Lukasz Korycki

According to our database1, Lukasz Korycki authored at least 15 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Correction: Adversarial concept drift detection under poisoning attacks for robust data stream mining.
Mach. Learn., May, 2024

Class-Incremental Mixture of Gaussians for Deep Continual Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Adversarial concept drift detection under poisoning attacks for robust data stream mining.
Mach. Learn., October, 2023

2022
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams.
Pattern Recognit., 2022

2021
Mining Drifting Data Streams on a Budget: Combining Active Learning with Self-Labeling.
CoRR, 2021

Streaming Decision Trees for Lifelong Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Low-Dimensional Representation Learning from Imbalanced Data Streams.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2021

Concept Drift Detection from Multi-Class Imbalanced Data Streams.
Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Class-Incremental Experience Replay for Continual Learning Under Concept Drift.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020
Adaptive Deep Forest for Online Learning from Drifting Data Streams.
CoRR, 2020

Online Oversampling for Sparsely Labeled Imbalanced and Non-Stationary Data Streams.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019
Unsupervised Drift Detector Ensembles for Data Stream Mining.
Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics, 2019

Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
Clustering-Driven and Dynamically Diversified Ensemble for Drifting Data Streams.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017
Combining Active Learning and Self-Labeling for Data Stream Mining.
Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, 2017


  Loading...