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.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
Detects crypto pump-and-dump schemes with a thresholding-based model.
Sharp thresholds and contiguity for community detection in contextual SBM.
New method calibrates false detection rates in sequential change detection.
Proposes ATH for KPI anomaly detection based on local data properties.
Spectral method detects communities in sparse hypergraphs, achieving detection threshold.
New algorithm detects communities even with corrupted data, reaching Kesten-Stigum threshold.
The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameter…
In this work, we derive a generic overcomplete frame thresholding scheme based on risk minimization. Overcomplete frames being favored for analysis tasks such as classification, regression or anomaly detection, we provide a way to leverage those optimal representations in real-world applications through the use of thre…
Study community detection in multi-view data with various types of information.
Detecting edge correlation between two graphs sharpens a threshold based on densest subgraph.
New stability thresholds detect K-stability in Fano manifolds.
Improves interpretability of anomaly scores in GBRBM-based detection.
Graph energy helps detect communities in networks better than traditional methods.
Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.
Paper studies community detection in censored hypergraphs using information theory.
Detection of dense cycles in graphs reveals a gap between easy detection and hard recovery.
The stochastic block model is one of the oldest and most ubiquitous models for studying clustering and community detection. In an exciting sequence of developments, motivated by deep but non-rigorous ideas from statistical physics, Decelle et al. conjectured a sharp threshold for when community detection is possible in…
To estimate a sparse linear model from data with Gaussian noise, consilience from lasso and compressed sensing literatures is that thresholding estimators like lasso and the Dantzig selector have the ability in some situations to identify with high probability part of the significant covariates asymptotically, and are …
New algorithms detect communities in sparse graphs with labeled data.
Paper uses SDP for community detection with side information.
We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated by a hypergraph stochastic block model. We solve the positive part of the conjecture for the case of…
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicabili…
Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with "known" number of clusters. The paper provides a streaming clustering and anomaly detection algorithm that does not require strict arbitrary…
Study on detecting and recovering hidden dense cycles in random graphs.
Proposes ACLAE-DT for unsupervised anomaly detection in multivariate time series.
In this paper, we study the sensitivity of the spectral clustering based community detection algorithm subject to a Erdos-Renyi type random noise model. We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probability under a general…
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced by Johnstone, in which a prominent eigenvector (or "spike") is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughou…
New algorithm detects communities near KS threshold with optimal rate, even in noisy conditions.
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these tests aim to estimate the bar for granting loans to white and minority applicants, with a higher inferred threshold for minorities indicative…
Paper improves anomaly detection by using non-uniform random choices in isolation forests.
In this paper, we propose a new threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. We use the expected number of jump misclassifications as the objective function to…
A martingale framework for concept change detection based on testing data exchangeability was recently proposed (Ho, 2005). In this paper, we describe the proposed change-detection test based on the Doob's Maximal Inequality and show that it is an approximation of the sequential probability ratio test (SPRT). The relat…
Detects dense subhypergraphs in random hypergraphs using low-degree polynomials.
Bayesian method estimates contamination factor for unsupervised anomaly detection.
Proposes Likelihood Regret for VAEs to improve OOD detection.
New model for community detection with side information improves recovery accuracy.
Paper introduces WWAggr for ensemble CPD, improving accuracy and decision threshold selection.
We propose an efficient meta-algorithm for Bayesian estimation problems that is based on low-degree polynomials, semidefinite programming, and tensor decomposition. The algorithm is inspired by recent lower bound constructions for sum-of-squares and related to the method of moments. Our focus is on sample complexity bo…
We study the fundamental limits of detecting the presence of an additive rank-one perturbation, or spike, to a Wigner matrix. When the spike comes from a prior that is i.i.d. across coordinates, we prove that the log-likelihood ratio of the spiked model against the non-spiked one is asymptotically normal below a certai…
Real-time fuel leakage detection framework MOCPD improves accuracy.
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a …
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, in which a prominent eigenvector is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughout the sciences. Baik, Ben Arous and Pé…
New Bethe-Hessian method improves community detection in sparse networks.
A new algorithm detects out-of-distribution samples by concentrating them in feature space.
Paper proves integrability and entropy compactness for Kähler potentials with uniform log-log threshold.
New method selects recent similar periods for better electricity price forecasting.
Under Markovian assumptions, we leverage a Central Limit Theorem (CLT) for the empirical measure in the test statistic of the composite hypothesis Hoeffding test so as to establish weak convergence results for the test statistic, and, thereby, derive a new estimator for the threshold needed by the test. We first show t…