such as the restriction to comparisons of measures of equal mass and sensitivity to outliers, several unbalanced or partial transport relaxations of the GW distance have been introduced in the recent …

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Haoqing Li, Ricardo Borsoi, Tales Imbiriba, Pau Closas
https://arxiv.org/abs/2506.13733

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Multiresolution image fusion is a key problem for real-time satellite imaging and plays a central role in detecting and monitoring natural phenomena such as floods. It aims to solve the trade-off between temporal and spatial resolution in remote sensing instruments. Although several algorithms have been proposed for this problem, the presence of outliers such as clouds downgrades their performance. Moreover, strategies that integrate robustness, recursive operation and learned models are missin…

Doubly robust outlier resistant inference on causal treatment effect
Joonsung Kang
https://arxiv.org/abs/2507.17439 https://arxiv.org/pdf/2507.17439…

Doubly robust outlier resistant inference on causal treatment effect
Outliers can severely distort causal effect estimation in observational studies, yet this issue has received limited attention in the literature. Their influence is especially pronounced in small sample sizes, where detecting and removing outliers becomes increasingly difficult. Therefore, it is essential to estimate treatment effects robustly without excluding these influential data points. To address this, we propose a doubly robust point estimator for the average treatment effect under a con…

An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
Takeyuki Sasai, Hironori Fujisawa
https://arxiv.org/abs/2506.12591 https…

An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
Sparse linear regression methods such as Lasso require a tuning parameter that depends on the noise variance, which is typically unknown and difficult to estimate in practice. In the presence of heavy-tailed noise or adversarial outliers, this problem becomes more challenging. In this paper, we propose an estimator for robust and sparse linear regression that eliminates the need for explicit prior knowledge of the noise scale. Our method builds on the Huber loss and incorporates an iterative sc…

GLASD: A Loss-Function-Agnostic Global Optimizer for Robust Correlation Estimation under Data Contamination and Heavy Tails
Priyam Das
https://arxiv.org/abs/2506.14801

GLASD: A Loss-Function-Agnostic Global Optimizer for Robust Correlation Estimation under Data Contamination and Heavy Tails
Robust correlation estimation is essential in high-dimensional settings, particularly when data are contaminated by outliers or exhibit heavy-tailed behavior. Many robust loss functions of practical interest-such as those involving truncation or redescending M-estimators-lead to objective functions that are inherently non-convex and non-differentiable. Traditional methods typically focus on a single loss function tailored to a specific contamination model and develop custom algorithms tightly c…

Robust copula estimation for one-shot devices with correlated failure modes
E. Castilla, P. J. Chocano
https://arxiv.org/abs/2506.10152 https://

Robust copula estimation for one-shot devices with correlated failure modes
This paper presents a robust method for estimating copula models to evaluate dependence between failure modes in one-shot devices-systems designed for single use and destroyed upon activation. Traditional approaches, such as maximum likelihood estimation (MLE), often produce unreliable results when faced with outliers or model misspecification. To overcome these limitations, we introduce a divergence-based estimation technique that enhances robustness and provides a more reliable characterizati…