Mykola Pechenizkiy

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2019, 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

DyNMF: Role Analytics in Dynamic Social Networks

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2018, IJCAI.

struc2gauss: Structure Preserving Network Embedding via Gaussian Embedding pdf

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2018, ArXiv.

Supervised Human-Guided Data Exploration

Mykola Pechenizkiy, Kai Puolamäki, Samaneh Khoshrou, 2019, PKDD/ECML Workshops.

HM-EIICT: Fairness-aware link prediction in complex networks using community information

Mykola Pechenizkiy, Akrati Saxena, George Fletcher, 2021, Journal of Combinatorial Optimization.

Predicting the past: memory based copyist and author discrimination in medieval epics.

Mykola Pechenizkiy, Mike Kestemont, Karl Tuyls, 2009 .

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders pdf

Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu, 2020, Machine Learning.

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders

Mykola Pechenizkiy, D. Mocanu, Zahra Atashgahi, 2021, Machine Learning.

A survey on concept drift adaptation

A. Bifet, I. Žliobaitė, Mykola Pechenizkiy, 2014, ACM Comput. Surv..

A survey on using domain and contextual knowledge for human activity recognition in video streams

Mykola Pechenizkiy, Giulio Iannello, Leonardo Onofri, 2016, Expert Syst. Appl..

ACLAC: An approach for adaptive closed-loop anesthesia control

Juan A. Méndez, Mykola Pechenizkiy, Alexandr V. Maslov, 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

Teaching Responsible Machine Learning to Engineers

Hilde J. P. Weerts, Mykola Pechenizkiy, 2021, Teaching ML.

International Workshop on Applying Data Mining in e-Learning ( ADML ’ 07 ) as part of the Second European Conference on Technology Enhanced Learning ( EC-TEL 07 )

Toon Calders, Mykola Pechenizkiy, Cristóbal Romero, 2007 .

New Trends in Databases and Information Systems

Marek Wojciechowski, Mykola Pechenizkiy, Mykola Pechenizkiy, 2018, Communications in Computer and Information Science.

Feedback adaptation in web-based learning systems

Mykola Pechenizkiy, Seppo Puuronen, Pekka Räsänen, 2007 .

Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

Mykola Pechenizkiy, George H. L. Fletcher, Giovanni Iacca, 2019, Evolutionary Computation.

Novelty producing synaptic plasticity

G. Fletcher, Mykola Pechenizkiy, Giovanni Iacca, 2020, GECCO Companion.

Learning with delayed synaptic plasticity

Mykola Pechenizkiy, George H. L. Fletcher, Giovanni Iacca, 2019, GECCO.

On the Use of Information Systems Research Methods in Datamining

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2005 .

An Overview of Concept Drift Applications

Mykola Pechenizkiy, João Gama, Indrė Žliobaitė, 2016 .

Introduction to the Special Section on AI in Manufacturing

J. Vanschoren, A. Ypma, Mykola Pechenizkiy, 2022, SIGKDD Explor..

Feedback Loop and Bias Amplification in Recommender Systems

Bamshad Mobasher, Masoud Mansoury, Robin D. Burke, 2020, CIKM.

Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning

Dragan Gasevic, Mykola Pechenizkiy, Dragan Gasevic, 2020, Br. J. Educ. Technol..

Dynamic integration of classifiers for handling concept drift

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2008, Inf. Fusion.

Selfish Sparse RNN Training pdf

Mykola Pechenizkiy, Decebal Constantin Mocanu, Yulong Pei, 2021, ICML.

Knowledge Elicitation using Deep Metric Learning and Psychometric Testing

Vlado Menkovski, Mykola Pechenizkiy, Lu Yin, 2020, IJCAI.

Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions

Mykola Pechenizkiy, Wouter Duivesteijn, Marcos L. P. Bueno, 2021, Data Min. Knowl. Discov..

Predictive Handling of Asynchronous Concept Drifts in Distributed Environments

Mykola Pechenizkiy, Steven C. H. Hoi, Vivekanand Gopalkrishnan, 2013, IEEE Transactions on Knowledge and Data Engineering.

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration

Zhangyang Wang, Mykola Pechenizkiy, D. Mocanu, 2021, NeurIPS.

Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

Mykola Pechenizkiy, Decebal Constantin Mocanu, Lu Yin, 2021, ICML.

Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching

Mykola Pechenizkiy, Natalia Sidorova, Rafal Kocielnik, 2012, IHI '12.

Tink: A Temporal Graph Analytics Library for Apache Flink

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2018, WWW.

Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation pdf

Mykola Pechenizkiy, Bamshad Mobasher, Masoud Mansoury, 2021, ArXiv.

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost

Mykola Pechenizkiy, Lu Yin, Tianjin Huang, 2022, AAAI.

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Zhangyang Wang, Mykola Pechenizkiy, D. Mocanu, 2022, ICLR.

Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

Zhangyang Wang, Mykola Pechenizkiy, D. Mocanu, 2021, ICLR.

Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware

Mykola Pechenizkiy, Yulong Pei, Decebal Constantin Mocanu, 2019, Neural Computing and Applications.

Evaluation of the Sample Clustering Process on Graphs

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2020, IEEE Transactions on Knowledge and Data Engineering.

Multi-strategy Differential Evolution

Mykola Pechenizkiy, George H. L. Fletcher, Giovanni Iacca, 2018, EvoApplications.

Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling

Mykola Pechenizkiy, Wouter Duivesteijn, Xin Du, 2020, Data Mining and Knowledge Discovery.

Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?

Mykola Pechenizkiy, Indre Zliobaite, Jorn Bakker, 2012, Expert Syst. Appl..

Towards Context Aware Food Sales Prediction

Mykola Pechenizkiy, Indre Zliobaite, Jorn Bakker, 2009, 2009 IEEE International Conference on Data Mining Workshops.

Food Wholesales Prediction: What Is Your Baseline?

Mykola Pechenizkiy, Jorn Bakker, Mykola Pechenizkiy, 2009, ISMIS.

Food Sales Prediction: "If Only It Knew What We Know"

Mykola Pechenizkiy, Patrick Meulstee, Mykola Pechenizkiy, 2008, 2008 IEEE International Conference on Data Mining Workshops.

Discrimination Aware Decision Tree Learning

Toon Calders, Mykola Pechenizkiy, Faisal Kamiran, 2010, 2010 IEEE International Conference on Data Mining.

FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

Mykola Pechenizkiy, A. Saxena, Yulong Pei, 2022, ArXiv.

A robust density-based clustering algorithm for multi-manifold structure

Mykola Pechenizkiy, Yulong Pei, Julia Efremova, 2016, SAC.

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning

Matthew E. Taylor, Mykola Pechenizkiy, Shibhansh Dohare, 2023, AAMAS.

Towards more relevance-oriented data mining research

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2008, Intell. Data Anal..

Keynote Paper: Data Mining Researcher, Who is Your Customer? Some Issues Inspired by the Information Systems Field

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2006, 17th International Workshop on Database and Expert Systems Applications (DEXA'06).

Learning with Actionable Attributes: Attention -- Boundary Cases!

Mykola Pechenizkiy, Indre Zliobaite, I. Žliobaitė, 2010, 2010 IEEE International Conference on Data Mining Workshops.

Dealing With Concept Drifts in Process Mining

Mykola Pechenizkiy, Wil M. P. van der Aalst, R. P. Jagadeesh Chandra Bose, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Handling Concept Drift in Process Mining

Mykola Pechenizkiy, Wil M. P. van der Aalst, R. P. Jagadeesh Chandra Bose, 2011, CAiSE.

Towards the Stress Analytics Framework: Managing, Mining, and Visualizing Multi-modal Data for Stress Awareness

Mykola Pechenizkiy, Hindra Kurniawan, Mykola Pechenizkiy, 2014, 2014 IEEE 27th International Symposium on Computer-Based Medical Systems.

Stress detection from speech and Galvanic Skin Response signals

Mykola Pechenizkiy, Alexandr V. Maslov, Hindra Kurniawan, 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

Adaptation of feedback in e-learning systems at individual and group level

Mykola Pechenizkiy, Pme Paul De Bra, Ekaterina Vasilyeva, 2007 .

A Framework for Guiding the Museum Tour Personalization

Tgk Toon Calders, Mykola Pechenizkiy, Mykola Pechenizkiy, 2007 .

A Human-Grounded Evaluation of SHAP for Alert Processing pdf

Mykola Pechenizkiy, Werner van Ipenburg, Hilde J.P. Weerts, 2019, ArXiv.

Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning pdf

Mykola Pechenizkiy, Decebal Constantin Mocanu, Ghada Sokar, 2021, ArXiv.

Context-Aware Personal Route Recognition

Mykola Pechenizkiy, Oleksiy Mazhelis, Indre Zliobaite, 2011, Discovery Science.

Dynamic Sparse Network for Time Series Classification: Learning What to "see"

Mykola Pechenizkiy, D. Mocanu, Yu Zhang, 2022, NeurIPS.

Introduction to The Special Section on Bias and Fairness in AI

Bodo Rosenhahn, Mykola Pechenizkiy, Salvatore Ruggieri, 2021, SIGKDD Explor..

Diversity in search strategies for ensemble feature selection

Mykola Pechenizkiy, Padraig Cunningham, Alexey Tsymbal, 2005, Inf. Fusion.

On the Limitations of Human-Computer Agreement in Automated Essay Scoring

Mykola Pechenizkiy, Afrizal Doewes, 2021, EDM.

Generation of False Data Injection Attacks using Conditional Generative Adversarial Networks

Mykola Pechenizkiy, Mostafa Mohammadpourfard, Subhash Lakshminarayana, 2020, 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe).

Predicting Current User Intent with Contextual Markov Models

Toon Calders, Mykola Pechenizkiy, Hoang Thanh Lam, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Process Mining Online Assessment Data

Mykola Pechenizkiy, Wil M. P. van der Aalst, Wil M.P. van der Aalst, 2009, EDM.

Please Scroll down for Article New Review of Hypermedia and Multimedia Ah 12 Years Later: a Comprehensive Survey of Adaptive Hypermedia Methods and Techniques Ah 12 Years Later: a Comprehensive Survey of Adaptive Hypermedia Methods and Techniques

Mykola Pechenizkiy, Paul De Bra, Evgeny Knutov, 2022 .

EDM 2011: 4th international conference on educational data mining : Eindhoven, July 6-8, 2011 : proceedings

Cristina Conati, Tgk Toon Calders, Mykola Pechenizkiy, 2011, EDM 2011.

Hippocrates: A Context-Aware, Collaboration Enabling Search Tool

Mykola Pechenizkiy, Anca I. D. Bucur, Georgios Aravanis, 2015, 2015 IEEE 28th International Symposium on Computer-Based Medical Systems.

Discovering temporal hidden contexts in web sessions for user trail prediction

Toon Calders, Mykola Pechenizkiy, Hoang Thanh Lam, 2013, WWW.

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Mykola Pechenizkiy, Edward Tersoo Apeh, Bogdan Gabrys, 2012, SGAI Conf..

Bridging Recommendation and Adaptation: Generic Adaptation Framework - Twittomender compliance case-study

John Hannon, Mykola Pechenizkiy, Barry Smyth, 2011, DAH@HT.

Identifying Lead Users and their Insights on Social Media

Mykola Pechenizkiy, S. Gelper, R. Melsen, 2016 .

Application of Horizontal Visibility Graph as a Robust Measure of Neurophysiological Signals Synchrony

Mykola Pechenizkiy, Negar Ahmadi, Mykola Pechenizkiy, 2016, 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS).

Cluster-preserving sampling from fully-dynamic streaming graphs

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2019, Inf. Sci..

Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model

Mykola Pechenizkiy, Yulong Pei, Negar Ahmadi, 2019, Physica A: Statistical Mechanics and its Applications.

Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series

Mykola Pechenizkiy, Negar Ahmadi, Rene M. H. Besseling, 2018, Social Network Analysis and Mining.

Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models pdf

Mykola Pechenizkiy, Werner van Ipenburg, Hilde J.P. Weerts, 2019, ArXiv.

Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming

María José del Jesús, Mykola Pechenizkiy, Sebastián Ventura, 2018, IEEE Transactions on Cybernetics.

Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)

R. M. Schouten, Mykola Pechenizkiy, W. Duivesteijn, 2022, SDM.

A comparative study of dimensionality reduction techniques to enhance trace clustering performances

Mykola Pechenizkiy, Minseok Song, H. Yang, 2013, Expert Syst. Appl..

NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist

Mykola Pechenizkiy, Meng Fang, Iftitahu Ni'mah, 2023, ACL.

Hunting the Unknown - White-Box Database Leakage Detection

Jerry den Hartog, Sandro Etalle, Mykola Pechenizkiy, 2014, DBSec.

Detection of Alcoholism Based on EEG Signals and Functional Brain Network Features Extraction

Mykola Pechenizkiy, Yulong Pei, Negar Ahmadi, 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

Pattern-Based Emotion Classification on Social Media

Mykola Pechenizkiy, Erik Tromp, Mykola Pechenizkiy, 2015, Advances in Social Media Analysis.

Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data

Mykola Pechenizkiy, Sebastián Ventura, José María Luna, 2018, IEEE Transactions on Cybernetics.

Mining exceptional relationships with grammar-guided genetic programming

Mykola Pechenizkiy, Sebastián Ventura, José María Luna, 2015, Knowledge and Information Systems.

Speeding-Up Association Rule Mining With Inverted Index Compression

Mykola Pechenizkiy, Sebastián Ventura, Alberto Cano, 2016, IEEE Transactions on Cybernetics.

Connected lighting system data analytics Master Thesis Connected Lighing System Data Analytics usefulness check of sequential pattern mining for predictive

Mykola Pechenizkiy, 2016 .

Handbook of Educational Data Mining

Mykola Pechenizkiy, Sebastián Ventura, Cristóbal Romero, 2010 .

Learning Invariant Representation for Continual Learning pdf

Decebal Constantin Mocanu, Ghada Sokar, Mykola Pechenizkiy, 2020, ArXiv.

SpaceNet: Make Free Space For Continual Learning pdf

Decebal Constantin Mocanu, Ghada Sokar, Mykola Pechenizkiy, 2020, Neurocomputing.

Feature Extraction for Classification in Knowledge Discovery Systems

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2003, KES.

Topological Insights in Sparse Neural Networks

Mykola Pechenizkiy, David L. Ferraro, D. Mocanu, 2020, ECML/PKDD.

A DSL based on CSS for hypertext adaptation

Mykola Pechenizkiy, George H. L. Fletcher, Paul De Bra, 2014, HT.

Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections

Mykola Pechenizkiy, Padraig Cunningham, Alexey Tsymbal, 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).

Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency

Mykola Pechenizkiy, Yun Sing Koh, Russel Pears, 2021, Data Mining and Knowledge Discovery.

Knowledge Management Challenges in Knowledge Discovery Systems

A. Tsymbal, M. Pechenizkiy, S. Puuronen, 2005, 16th International Workshop on Database and Expert Systems Applications (DEXA'05).

Does Relevance Matter to Data Mining Research?

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2008, Data Mining: Foundations and Practice.

ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection

Mykola Pechenizkiy, Vlado Menkovski, Iftitahu Ni'mah, 2021, EMNLP.

The MOOClet Framework: Improving Online Education through Experimentation and Personalization of Modules

Neil T. Heffernan, Mykola Pechenizkiy, Joseph Jay Williams, 2014 .

Knowledge Discovery from Microbiology Data: Many-Sided Analysis of Antibiotic Resistance in Nosocomial Infections

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2005, Wissensmanagement.

Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree

Henrik Palmer Olsen, Hilde J. P. Weerts, Mykola Pechenizkiy, 2023, FAccT.

Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model

Mykola Pechenizkiy, Alessandro Di Bucchianico, Yingjun Deng, 2020, Reliab. Eng. Syst. Saf..

Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift

Mykola Pechenizkiy, Tommi Kärkkäinen, Indre Zliobaite, 2010, SKDD.

Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers

Mykola Pechenizkiy, D. Mocanu, Zahra Atashgahi, 2023, ArXiv.

Introduction to the special section on educational data mining

Toon Calders, Mykola Pechenizkiy, Mykola Pechenizkiy, 2012, SKDD.

Learning from Medical Data Streams AIME 2011 Workshop Report

Mykola Pechenizkiy, Mohamed Medhat Gaber, Pedro Pereira Rodrigues, 2011 .

A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams

Yun Sing Koh, A. Bifet, Mykola Pechenizkiy, 2022, 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA).

Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification

Mykola Pechenizkiy, Yun Sing Koh, Russel Pears, 2021, 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA).

Ensuring cybersecurity of smart grid against data integrity attacks under concept drift

Yang Weng, Behnam Mohammadi-Ivatloo, Mohsen Tajdinian, 2020, International Journal of Electrical Power & Energy Systems.

BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints

Mykola Pechenizkiy, Tommi Kärkkäinen, Yulong Pei, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Modelling Recurrent Events for Improving Online Change Detection

Mykola Pechenizkiy, Tommi Kärkkäinen, Indre Zliobaite, 2016, SDM.

DOBRO: a prediction error correcting robot under drifts

Eric Bouillet, Mykola Pechenizkiy, Tommi Kärkkäinen, 2016, SAC.

Predictive analytics on evolving data streams anticipating and adapting to changes in known and unknown contexts

Mykola Pechenizkiy, Mykola Pechenizkiy, 2015, 2015 International Conference on High Performance Computing & Simulation (HPCS).

Enhancing Adversarial Training via Reweighting Optimization Trajectory

Mykola Pechenizkiy, Tianlong Chen, Lu Yin, 2023, arXiv.org.

Calibrated Adversarial Training

Mykola Pechenizkiy, Vlado Menkovski, Tianjin Huang, 2021, ArXiv.

Direction-aggregated Attack for Transferable Adversarial Examples pdf

Mykola Pechenizkiy, Yulong Pei, Tianjin Huang, 2021, ACM J. Emerg. Technol. Comput. Syst..

Bridging the Performance Gap between FGSM and PGD Adversarial Training

Mykola Pechenizkiy, Vlado Menkovski, Tianjin Huang, 2020, ArXiv.

Feature Extraction for Dynamic Integration of Classifiers

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2007, Fundam. Informaticae.

Sequential Genetic Search for Ensemble Feature Selection

Mykola Pechenizkiy, Padraig Cunningham, Alexey Tsymbal, 2005, IJCAI.

The Impact of Feature Extraction on the Performance of a Classifier: kNN, Naïve Bayes and C4.5

Mykola Pechenizkiy, Mykola Pechenizkiy, 2005, Canadian Conference on AI.

Dynamic Integration of Classifiers in the Space of Principal Components

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2003, ADBIS.

Search strategies for ensemble feature selection in medical diagnostics

Mykola Pechenizkiy, Padraig Cunningham, Alexey Tsymbal, 2003, 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings..

Eigenvector-Based Feature Extraction for Classification

Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, 2002, FLAIRS.

JYVÄSKYLÄ STUDIES IN COMPUTING 56

Mykola Pechenizkiy, Mykola Pechenizkiy, 2005 .

Feature extraction for classification in the data mining process

Mykola Pechenizkiy, S. Puuronen, Alexey Tsymbal, 2003 .

Are Large Kernels Better Teachers than Transformers for ConvNets?

Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu, 2023, ICML.

Explaining Soccer Match Outcomes with Goal Scoring Opportunities Predictive Analytics

Mykola Pechenizkiy, Harm Eggels, Ruud van Elk, 2016, MLSA@PKDD/ECML.

FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity pdf

Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu, 2021, ArXiv.

FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems

Robin Burke, Bamshad Mobasher, Masoud Mansoury, 2020, UMAP.

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets

Zhangyang Wang, Mykola Pechenizkiy, D. Mocanu, 2022, LoG.

Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks

Mykola Pechenizkiy, D. Mocanu, Zahra Atashgahi, 2023, Trans. Mach. Learn. Res..

Node classification in dynamic social networks

Yulong Pei, Mykola Pechenizkiy, Jianpeng Zhang, 2016 .

Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks

Mykola Pechenizkiy, Tianjin Huang, Yulong Pei, 2021, ECML/PKDD.

HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning

Mykola Pechenizkiy, Sebastián Ventura, Amelia Zafra, 2013, Inf. Sci..

Cross-lingual polarity detection with machine translation

Mykola Pechenizkiy, Erkin Demirtas, Mykola Pechenizkiy, 2013, WISDOM '13.

NodeSim: node similarity based network embedding for diverse link prediction pdf

Mykola Pechenizkiy, Akrati Saxena, George Fletcher, 2021, EPJ Data Science.

Self-Attention Meta-Learner for Continual Learning

Mykola Pechenizkiy, Decebal Constantin Mocanu, Ghada Sokar, 2021, AAMAS.

Clustering Affiliation Inference from Graph Samples

Mykola Pechenizkiy, Kaijie Zhu, Yulong Pei, 2018 .

ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks pdf

Mykola Pechenizkiy, Werner van Ipenburg, Yulong Pei, 2020, Machine Learning.

Adversarial balancing-based representation learning for causal effect inference with observational data pdf

Lei Sun, Mykola Pechenizkiy, Wouter Duivesteijn, 2019, Data Mining and Knowledge Discovery.

The banking transactions dataset and its comparative analysis with scale-free networks

Mykola Pechenizkiy, Akrati Saxena, Werner van Ipenburg, 2021, ASONAM.

Subgraph anomaly detection in financial transaction networks

Werner van Ipenburg, Mykola Pechenizkiy, Yulong Pei, 2020, ICAIF.

Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems

Bamshad Mobasher, Masoud Mansoury, Arman Dehpanah, 2020, FLAIRS.

Efficient and effective training of sparse recurrent neural networks

Mykola Pechenizkiy, Vlado Menkovski, Decebal Constantin Mocanu, 2021, Neural Computing and Applications.

CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models

Yitong Li, Meng Fang, Mykola Pechenizkiy, 2023, Annual Meeting of the Association for Computational Linguistics.

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

Noor H. Awad, Hilde J. P. Weerts, F. Hutter, 2023, J. Artif. Intell. Res..

Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning

Lambèr M. M. Royakkers, Hilde J. P. Weerts, Mykola Pechenizkiy, 2022, ArXiv.

How Fair is Fairness-aware Representative Ranking?

Mykola Pechenizkiy, Akrati Saxena, George Fletcher, 2021, WWW.

How Fair is Fairness-aware Representative Ranking and Methods for Fair Ranking pdf

Mykola Pechenizkiy, Akrati Saxena, George Fletcher, 2021, ArXiv.

Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution

Mykola Pechenizkiy, George H. L. Fletcher, Giovanni Iacca, 2018, GECCO.

A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems pdf

Mykola Pechenizkiy, Bamshad Mobasher, Masoud Mansoury, 2021, ACM Trans. Inf. Syst..

Data Driven Decision Support for Failure Prediction

M. Pechenizkiy, Mykola Pechenizkiy, 2012 .

Techniques for Discrimination-Free Predictive Models

Toon Calders, Mykola Pechenizkiy, Faisal Kamiran, 2013, Discrimination and Privacy in the Information Society.

SentiCorr: Multilingual Sentiment Analysis of Personal Correspondence

Mykola Pechenizkiy, Erik Tromp, Mykola Pechenizkiy, 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

CurriM: Curriculum Mining

Mykola Pechenizkiy, Pedro A. Toledo, Paul De Bra, 2012, EDM.

Causal Discovery from Incomplete Data: A Deep Learning Approach pdf

Vlado Menkovski, Yuhao Wang, Mykola Pechenizkiy, 2020, ArXiv.

Twitter rumour detection in the health domain

Mykola Pechenizkiy, Yulong Pei, Paolo Soda, 2018, Expert Syst. Appl..

Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining

Mykola Pechenizkiy, Wouter Duivesteijn, Tara Farzami, 2017, ECML/PKDD.

Diversity in Random Subspacing Ensembles

Mykola Pechenizkiy, Padraig Cunningham, Alexey Tsymbal, 2004, DaWaK.

LevDoom: A Benchmark for Generalization on Level Difficulty in Reinforcement Learning

Mykola Pechenizkiy, Meng Fang, Tianhong Dai, 2022, 2022 IEEE Conference on Games (CoG).

Defining Adaptation in a Generic Multi Layer Model: CAM: The GRAPPLE Conceptual Adaptation Model

Mykola Pechenizkiy, Maurice Hendrix, Alexandra I. Cristea, 2008, EC-TEL.

Mobile Sentiment Analysis

Mykola Pechenizkiy, Mohamed Medhat Gaber, Erik Tromp, 2012, KES.

Dynamic Sparsity Is Channel-Level Sparsity Learner

Zhangyang Wang, Mykola Pechenizkiy, Xiaolong Ma, 2023, ArXiv.

GRD: A Generative Approach for Interpretable Reward Redistribution in Reinforcement Learning

Ziyan Wang, Jun Wang, Mykola Pechenizkiy, 2023, ArXiv.

Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison pdf

Robin Burke, Bamshad Mobasher, Masoud Mansoury, 2019, RMSE@RecSys.

ELBA: Exceptional Learning Behavior Analysis

Mykola Pechenizkiy, Wouter Duivesteijn, Xin Du, 2018, EDM.

Reducing Dimensionality in Multiple Instance Learning with a Filter Method

Mykola Pechenizkiy, Sebastián Ventura, Amelia Zafra, 2010, HAIS.

On structure preserving sampling and approximate partitioning of graphs

Mykola Pechenizkiy, George H. L. Fletcher, Wouter van Heeswijk, 2016, SAC.

OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers

Mykola Pechenizkiy, Indre Zliobaite, Jorn Bakker, 2009, Discovery Science.

Online Mass Flow Prediction in CFB Boilers

Mykola Pechenizkiy, Tommi Kärkkäinen, Timo Leino, 2009, ICDM.

Handling outliers and concept drift in online mass flow prediction in CFB boilers

Mykola Pechenizkiy, Tommi Kärkkäinen, Indre Zliobaite, 2009, SensorKDD '09.

A white-box anomaly-based framework for database leakage detection

Jerry den Hartog, Sandro Etalle, Mykola Pechenizkiy, 2017, J. Inf. Secur. Appl..

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach

Jun Wang, Mykola Pechenizkiy, Biwei Huang, 2023, NeurIPS.

Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware

Mykola Pechenizkiy, D. Mocanu, Yulong Pei, 2019, Neural Computing and Applications.

KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering

Mykola Pechenizkiy, Samaneh Khoshrou, Iftitahu Ni'mah, 2023, ArXiv.

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Mykola Pechenizkiy, Zhenyu (Allen) Zhang, Lu Yin, 2023, ArXiv.

Fairness-aware fake news mitigation using counter information propagation

Mykola Pechenizkiy, A. Saxena, Cristina Gutiérrez Bierbooms, 2023, Applied Intelligence.

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

Mykola Pechenizkiy, Yulong Pei, Negar Ahmadi, 2020, Brain Informatics.

Feedback Loop and Bias Amplification in Recommender Systems

R. Burke, B. Mobasher, Mykola Pechenizkiy, 2020, CIKM.

Using Cellular Automata for feature construction - preliminary study

P. Kokol, G. Stiglic, M. Pechenizkiy, 2007, 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference.

A bounded-size clustering algorithm on fully-dynamic streaming graphs

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2018, Intell. Data Anal..

Clustering-Structure Representative Sampling from Graph Streams

Mykola Pechenizkiy, George H. L. Fletcher, Yulong Pei, 2017, COMPLEX NETWORKS.

Multi-View Risk Classification for Customer Due Diligence

Juan M. G. Huesca, Werner van Ipenburg, Simon van der Zon, 2020 .

Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective

Mykola Pechenizkiy, Tianlong Chen, Yihua Zhang, 2023, ArXiv.

Versioning in adaptive hypermedia

Mykola Pechenizkiy, P.M.E. De Bra, Evgeny Knutov, 2009 .

Proceedings of the First International Workshop on Dynamic and Adaptive Hypertext : Generic Frameworks , Approaches and Techniques ( DAH ’ 09 )

P. D. Bra, Mykola Pechenizkiy, 2009 .

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

Mykola Pechenizkiy, M. V. Keulen, D. Mocanu, 2023, ArXiv.

The Impact of Batch Learning in Stochastic Linear Bandits

M. Kaptein, Mykola Pechenizkiy, Pratik Gajane, 2022, 2022 IEEE International Conference on Data Mining (ICDM).

The Impact of Batch Learning in Stochastic Bandits pdf

Mykola Pechenizkiy, Pratik Gajane, Danil Provodin, 2021, ArXiv.

Quick and Robust Feature Selection: the Strength of Energy-eﬃcient Sparse Training for Autoencoders (Extended Abstract

Mykola Pechenizkiy, Zahra Atashgahi, Ghada Sokar, 2021 .

A holistic framework for understanding acceptance of Remote Patient Management (RPM) systems by non-professional users

Mykola Pechenizkiy, A. Tesanovic, S. Puuronen, 2010, 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS).

Towards Proximity Tracking and Sensemaking for Supporting Teamwork and Learning

Mykola Pechenizkiy, Olga C. Santos, Kalina Yacef, 2017, 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT).

How to Capitalise on Mobility, Proximity and Motion Analytics to Support Formal and Informal Education?

Mykola Pechenizkiy, Kalina Yacef, Roberto Martínez Maldonado, 2017, MMLA-CrossLAK@LAK.

FairSNA: Algorithmic Fairness in Social Network Analysis

Mykola Pechenizkiy, A. Saxena, George Fletcher, 2022, ACM Comput. Surv..

How Fair is Fairness-aware Representative Ranking?

G. Fletcher, Mykola Pechenizkiy, A. Saxena, 2021, WWW.

Enhancing Adversarial Training via Reweighting Optimization Trajectory

Mykola Pechenizkiy, Tianlong Chen, Lu Yin, 2023, ECML/PKDD.

Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning

Mykola Pechenizkiy, D. Mocanu, Ghada Sokar, 2021, ArXiv.

On improving deep learning generalization with adaptive sparse connectivity pdf

Decebal Constantin Mocanu, Mykola Pechenizkiy, Shiwei Liu, 2019, ArXiv.

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

G. Fletcher, Mykola Pechenizkiy, Giovanni Iacca, 2019, Evolutionary Computation.

The Challenge of Feedback Personalization to Learning Styles in a Web-Based Learning System

Mykola Pechenizkiy, Seppo Puuronen, Ekaterina Vasilyeva, 2006, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06).

Large Language Models Are Neurosymbolic Reasoners

Mykola Pechenizkiy, Zijing Shi, Yudi Zhang, 2024, AAAI.

Unveiling the Power of Sparse Neural Networks for Feature Selection

M. Schaar, Mykola Pechenizkiy, D. Mocanu, 2024, ArXiv.

VANET Meets Deep Learning: The Effect of Packet Loss on the Object Detection Performance

Ivan Wang-Hei Ho, Yuhao Wang, Vlado Menkovski, 2019, 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring).