A5063640638- Data Stream Mining Techniques
- Anomaly Detection Techniques and Applications
- Time Series Analysis and Forecasting
- Machine Learning and Data Classification
- Advanced Bandit Algorithms Research
- Smart Grid Energy Management
- Microgrid Control and Optimization
- Healthcare Operations and Scheduling Optimization
- Advanced Control Systems Optimization
- Internet Traffic Analysis and Secure E-voting
- Reinforcement Learning in Robotics
- Optimal Power Flow Distribution
- Neural Networks and Applications
- Data Mining Algorithms and Applications
- Auction Theory and Applications
- Network Security and Intrusion Detection
- Metaheuristic Optimization Algorithms Research
- Fault Detection and Control Systems
Karlsruhe Institute of Technology
2022-2024
University of Technology Sydney
2022
Nanjing University
2022
Arizona State University
2022
University of Minnesota
2022
Singapore Management University
2022
Osaka University
2022
Southwest Jiaotong University
2022
Korea Advanced Institute of Science and Technology
2022
Maebashi Institute of Technology
2022
CapyMOA is an open-source library designed for efficient machine learning on streaming data. It provides a structured framework real-time and evaluation, featuring flexible data representation. includes extensible architecture that allows integration with external frameworks such as MOA PyTorch, facilitating hybrid approaches combine traditional online algorithms deep techniques. By emphasizing adaptability, scalability, usability, researchers practitioners to tackle dynamic challenges...
Abstract Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring prediction systems to react, e.g., by issuing an alarm or updating a learning algorithm. However, detecting challenging observations are high-dimensional. In high-dimensional data, change detectors should not only be able identify happen, but also in which subspace they occur. Ideally, one quantify how severe are. Our approach, ABCD, has these...
Experimental studies are a cornerstone of machine learning (ML) research. A common, but often implicit, assumption is that the results study will generalize beyond itself, e.g. to new data. That is, there high probability repeating under different conditions yield similar results. Despite importance concept, problem measuring generalizability remains open. This probably due lack mathematical formalization experimental studies. In this paper, we propose such and develop quantifiable notion...
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from K arms with unknown expected rewards and costs. The goal is to maximize total reward under budget constraint. A thus seeks choose arm highest reward-cost ratio as often possible. Current approaches for this problem have several issues, which we illustrate. To overcome them, propose new upper confidence bound (UCB) sampling policy, ømega-UCB, that uses asymmetric intervals. These intervals scale...
Today, the collection of decentralized data is a common scenario: smartphones store users' messages locally, smart meters collect energy consumption data, and modern power tools monitor operator behavior. We identify different types outliers in such data: local, global, partition outliers. They contain valuable information, for example, about mistakes operation. However, existing outlier detection approaches cannot distinguish between those types. Thus, we propose "tandem" technique to join...
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from $K$ arms with unknown expected rewards and costs. The goal is to maximize total reward under budget constraint. A thus seeks choose arm highest reward-cost ratio as often possible. Current state-of-the-art policies for this problem have several issues, which we illustrate. To overcome them, propose new upper confidence bound (UCB) sampling policy, $\omega$-UCB, that uses asymmetric intervals. These...
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring prediction systems to react, e.g., by issuing an alarm or updating a learning algorithm. However, detecting challenging observations are high-dimensional. In high-dimensional data, change detectors should not only be able identify happen, but also in which subspace they occur. Ideally, one quantify how severe are. Our approach, ABCD, has these...
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., predictive maintenance, fraud detection, or medicine. A principled approach to detect compare the distributions observations within stream each other via hypothesis testing. Maximum mean discrepancy (MMD; also called energy distance) a well-known (semi-)metric on space probability distributions. MMD gives rise powerful non-parametric two-sample tests kernel-enriched domains under mild...