A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithm identifies outliers in PCA without needing parameters.
Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the column sparse outlier model. The existing methods for column sparse outlier model assumes either the knowledge of the dimension of the lower dimensional subspace or the …
Paper uses genome Markov structure for outlier detection and read classification.
A new algorithm identifies outliers in Gaussian clustering models.
In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential …
Enhances clustering for functional data, robust to outliers.
Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.
Identifies arms with rewards significantly different from the majority.
Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robust…
Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers. Recently, the Robust PCA approach via nuclear norm minimization has been extended to matrices with linear structures which appear in applications such as system identificat…
This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance; our results show that accurate outlier identification is achievable using very few linear sum…
Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from the observed sum. Such additive decompositions have applications in a variety of numerical problems including system identification, latent var…
COPOD detects outliers efficiently and interpretable using copulas.
Traditional dictionary learning methods are based on quadratic convex loss function and thus are sensitive to outliers. In this paper, we propose a generic framework for robust dictionary learning based on concave losses. We provide results on composition of concave functions, notably regarding super-gradient computati…
Paper introduces Prob-SSI for robust OMA in noisy data.
The paper provides robustness guarantees for mode estimation in bandits.
PRAE identifies outliers and reconstructs inliers in autoencoders.
ECOD detects outliers without parameters, fast and simple.
Detects outliers in continuous-time event sequences, including unexpected absences and occurrences.
Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimizat…
We consider the task of robust non-linear regression in the presence of both inlier noise and outliers. Assuming that the unknown non-linear function belongs to a Reproducing Kernel Hilbert Space (RKHS), our goal is to estimate the set of the associated unknown parameters. Due to the presence of outliers, common techni…
Paper recovers multi-subspace matrices from permuted data.
This paper uses RBM to calculate conditional probabilities for nonlinear system identification.
Paper tackles adaptive sampling for identifying largest gaps between distributions.
Detects outliers in VAE latent space by identifying vacant holes.
Study compares statistical and machine learning models for detecting crypto trading anomalies.
The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model order selection step is needed to suitably trade-off bias and variance. Recently, a different approach to linear system identification has been…
Enhances OOD detection using latent diffusion for more robust and efficient training.
Improved random forest proximities capture data geometry.
This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows. Since traffic data is lar…
Auditor package assesses model performance and detects anomalies.
New method for group testing robust to errors in group membership specifications.
The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation. While most works focus on local optimization heuristics without global optimality guarantees or with guarantees valid only…
Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, well-documented sensitivity to outliers. Recent work has considered the setting where each point has …
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
New method for robust PCA with exponential family distributions.
Paper addresses state estimation in sensor networks with intermittent data.
CoCAI uses copulas for accurate multivariate time-series forecasting and anomaly detection.
New method detects outliers in correlated data.
We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure…
A novel approach ODAR detects outliers for clustering.
New framework detects outliers in non-IID categorical data.
Outlier detection aims to identify unusual data instances that deviate from expected patterns. The outlier detection is particularly challenging when outliers are context dependent and when they are defined by unusual combinations of multiple outcome variable values. In this paper, we develop and study a new conditiona…
Paper proposes methods to make OT robust to outliers.
Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers do…
Survey compares methods for generating artificial outliers.
Paper tackles outlier detection in signals modeled by generative models with theoretical guarantees.
Transforms distance-based outlier scores into interpretable probabilistic estimates.