Anomaly search over discrete composite hypotheses in hierarchical statistical models
Detection of anomalies among a large number of processes is a fundamental task that has
been studied in multiple research areas, with diverse applications spanning from spectrum …
been studied in multiple research areas, with diverse applications spanning from spectrum …
Deep multi-agent reinforcement learning for decentralized active hypothesis testing
H Szostak, K Cohen - IEEE Access, 2024 - ieeexplore.ieee.org
We consider a decentralized formulation of the active hypothesis testing (AHT) problem,
where multiple agents gather noisy observations from the environment with the purpose of …
where multiple agents gather noisy observations from the environment with the purpose of …
Decentralized anomaly detection via deep multi-agent reinforcement learning
H Szostak, K Cohen - 2022 58th Annual Allerton Conference …, 2022 - ieeexplore.ieee.org
We consider a decentralized anomaly detection problem, where multiple agents collaborate
to localize a single anomalous process among a finite number M of processes. At each time …
to localize a single anomalous process among a finite number M of processes. At each time …
Composite anomaly detection via hierarchical dynamic search
Anomaly detection among a large number of processes arises in many applications ranging
from dynamic spectrum access to cybersecurity. In such problems one can often obtain noisy …
from dynamic spectrum access to cybersecurity. In such problems one can often obtain noisy …
An optimization method for out-of-distribution anomaly detection models
J Qiu, H Shi, YH Hu, Z Yu - arXiv preprint arXiv:2302.00939, 2023 - arxiv.org
Frequent false alarms impede the promotion of unsupervised anomaly detection algorithms
in industrial applications. Potential characteristics of false alarms depending on the trained …
in industrial applications. Potential characteristics of false alarms depending on the trained …
Anomaly Search of a Hidden Markov Model
We address the problem of detecting an anomalous process among a large number of
processes. At each time t, normal processes are in state zero (normal state), whereas the …
processes. At each time t, normal processes are in state zero (normal state), whereas the …
Asymptotically optimal sequential anomaly identification with ordering sampling rules
A Tsopelakos, G Fellouris - arXiv preprint arXiv:2309.14528, 2023 - arxiv.org
The problem of sequential anomaly detection and identification is considered in the
presence of a sampling constraint. Specifically, multiple data streams are generated by …
presence of a sampling constraint. Specifically, multiple data streams are generated by …
Methods of Semantic Structured Search
Y Pohuliaiev, K Smelyakov… - 2022 IEEE 9th …, 2022 - ieeexplore.ieee.org
In this paper, we consider a method of documentary search based on a weighted
assessment of the results of semantic and structured search methods. The main advantages …
assessment of the results of semantic and structured search methods. The main advantages …
Anomaly Search over Composite Hypotheses in Hierarchical Statistical Models
Detection of anomalies among a large number of processes is a fundamental task that has
been studied in multiple research areas, with diverse applications spanning from spectrum …
been studied in multiple research areas, with diverse applications spanning from spectrum …
Complex Theory and Batch Processing in Mechanical Systemic Data Extraction
X Chang, H Pan, J Xu, S Qiao, T Wang - IEEE Access, 2022 - ieeexplore.ieee.org
This paper designs a new batching program to extract the original data, which helps to
traverse the entire sample space quickly and provides a new approach for data extraction …
traverse the entire sample space quickly and provides a new approach for data extraction …