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.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
The study defines backdoor detection in ML and proves its infeasibility.
We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant dimensions, and another for selecting CPs. It has been difficult to properly contro…
Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection…
Detects graph topology changes from noisy signals using prior spectral information.
Detects changes in classifier scores to identify shifts in class priors.
Simulated Bifurcation outperforms quantum machines in community detection.
Automates detecting problem statements in peer assessments.
Optimizes quickest detection of drift in Brownian motion with false negatives.
Improved software flaw detection using NAS on multimodal DL models.
We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We s…
We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data dimension scales. In particular, we consider a general approach where changes are detected by comparing the distribution of the log-likelihood of …
Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years. Generative Adversarial Networks (GANs) and the adversarial training process have been rec…
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised…
Develops a method to detect changes in linear systems with temporal correlations.
AUCRSS detects change points in partially observed multivariate autocorrelated data.
Capsule networks improve anomaly detection in high-dimensional datasets.
Detecting correlated trees helps align sparse graphs.
A framework is proposed to detect anomalies in multi-modal data. A deep neural network-based object detector is employed to extract counts of objects and sub-events from the data. A cyclostationary model is proposed to model regular patterns of behavior in the count sequences. The anomaly detection problem is formulate…
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…
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traff…
ARCADe detects anomalies in a sequence of tasks with limited data.
New method detects anomalies in systems influenced by their environment.
We show linear XOR classification is possible and propose equality separation for anomaly detection.
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we propose to use community detection to tackle the problem of secure friend discovery…
Unified framework for online LLM watermark detection using e-processes.
Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCB…
Hashing detects anomalies in structured data efficiently.
Study on detecting hierarchical community structures in networks.
Detection of dense cycles in graphs reveals a gap between easy detection and hard recovery.
InQMAD detects anomalies in streaming data using quantum measurements and density matrices.
Analyzes methods for detecting communities in networks.
We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we p…
Develops a nonparametric framework for detecting changes in sequential data.
Deep learning improves anomaly detection across various fields.
New algorithm detects anomalies by forcing samples to displace mass in low-density regions.
New approach predicts event probabilities for better event detection.
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
We formalize the problem of detecting a community in a network into testing whether in a given (random) graph there is a subgraph that is unusually dense. We observe an undirected and unweighted graph on N nodes. Under the null hypothesis, the graph is a realization of an Erdös-Rényi graph with probability p0. Under th…
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, dat…
We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a "mixture of s…
An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: ran…
Detecting edge correlation between two graphs sharpens a threshold based on densest subgraph.
New method for mixed memberships using symmetrized Laplacian inverse matrix.
Adaptive anomaly detection for IoT data reduces delay by 84%.
The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems und…