Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
problem Computational hardness of hypergraphic planted clique detection.
method No specific method mentioned; focuses on conjecturing equivalence.
result Equivalence of computational hardness between HPC and PC detection.
New method detects change points in multi-dimensional sequences, controlling false detection.
problem Detecting change points in sequences with multiple dimensions.
method Two-stage approach: select relevant dimensions and CPs, using selective inference.
result Exact inference possible for a class of CP detection methods.
The study defines backdoor detection in ML and proves its infeasibility.
problem Backdoor detection in machine learning systems.
method Formal statistical definition and analysis of feasibility.
result Backdoor detection is impossible except for very small alphabet sizes.
Paper proposes RL for real-time smart grid cyber attack detection.
problem Real-time detection of cyber-attacks in smart grids.
method Formulated as POMDP, uses model-free reinforcement learning.
result Effective in timely and accurate detection of cyber-attacks.
Detects missing tensor signals in a KS subspace with high probability.
problem Detecting tensor signals with many missing entities in a KS subspace.
method Projecting the signal onto the KS subspace and bounding residual energy.
result Reliable detection is possible if the missing signal cardinality exceeds KS subspace dimensions.
Detects graph topology changes from noisy signals using prior spectral information.
problem Detecting changes in graph topology from graph signals.
method Leverages graph filtering and subspace detection to distill problem into a CUSUM-based algorithm.
result Demonstrates the effectiveness of incorporating prior spectral signatures for change-point detection.
GANs improve anomaly detection accuracy.
problem Detecting unseen anomalies is challenging.
method Adversarial training of GANs.
result Remarkable results on anomaly detection.
Detects changes in classifier scores to identify shifts in class priors.
problem Label shift changes in classification data.
method Sequential changepoint detection of classifier scores.
result Outperforms other detection procedures in label shift settings.
This paper tackles credit card fraud detection using graph-based learning methods.
problem Detecting credit card fraud to reduce financial losses.
method Graph p-Laplacian based semi-supervised learning combined with undersampling techniques.
result Graph p-Laplacian semi-supervised learning outperforms current methods.
Detecting changes in high-dimensional datastreams is harder than in low-dimensional ones.
problem Difficulty in detecting changes in multivariate datastreams as dimension increases.
method Comparing log-likelihood distributions over different time windows to measure changes.
result The detectability of changes worsens with higher data dimension, a phenomenon called detectability loss.
Paper designs optimal collaboration strategies for signal detection in large networks.
problem Signal detection in large distributed networks with limited communication.
method Designs optimal collaboration strategies using sparse PCA equivalence.
result Optimal collaboration strategies improve detection performance.
A new framework detects changes in multi-armed bandit problems.
problem Change in reward distributions over time in multi-armed bandit problems.
method Change-detection (CD) based UCB policies, CUSUM-UCB, PHT-UCB.
result CUSUM-UCB obtains the best known regret upper bound.
Simulated Bifurcation outperforms quantum machines in community detection.
problem Community detection in complex networks
method Quantum-inspired Simulated Bifurcation algorithm for QUBO formulation
result Simulated Bifurcation achieves highest modularity in community detection
New algorithm detects changes in genomic data faster and more accurately.
problem Detecting changes in genomic data with constraints.
method Adapting a functional pruning technique to solve constrained changepoint detection problems.
result Log-linear time complexity algorithm achieves state-of-the-art accuracy.
Automates detecting problem statements in peer assessments.
problem Identifying problem statements in peer assessment reviews.
method Used machine learning models including neural networks and traditional classifiers.
result Hierarchical Attention Network classifier achieved 93.1% accuracy.
Optimizes quickest detection of drift in Brownian motion with false negatives.
problem Quickest detection of drift in Brownian motion with false negatives.
method Formulated as an optimal multiple stopping problem, then equivalent to a recursive optimal stopping problem, solved using free boundary methods.
result Explicit formulae for expected cost and optimal strategy found.
Improved software flaw detection using NAS on multimodal DL models.
problem Software flaw detection in multimodal deep learning models.
method Adapted NAS framework for multimodal learning, combined with multimodal deep learning models.
result Improved performance on the Juliet Test Suite.
New method detects changes in data streams efficiently.
problem Quickest change-point detection in data streams.
method Inductive Conformal Martingales for quickest change-point detection.
result Inductive Conformal Martingales are efficient under general conditions.
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
AUCRSS detects change points in partially observed multivariate autocorrelated data.
problem Detecting change points in multivariate autocorrelated data with limited sensing resources.
method Adaptive Upper Confidence Region (AUCRSS) with state space model (SSM), adaptive sampling policy, and generalized likelihood ratio test.
result The method outperforms existing approaches in detecting change points efficiently.
Capsule networks improve anomaly detection in high-dimensional datasets.
problem Anomaly detection in high-dimensional, class-imbalanced datasets.
method Used a capsule network architecture with autoencoder pre-training and dynamic routing.
result Capsule network outperformed other models in anomaly detection.
Detects malware-infected clients and malicious domains using transfer learning.
problem Detecting malware-infected computers and malicious web domains from encrypted HTTPS traffic.
method Transfer learning with sluice networks to bootstrap each other's detection models.
result Outperforms known reference models and detects previously unknown malware and domains.
Anomaly detection in multi-modal data using cyclostationary models and neural networks.
problem Detect anomalies in multi-modal data like CCTV imagery and social media posts.
method Deep neural network for object detection, cyclostationary model for regular patterns, sequential anomaly detection algorithms.
result Asymptotically efficient anomaly detection algorithms applied to NYC 5K run detection.
Detecting correlated trees helps align sparse graphs.
problem Detecting correlation between trees for sparse random graphs.
method MPAlign message-passing algorithm for graph alignment.
result MPAlign succeeds in polynomial time for partial alignment.
Paper studies community detection in Degree-Corrected Block Models.
problem Community detection in networks.
method Derives asymptotic minimax risks and proposes an algorithm for consistent community detection.
result Shows how minimax risks depend on degree-correction parameters and network connectivities.
Optimal alarms detect vehicle collisions with theoretical and empirical validation.
problem Detecting dangerous vehicle collisions in real-time.
method Surveyed and compared three classes of collision detection techniques: Monte Carlo, deterministic approximations, and machine learning.
result Monte Carlo sampling is a robust solution for real-time collision detection despite its simplicity.
Enhances anomaly detection using privileged information in one-class SVM.
problem Anomaly detection in normal state description.
method Introduces a new one-class classification algorithm that incorporates privileged information.
result Improved performance in anomaly detection, validated on synthetic and real datasets.
New taxonomy for SCADA-based wind turbine fault detection improves model performance.
problem Lack of consensus on feature causality in normal behavior models.
method Presented a new taxonomy based on causal relations between input features and target.
result Evaluation of different feature configurations on fault detection performance.
Method detects anomalies in small, imbalanced data sets.
problem Anomaly detection in small, imbalanced data sets.
method A novel (1+ε)-class classification method. result Better performance on anomaly detection problems.
New semidefinite programming algorithm detects communities in graphs robustly.
problem Detecting communities in graphs, especially in the stochastic block model.
method Semidefinite programming approach, robust to generative model changes.
result The semidefinite relaxation can be solved efficiently and is very robust.
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…
ARCADe detects anomalies in a sequence of tasks with limited data.
problem Learning a sequence of anomaly detection tasks with only normal class examples.
method Formulated as a meta-learning problem, ARCADe addresses catastrophic forgetting and overfitting.
result ARCADe outperforms baselines on three datasets.
New method detects malware from EXE files efficiently.
problem Detecting malware from raw byte sequences.
method Developed a neural network with linear complexity and batch normalization workaround.
result Identifies interpretable sub-regions of malware in EXE files.
New method detects anomalies in systems influenced by their environment.
problem Detecting anomalies in systems under environmental influence.
method Adversarial learning and time series representation learning.
result Successfully addresses label sparsity and subjectivity in anomaly detection.
TitAnt detects online transaction fraud in milliseconds.
problem Real-time detection of online transaction fraud in e-commerce.
method Feature extraction, real-time prediction, deployment in Ant Financial.
result Efficient real-time fraud detection in milliseconds.
We show linear XOR classification is possible and propose equality separation for anomaly detection.
problem Linearly separating XOR data.
method Equality separation, adapting SVM objective for data within/outside margin.
result Equality separation can detect both seen and unseen anomalies.
Research tackles novelty detection for mixed-type data, proposing probabilistic methods.
problem Detect anomalies in mixed-type datasets like numerical and categorical data.
method Experimental comparison of methods, probabilistic nonparametric model, autoencoder-based model.
result Developed robust methods for mixed-type data novelty detection.
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
problem Detecting PMH signals without prior CSI is challenging and computationally expensive.
method Proposes HEM-ML and HEM-KD schemes using EM and KD-tree for efficient detection.
result Achieves comparable detection results to optimal ML detector with reduced complexity.
Paper detects changes in human behavior using smartphones.
problem Detecting changes in high-dimensional, heterogeneous, and periodic data.
method Hierarchical model with latent variables and non-stationary periodic covariance functions.
result The method accurately detects changes in human behavior using smartphones.
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…
DeepFall detects falls using autoencoders from non-invasive sensors.
problem Detecting rare falls from non-invasive data.
method Uses deep spatio-temporal convolutional autoencoders for anomaly detection.
result Superior fall detection compared to traditional methods.
Unified framework for online LLM watermark detection using e-processes.
problem Detecting AI-generated text from human-written content in online settings.
method Unified framework based on e-processes for anytime-valid hypothesis testing on independence.
result Proposed methods achieve competitive performance in watermark detection.
Hashing detects anomalies in structured data efficiently.
problem Identifying non-conforming samples on low-dimensional manifolds.
method Locality Sensitive Hashing in Preference Space.
result State-of-the-art performance at lower computational cost.
Detection of dense cycles in graphs reveals a gap between easy detection and hard recovery.
problem Detecting and recovering dense cycles in Erdős-Rényi graphs.
method Characterization of computational thresholds for detection and recovery using low-degree polynomial algorithms.
result A gap exists between the detection and recovery thresholds for certain parameter regimes.
InQMAD detects anomalies in streaming data using quantum measurements and density matrices.
problem Detecting anomalies in streaming data with challenges like conceptual drift and continuous learning.
method Incremental anomaly detection based on random Fourier features and quantum measurements.
result InQMAD outperforms 12 state-of-the-art methods in a systematic evaluation.
This study evaluates and compares novelty detection algorithms for discrete sequences.
problem Identifying anomalies in temporal data.
method Experimental comparison of state-of-the-art novelty detection methods on various public and industrial datasets.
result Recommendations for efficient and appropriate methods based on extensive experiments and scalability tests.
Develops a nonparametric framework for detecting changes in sequential data.
problem Detecting changes in nonparametrically specified distributions.
method Introduces e-detectors based on e-processes for nonnegative supermartingales.
result Provides bounds on average run length and detection delay.
Study on detecting hierarchical community structures in networks.
problem Detecting hierarchical community structures in networks.
method Analysis of planted hierarchies of partitions in networks, identifying additional detectability phases.
result There are additional phases in which the presence of multiple consistent partitions can either help or hinder detection of hierarchical structures.