In this paper, we investigate Thompson sampling-based sequential block elimination approaches for dynamic assignment problems in a pure-exploration Multi-Armed Bandit (MAB) setting with limited budget constraints. The problem can be considered as ...

We propose a new sequential decision-making setting, combining key aspects of two established online learning problems with bandit feedback. The optimal action to play at any given moment is contingent on an underlying changing state that is not ...

Annually, over 800,000 individuals are victims of cyberattacks, predominantly through malware, which possesses the capacity to emerge as a formidable instrument of destruction in the realm of cybersecurity. And, it is a challenging task to ...

PathSim is a widely used meta-path-based similarity in heterogeneous information networks (HINs). Numerous applications rely on the computation of PathSim, including similarity search and clustering. Computing PathSim scores on large HINs is ...

Reinforcement Learning (RL) knowledge graph reasoning aims to predict complete triplets by learning existing relationship paths. This greatly improves the efficiency of prediction because the RL-based methods do not traverse all entities and ...

It is well known that seismic data (or seismic volumes/images) are one of the primary work materials of the oil and gas industry. Nevertheless, the manual interpretation of such data has become increasingly time-consuming and prone to errors. This ...

Anomaly detection has become pervasive in modern technology, covering applications from cybersecurity, to medicine or system failure detection. Before outputting a binary outcome (i.e., anomalous or non-anomalous), most algorithms evaluate ...

Tensor decompositions have proven to be effective in analyzing the structure of multidimensional data. However, most of these methods require a key parameter: the number of desired components. In the case of the CANDECOMP/PARAFAC decomposition (...$\mathrm{}$$\mathrm{}$

We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, ...

Many algorithms have been proposed to learn local graphical structures around target variables of interest from observational data, focusing on two sets of variables. The first one, called Parent–Children (PC) set, contains all the variables that ...

High dimensional learning is a perennial problem due to challenges posed by the “curse of dimensionality”; learning typically demands more computing resources as well as more training data. In differentially private (DP) settings, this is further ...

In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only ...

Time Series Classification (TSC) is essential in fields like medicine, environmental science, and finance, enabling tasks such as disease diagnosis, anomaly detection, and stock price analysis. While machine learning models like Recurrent Neural ...

Modern data-oriented applications often require integrating data from multiple heterogeneous sources. When these datasets share attributes, but are otherwise unlinked, there is no way to join them and reason at the individual level explicitly. ...

Quantification, i.e., the task of predicting the class prevalence values in bags of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms for binary ...$\mathrm{}$

Label propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node classification. Many label propagation algorithms utilize random walks (or network ...

With the advancement of technology, real-world networks have become vulnerable to many attacks such as cyber-crimes, terrorist attacks, and financial frauds. Accuracy and scalability are the two principal but contrary characteristics for ...

Consider a large labeled graph (network), denoted the target. Subgraph matching is the problem of finding all instances of a small subgraph, denoted the query, in the target graph. Unlike the majority of existing methods that are restricted to ...