Tootfinder

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…

The influence of data gaps and outliers on resilience indicators
Teng Liu, Andreas Morr, Sebastian Bathiany, Lana L. Blaschke, Zhen Qian, Chan Diao, Taylor Smith, Niklas Boers
https://arxiv.org/abs/2505.19034

The influence of data gaps and outliers on resilience indicators
The resilience, or stability, of major Earth system components is increasingly threatened by anthropogenic pressures, demanding reliable early warning signals for abrupt and irreversible regime shifts. Widely used data-driven resilience indicators based on variance and autocorrelation detect `critical slowing down', a signature of decreasing stability. However, the interpretation of these indicators is hampered by poorly understood interdependencies and their susceptibility to common data issue…

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…

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…

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…

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…

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…

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…

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…

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

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…

The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD $o^2$)
Jordan Barrett, Ryan DeWolfe, Bogumi{\l} Kami\'nski, Pawe{\l} Pra{\l}at, Aaron Smith, Fran\c{c}ois Th\'eberge
https://arxiv.org/abs/2506.05486

The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+$o^2$)
The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster, more interpretable, and can be investigated analytically. In this paper, we use the underlying ingredients of the ABCD model, and its generalization to include outliers (ABCD+$o$), and introduce another variant that allows for overlapping com…

Robust Distributed Phase Retrieval for Multi-View Compressive Networked Sensing With Outliers
Ming-Hsun Yang
https://arxiv.org/abs/2505.24651 https://

Robust Distributed Phase Retrieval for Multi-View Compressive Networked Sensing With Outliers
This work examines the multi-view compressive phase retrieval problem in a distributed sensor network, where each sensor device, limited by storage and sensing capabilities, can access only intensity measurements from an unknown part of the global sparse vector. The goal is to enable each sensor to recover its observable sparse signal when measurements are corrupted by outliers. To achieve reliable local signal recovery with limited data access, we propose a distributed reconstruction algorithm…

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…

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…

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…

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.…

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…

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…

This https://arxiv.org/abs/2505.07614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy
Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their structure is vulnerable to malicious influences. In this paper, we address a specific threat, Byzantine attacks, where compromised clients inject adversarial updates to derail global convergence. We combine the trust scores concept with trial function methodology to dynamically filter outliers. Our methods address the c…

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…

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…

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu, Zhihan Zhang, Kate Lin, Yuwei Zhang, Akshay Paruchuri, Hong Yu, Mehran Kazemi, Kumar Ayush, A. Ali Heydari, Maxwell A. Xu, Girish Narayanswamy, Yun Liu, Ming-Zher Poh, Yuzhe Yang, Mark Malhotra, Shwetak Patel, Hamid Palangi, Xuhai Xu, Daniel McDuff, Tim Althoff, Xin Liu
https://

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness -- the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies -- remains underexplored. These artifacts are especially common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for…

Subspace-constrained randomized coordinate descent for linear systems with good low-rank matrix approximations
Jackie Lok, Elizaveta Rebrova
https://arxiv.org/abs/2506.09394

Subspace-constrained randomized coordinate descent for linear systems with good low-rank matrix approximations
The randomized coordinate descent (RCD) method is a classical algorithm with simple, lightweight iterations that is widely used for various optimization problems, including the solution of positive semidefinite linear systems. As a linear solver, RCD is particularly effective when the matrix is well-conditioned; however, its convergence rate deteriorates rapidly in the presence of large spectral outliers. In this paper, we introduce the subspace-constrained randomized coordinate descent (SC-RCD…

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…

This https://arxiv.org/abs/2504.14515 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A robust mixed-effects quantile regression model using generalized Laplace mixtures to handle outliers and skewness
Mixed-effects quantile regression models are widely used to capture heterogeneous responses in hierarchically structured data. The asymmetric Laplace (AL) distribution has traditionally served as the basis for quantile regression; however, its fixed skewness limits flexibility and renders it sensitive to outliers. In contrast, the generalized asymmetric Laplace (GAL) distribution enables more flexible modeling of skewness and heavy-tailed behavior, yet it remains vulnerable to extreme observati…

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…

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…

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…

Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers
Wanshan Cui, Yejin Jeong, Inwook Song, Gyuri Kim, Minsang Kwon, Donghun Lee
https://arxiv.org/abs/2506.01994

Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers
Accurate prediction of polymer material properties through data-driven approaches greatly accelerates novel material development by reducing redundant experiments and trial-and-error processes. However, inevitable outliers in empirical measurements can severely skew machine learning results, leading to erroneous prediction models and suboptimal material designs. To address this limitation, we propose a novel approach to enhance dataset quality efficiently by integrating multi-algorithm outlier …

This https://arxiv.org/abs/2505.18975 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
State Space Models (SSMs), like recent Mamba2, have achieved remarkable performance and received extensive attention. However, deploying Mamba2 on resource-constrained edge devices encounters many problems: severe outliers within the linear layer challenging the quantization, diverse and irregular element-wise tensor operations, and hardware-unfriendly nonlinear functions in the SSM block. To address these issues, this paper presents FastMamba, a dedicated accelerator on FPGA with hardware-algo…

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.

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
https://arxiv.org/abs/2506.01907 https://

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than data anonymization in balancing this trade-off, however, not without its own challenges. Synthetic data produced by generative models trained on source data may inadvertently reveal information about outliers. Techniques specifically designed for preserving privacy, such as introducing noise to satisfy differential priva…

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…

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…

Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
Amer Krivo\v{s}ija, Alexander Munteanu, Andr\'e Nusser, Chris Schwiegelshohn
https://arxiv.org/abs/2505.23431

Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
This paper introduces $k$-Dynamic Time Warping ($k$-DTW), a novel dissimilarity measure for polygonal curves. $k$-DTW has stronger metric properties than Dynamic Time Warping (DTW) and is more robust to outliers than the Fréchet distance, which are the two gold standards of dissimilarity measures for polygonal curves. We show interesting properties of $k$-DTW and give an exact algorithm as well as a $(1+\varepsilon)$-approximation algorithm for $k$-DTW by a parametric search for the $k$-th lar…

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…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
The Tien Mai
https://arxiv.org/abs/2506.07790 https:…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies on the squared loss, performs well under Gaussian noise assumptions but often deteriorates in the presence of heavy-tailed errors or outliers commonly encountered in real data applications such as genomics, finance, and signal processing. To address these challenges, we propose a novel robust regression method, terme…

This https://arxiv.org/abs/2505.19034 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

The influence of data gaps and outliers on resilience indicators
The resilience, or stability, of major Earth system components is increasingly threatened by anthropogenic pressures, demanding reliable early warning signals for abrupt and irreversible regime shifts. Widely used data-driven resilience indicators based on variance and autocorrelation detect `critical slowing down', a signature of decreasing stability. However, the interpretation of these indicators is hampered by poorly understood interdependencies and their susceptibility to common data issue…

The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
Jiang Hu, Jiahui Xie, Yangchun Zhang, Wang Zhou
https://arxiv.org/abs/2506.05116

The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
Factor models are essential tools for analyzing high-dimensional data, particularly in economics and finance. However, standard methods for determining the number of factors often overestimate the true number when data exhibit heavy-tailed randomness, misinterpreting noise-induced outliers as genuine factors. This paper addresses this challenge within the framework of Elliptical Factor Models (EFM), which accommodate both heavy tails and potential non-linear dependencies common in real-world da…