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Mykola Pechenizkiy
Person information
- affiliation: Eindhoven University of Technology, Netherlands
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2020 – today
- 2024
- [j65]Akrati Saxena
, George Fletcher
, Mykola Pechenizkiy
:
FairSNA: Algorithmic Fairness in Social Network Analysis. ACM Comput. Surv. 56(8): 213:1-213:45 (2024) - [j64]Ricky Maulana Fajri
, Akrati Saxena
, Yulong Pei
, Mykola Pechenizkiy
:
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering. Expert Syst. Appl. 242: 122842 (2024) - [j63]Hilde J. P. Weerts
, Florian Pfisterer
, Matthias Feurer
, Katharina Eggensperger
, Edward Bergman
, Noor H. Awad
, Joaquin Vanschoren
, Mykola Pechenizkiy
, Bernd Bischl
, Frank Hutter
:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. 79: 639-677 (2024) - [c185]Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang:
Large Language Models Are Neurosymbolic Reasoners. AAAI 2024: 17985-17993 - [c184]Jiaxu Zhao, Zijing Shi, Yitong Li, Yulong Pei, Ling Chen, Meng Fang, Mykola Pechenizkiy:
More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation. ACL (Findings) 2024: 9987-10001 - [c183]Kaiting Liu, Zahra Atashgahi, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks. AISTATS 2024: 3952-3960 - [c182]Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. AAMAS 2024: 733-742 - [c181]Yucheng Yang, Tianyi Zhou, Lei Han, Meng Fang, Mykola Pechenizkiy:
Automatic Curriculum for Unsupervised Reinforcement Learning. AAMAS 2024: 2002-2010 - [c180]Hilde J. P. Weerts
, Raphaële Xenidis
, Fabien Tarissan
, Henrik Palmer Olsen
, Mykola Pechenizkiy
:
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action. FAccT 2024: 2060-2070 - [c179]Yucheng Yang, Tianyi Zhou, Qiang He, Lei Han, Mykola Pechenizkiy, Meng Fang:
Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning. ICLR 2024 - [c178]Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy:
A Structural-Clustering Based Active Learning for Graph Neural Networks. IDA (1) 2024: 28-40 - [i95]Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang:
Large Language Models Are Neurosymbolic Reasoners. CoRR abs/2401.09334 (2024) - [i94]Igor G. Smit, Zaharah Allah Bukhsh
, Mykola Pechenizkiy
, Kostas Alogariastos, Kasper Hendriks, Yingqian Zhang
:
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking. CoRR abs/2404.08006 (2024) - [i93]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action. CoRR abs/2404.12143 (2024) - [i92]Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy:
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling. CoRR abs/2405.19017 (2024) - [i91]Tim D'Hondt, Mykola Pechenizkiy, Robert Peharz:
One-Shot Federated Learning with Bayesian Pseudocoresets. CoRR abs/2406.02177 (2024) - [i90]Calarina Muslimani, Bram Grooten, Deepak Ranganatha Sastry Mamillapalli, Mykola Pechenizkiy, Decebal Constantin Mocanu, Matthew E. Taylor:
Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity. CoRR abs/2406.06495 (2024) - [i89]Qiao Xiao, Pingchuan Ma, Adriana Fernandez-Lopez, Boqian Wu, Lu Yin, Stavros Petridis, Mykola Pechenizkiy, Maja Pantic, Decebal Constantin Mocanu, Shiwei Liu:
Dynamic Data Pruning for Automatic Speech Recognition. CoRR abs/2406.18373 (2024) - 2023
- [j62]Akrati Saxena
, Cristina Gutiérrez Bierbooms, Mykola Pechenizkiy:
Fairness-aware fake news mitigation using counter information propagation. Appl. Intell. 53(22): 27483-27504 (2023) - [j61]Syed Ihtesham Hussain Shah
, Muddasar Naeem
, Giovanni Paragliola
, Antonio Coronato
, Mykola Pechenizkiy
:
An AI-empowered infrastructure for risk prevention during medical examination. Expert Syst. Appl. 225: 120048 (2023) - [j60]Ben Halstead
, Yun Sing Koh
, Patricia Riddle
, Mykola Pechenizkiy
, Albert Bifet
:
Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams. ACM Trans. Knowl. Discov. Data 17(8): 107:1-107:36 (2023) - [j59]Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c177]Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy:
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. AAAI 2023: 10945-10953 - [c176]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist. ACL (1) 2023: 1240-1266 - [c175]Jiaxu Zhao, Meng Fang, Zijing Shi, Yitong Li, Ling Chen, Mykola Pechenizkiy:
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models. ACL (1) 2023: 13538-13556 - [c174]Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. AAMAS 2023: 1932-1941 - [c173]Sheetal Borar
, Hilde J. P. Weerts
, Binyam Gebre
, Mykola Pechenizkiy
:
Improving Recommender System Diversity with Variational Autoencoders. BIAS 2023: 85-99 - [c172]Zirui Liang, Yuntao Li, Tianjin Huang, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy:
Heterophily-Based Graph Neural Network for Imbalanced Classification. COMPLEX NETWORKS (1) 2023: 74-86 - [c171]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
Algorithmic Unfairness Through the Lens of EU Non-Discrimination Law. EWAF 2023 - [c170]Hilde J. P. Weerts
, Raphaële Xenidis
, Fabien Tarissan
, Henrik Palmer Olsen
, Mykola Pechenizkiy
:
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree. FAccT 2023: 805-816 - [c169]Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet:
FALL: A Modular Adaptive Learning Platform for Streaming Data. ICDE 2023: 3619-3622 - [c168]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi Kärkkäinen, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang:
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. ICLR 2023 - [c167]Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu:
Are Large Kernels Better Teachers than Transformers for ConvNets? ICML 2023: 14023-14038 - [c166]Dennis Collaris
, Pratik Gajane
, Joost Jorritsma
, Jarke J. van Wijk
, Mykola Pechenizkiy
:
LEMON: Alternative Sampling for More Faithful Explanation Through Local Surrogate Models. IDA 2023: 77-90 - [c165]Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu:
Dynamic Sparsity Is Channel-Level Sparsity Learner. NeurIPS 2023 - [c164]Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy:
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach. NeurIPS 2023 - [c163]Tristan Tomilin, Meng Fang, Yudi Zhang, Mykola Pechenizkiy:
COOM: A Game Benchmark for Continual Reinforcement Learning. NeurIPS 2023 - [c162]Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy:
Enhancing Adversarial Training via Reweighting Optimization Trajectory. ECML/PKDD (1) 2023: 113-130 - [c161]Jiaxu Zhao, Lu Yin, Shiwei Liu, Meng Fang, Mykola Pechenizkiy:
REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training. ECML/PKDD (2) 2023: 313-329 - [e10]Mingyu Feng, Tanja Käser, Partha P. Talukdar, Rakesh Agrawal, Y. Narahari, Mykola Pechenizkiy:
Proceedings of the 16th International Conference on Educational Data Mining, EDM 2023, Bengaluru, India, July 11-14, 2023. International Educational Data Mining Society 2023 [contents] - [i88]Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu
, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. CoRR abs/2302.06548 (2023) - [i87]Zahra Atashgahi, Xuhao Zhang, Neil Kichler
, Shiwei Liu, Lu Yin, Mykola Pechenizkiy
, Raymond N. J. Veldhuis, Decebal Constantin Mocanu
:
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. CoRR abs/2303.07200 (2023) - [i86]Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR abs/2303.08485 (2023) - [i85]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist. CoRR abs/2305.08566 (2023) - [i84]Jiaxu Zhao, Meng Fang, Zijing Shi, Yitong Li, Ling Chen, Mykola Pechenizkiy:
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models. CoRR abs/2305.11262 (2023) - [i83]Hilde J. P. Weerts, Raphaële Xenidis, Fabien Tarissan, Henrik Palmer Olsen, Mykola Pechenizkiy:
Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree. CoRR abs/2305.13938 (2023) - [i82]Zahra Atashgahi, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers. CoRR abs/2305.18382 (2023) - [i81]Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy:
GRD: A Generative Approach for Interpretable Reward Redistribution in Reinforcement Learning. CoRR abs/2305.18427 (2023) - [i80]Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu:
Are Large Kernels Better Teachers than Transformers for ConvNets? CoRR abs/2305.19412 (2023) - [i79]Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu:
Dynamic Sparsity Is Channel-Level Sparsity Learner. CoRR abs/2305.19454 (2023) - [i78]Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy:
Enhancing Adversarial Training via Reweighting Optimization Trajectory. CoRR abs/2306.14275 (2023) - [i77]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
Provably Efficient Exploration in Constrained Reinforcement Learning: Posterior Sampling Is All You Need. CoRR abs/2309.15737 (2023) - [i76]Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, Shiwei Liu:
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. CoRR abs/2310.05175 (2023) - [i75]Zirui Liang, Yuntao Li, Tianjin Huang, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy:
Heterophily-Based Graph Neural Network for Imbalanced Classification. CoRR abs/2310.08725 (2023) - [i74]Iftitahu Ni'mah, Samaneh Khoshrou, Vlado Menkovski, Mykola Pechenizkiy:
KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering. CoRR abs/2310.19650 (2023) - [i73]Can Jin, Tianjin Huang, Yihua Zhang, Mykola Pechenizkiy, Sijia Liu, Shiwei Liu, Tianlong Chen:
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective. CoRR abs/2312.01397 (2023) - [i72]Jiaxu Zhao, Lu Yin, Shiwei Liu, Meng Fang, Mykola Pechenizkiy:
REST: Enhancing Group Robustness in DNNs through Reweighted Sparse Training. CoRR abs/2312.03044 (2023) - [i71]Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy:
A Structural-Clustering Based Active Learning for Graph Neural Networks. CoRR abs/2312.04307 (2023) - [i70]Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu
:
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. CoRR abs/2312.04727 (2023) - [i69]Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin, Mykola Pechenizkiy:
GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models. CoRR abs/2312.06315 (2023) - [i68]Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. CoRR abs/2312.15339 (2023) - 2022
- [j58]Rianne Margaretha Schouten
, Marcos L. P. Bueno, Wouter Duivesteijn, Mykola Pechenizkiy
:
Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions. Data Min. Knowl. Discov. 36(1): 379-413 (2022) - [j57]Akrati Saxena
, George Fletcher, Mykola Pechenizkiy
:
NodeSim: node similarity based network embedding for diverse link prediction. EPJ Data Sci. 11(1): 24 (2022) - [j56]Fang Lv
, Wei Wang, Linxuan Han, Di Wang, Yulong Pei, Junheng Huang, Bailing Wang, Mykola Pechenizkiy
:
Mining trading patterns of pyramid schemes from financial time series data. Future Gener. Comput. Syst. 134: 388-398 (2022) - [j55]Akrati Saxena
, George Fletcher, Mykola Pechenizkiy
:
HM-EIICT: Fairness-aware link prediction in complex networks using community information. J. Comb. Optim. 44(4): 2853-2870 (2022) - [j54]Tianjin Huang
, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy
:
Direction-aggregated Attack for Transferable Adversarial Examples. ACM J. Emerg. Technol. Comput. Syst. 18(3): 60:1-60:22 (2022) - [j53]Zahra Atashgahi
, Ghada Sokar, Tim van der Lee, Elena Mocanu
, Decebal Constantin Mocanu
, Raymond N. J. Veldhuis, Mykola Pechenizkiy
:
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. Mach. Learn. 111(1): 377-414 (2022) - [j52]Yulong Pei
, Tianjin Huang
, Werner van Ipenburg, Mykola Pechenizkiy
:
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks. Mach. Learn. 111(2): 519-541 (2022) - [j51]Ben Halstead
, Yun Sing Koh
, Patricia Riddle, Russel Pears, Mykola Pechenizkiy
, Albert Bifet
, Gustavo Olivares, Guy Coulson:
Analyzing and repairing concept drift adaptation in data stream classification. Mach. Learn. 111(10): 3489-3523 (2022) - [j50]Zahra Atashgahi
, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu
, Raymond N. J. Veldhuis, Mykola Pechenizkiy:
A brain-inspired algorithm for training highly sparse neural networks. Mach. Learn. 111(12): 4411-4452 (2022) - [j49]Jefrey Lijffijt, Dimitra Gkorou, Pieter Van Hertum, Alexander Ypma, Mykola Pechenizkiy
, Joaquin Vanschoren
:
Introduction to the Special Section on AI in Manufacturing: Current Trends and Challenges. SIGKDD Explor. 24(2): 81-85 (2022) - [j48]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy
, Bamshad Mobasher
, Robin Burke:
A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems. ACM Trans. Inf. Syst. 40(2): 32:1-32:31 (2022) - [c160]Tristan Tomilin, Tianhong Dai
, Meng Fang, Mykola Pechenizkiy:
LevDoom: A Benchmark for Generalization on Level Difficulty in Reinforcement Learning. CoG 2022: 72-79 - [c159]Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet
:
A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams. DSAA 2022: 1-10 - [c158]Afrizal Doewes, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy:
Individual Fairness Evaluation for Automated Essay Scoring System. EDM 2022 - [c157]Collin F. Lynch, Mirko Marras, Mykola Pechenizkiy, Anna N. Rafferty, Steven Ritter, Vinitra Swamy, Renzhe Yu:
FATED 2022: Fairness, Accountability, and Transparency in Educational Data. EDM 2022 - [c156]Danil Provodin
, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Linear Bandits. ICDM 2022: 1149-1154 - [c155]Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. ICLR 2022 - [c154]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy:
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. ICLR 2022 - [c153]Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Semantic-Based Few-Shot Classification by Psychometric Learning. IDA 2022: 392-403 - [c152]Ghada Sokar, Elena Mocanu
, Decebal Constantin Mocanu
, Mykola Pechenizkiy, Peter Stone:
Dynamic Sparse Training for Deep Reinforcement Learning. IJCAI 2022: 3437-3443 - [c151]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. LoG 2022: 8 - [c150]Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy:
Phrase-level Textual Adversarial Attack with Label Preservation. NAACL-HLT (Findings) 2022: 1095-1112 - [c149]Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? NeurIPS 2022 - [c148]Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu:
Dynamic Sparse Network for Time Series Classification: Learning What to "See". NeurIPS 2022 - [c147]Dennis Collaris
, Hilde J. P. Weerts
, Daphne Miedema
, Jarke J. van Wijk, Mykola Pechenizkiy
:
Characterizing Data Scientists' Mental Models of Local Feature Importance. NordiCHI 2022: 9:1-9:12 - [c146]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks. ECML/PKDD (3) 2022: 85-101 - [c145]Tianjin Huang
, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy
:
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks. ECML/PKDD (1) 2022: 225-241 - [c144]Rianne Margaretha Schouten, Wouter Duivesteijn, Mykola Pechenizkiy:
Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). SDM 2022: 585-593 - [c143]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy:
Superposing many tickets into one: A performance booster for sparse neural network training. UAI 2022: 2267-2277 - [i67]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy:
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. CoRR abs/2202.02643 (2022) - [i66]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Linear Bandits. CoRR abs/2202.06657 (2022) - [i65]Hilde J. P. Weerts
, Lambèr Royakkers, Mykola Pechenizkiy
:
Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning. CoRR abs/2202.08536 (2022) - [i64]Pratik Gajane
, Akrati Saxena
, Maryam Tavakol, George Fletcher, Mykola Pechenizkiy:
Survey on Fair Reinforcement Learning: Theory and Practice. CoRR abs/2205.10032 (2022) - [i63]Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou
, Meng Fang, Mykola Pechenizkiy:
Phrase-level Textual Adversarial Attack with Label Preservation. CoRR abs/2205.10710 (2022) - [i62]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu:
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. CoRR abs/2205.15322 (2022) - [i61]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang:
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. CoRR abs/2207.03620 (2022) - [i60]Zahra Atashgahi, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy:
Memory-free Online Change-point Detection: A Novel Neural Network Approach. CoRR abs/2207.03932 (2022) - [i59]Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy:
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. CoRR abs/2208.10842 (2022) - [i58]Akrati Saxena
, George Fletcher, Mykola Pechenizkiy:
FairSNA: Algorithmic Fairness in Social Network Analysis. CoRR abs/2209.01678 (2022) - [i57]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning. CoRR abs/2209.03596 (2022) - [i56]Ricky Maulana Fajri, Akrati Saxena
, Yulong Pei, Mykola Pechenizkiy:
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering. CoRR abs/2209.12756 (2022) - [i55]Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? CoRR abs/2211.14627 (2022) - [i54]Tianjin Huang
, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu
, Zhangyang Wang, Mykola Pechenizkiy
, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. CoRR abs/2211.15335 (2022) - [i53]Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu
, Decebal Constantin Mocanu:
Dynamic Sparse Network for Time Series Classification: Learning What to "see". CoRR abs/2212.09840 (2022) - 2021
- [j47]Ben Halstead
, Yun Sing Koh
, Patricia Riddle, Russel Pears, Mykola Pechenizkiy
, Albert Bifet
:
Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency. Data Min. Knowl. Discov. 35(3): 796-836 (2021) - [j46]Xin Du
, Lei Sun, Wouter Duivesteijn, Alexander G. Nikolaev, Mykola Pechenizkiy
:
Adversarial balancing-based representation learning for causal effect inference with observational data. Data Min. Knowl. Discov. 35(4): 1713-1738 (2021) - [j45]Anil Yaman
, Giovanni Iacca
, Decebal Constantin Mocanu
, Matt Coler
, George Fletcher, Mykola Pechenizkiy
:
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. Evol. Comput. 29(3): 391-414 (2021) - [j44]Ghada Sokar, Decebal Constantin Mocanu
, Mykola Pechenizkiy
:
SpaceNet: Make Free Space for Continual Learning. Neurocomputing 439: 1-11 (2021) - [j43]Shiwei Liu, Decebal Constantin Mocanu
, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei, Mykola Pechenizkiy
:
Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. Neural Comput. Appl. 33(7): 2589-2604 (2021) - [j42]Shiwei Liu
, Iftitahu Ni'mah, Vlado Menkovski
, Decebal Constantin Mocanu
, Mykola Pechenizkiy
:
Efficient and effective training of sparse recurrent neural networks. Neural Comput. Appl. 33(15): 9625-9636 (2021) - [j41]Toon Calders, Eirini Ntoutsi, Mykola Pechenizkiy
, Bodo Rosenhahn, Salvatore Ruggieri:
Introduction to The Special Section on Bias and Fairness in AI. SIGKDD Explor. 23(1): 1-3 (2021) - [c142]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
calibrated adversarial training. ACML 2021: 626-641 - [c141]Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy:
Hierarchical Semantic Segmentation using Psychometric Learning. ACML 2021: 798-813 - [c140]Akrati Saxena
, Yulong Pei, Jan Veldsink, Werner van Ipenburg, George Fletcher, Mykola Pechenizkiy:
The banking transactions dataset and its comparative analysis with scale-free networks. ASONAM 2021: 283-296 - [c139]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Self-Attention Meta-Learner for Continual Learning. AAMAS 2021: 1658-1660 - [c138]Ben Halstead, Yun Sing Koh, Patricia Riddle, Russel Pears, Mykola Pechenizkiy, Albert Bifet
, Gustavo Olivares, Guy Coulson:
Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification. DSAA 2021: 1-2 - [c137]Yulong Pei, Tianjin Huang
, Werner van Ipenburg, Mykola Pechenizkiy:
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks. DSAA 2021: 1-2 - [c136]Afrizal Doewes, Mykola Pechenizkiy:
On the Limitations of Human-Computer Agreement in Automated Essay Scoring. EDM 2021 - [c135]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection. EMNLP (Findings) 2021: 1606-1617 - [c134]Ben Halstead
, Yun Sing Koh
, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet
, Russel Pears:
Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information. ICDE 2021: 1056-1067 - [c133]Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy:
Selfish Sparse RNN Training. ICML 2021: 6893-6904 - [c132]Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. ICML 2021: 6989-7000 - [c131]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. NeurIPS 2021: 9908-9922 - [c130]Tianjin Huang
, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. ECML/PKDD (2) 2021: 367-382 - [c129]Hilde Jacoba Petronella Weerts, Mykola Pechenizkiy:
Teaching Responsible Machine Learning to Engineers. Teaching ML 2021: 40-45 - [c128]Akrati Saxena
, George Fletcher, Mykola Pechenizkiy:
How Fair is Fairness-aware Representative Ranking? WWW (Companion Volume) 2021: 161-165 - [e9]Michael Kamp
, Irena Koprinska
, Adrien Bibal
, Tassadit Bouadi
, Benoît Frénay
, Luis Galárraga
, José Oramas
, Linara Adilova, Yamuna Krishnamurthy
, Bo Kang
, Christine Largeron, Jefrey Lijffijt
, Tiphaine Viard, Pascal Welke
, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele
, Franz Pernkopf
, Michaela Blott
, Holger Fröning
, Günther Schindler, Riccardo Guidotti
, Anna Monreale
, Salvatore Rinzivillo
, Przemyslaw Biecek
, Eirini Ntoutsi
, Mykola Pechenizkiy
, Bodo Rosenhahn
, Christopher L. Buckley
, Daniela Cialfi
, Pablo Lanillos
, Maxwell Ramstead
, Tim Verbelen
, Pedro M. Ferreira
, Giuseppina Andresini
, Donato Malerba
, Ibéria Medeiros
, Philippe Fournier-Viger
, M. Saqib Nawaz
, Sebastián Ventura
, Meng Sun
, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo
, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama
, Ricard Gavaldà
, Lee A. D. Cooper
, Naghmeh Ghazaleh
, Jonas Richiardi
, Damian Roqueiro
, Diego Saldana Miranda
, Konstantinos Sechidis
, Guilherme Graça
:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e8]Michael Kamp
, Irena Koprinska
, Adrien Bibal
, Tassadit Bouadi
, Benoît Frénay
, Luis Galárraga
, José Oramas
, Linara Adilova, Yamuna Krishnamurthy
, Bo Kang
, Christine Largeron, Jefrey Lijffijt
, Tiphaine Viard, Pascal Welke
, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele
, Franz Pernkopf
, Michaela Blott
, Holger Fröning
, Günther Schindler, Riccardo Guidotti
, Anna Monreale
, Salvatore Rinzivillo
, Przemyslaw Biecek
, Eirini Ntoutsi
, Mykola Pechenizkiy
, Bodo Rosenhahn
, Christopher L. Buckley
, Daniela Cialfi
, Pablo Lanillos
, Maxwell Ramstead
, Tim Verbelen
, Pedro M. Ferreira
, Giuseppina Andresini
, Donato Malerba
, Ibéria Medeiros
, Philippe Fournier-Viger
, M. Saqib Nawaz
, Sebastián Ventura
, Meng Sun
, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo
, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama
, Ricard Gavaldà
, Lee A. D. Cooper
, Naghmeh Ghazaleh
, Jonas Richiardi
, Damian Roqueiro
, Diego Saldana Miranda
, Konstantinos Sechidis
, Guilherme Graça
:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents] - [i52]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Learning Invariant Representation for Continual Learning. CoRR abs/2101.06162 (2021) - [i51]Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy:
Selfish Sparse RNN Training. CoRR abs/2101.09048 (2021) - [i50]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Self-Attention Meta-Learner for Continual Learning. CoRR abs/2101.12136 (2021) - [i49]Akrati Saxena, George Fletcher, Mykola Pechenizkiy:
NodeSim: Node Similarity based Network Embedding for Diverse Link Prediction. CoRR abs/2102.00785 (2021) - [i48]Selima Curci, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Truly Sparse Neural Networks at Scale. CoRR abs/2102.01732 (2021) - [i47]Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. CoRR abs/2102.02887 (2021) - [i46]Akrati Saxena, George Fletcher, Mykola Pechenizkiy:
How Fair is Fairness-aware Representative Ranking and Methods for Fair Ranking. CoRR abs/2103.01335 (2021) - [i45]Tianjin Huang
, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy
:
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks. CoRR abs/2104.07917 (2021) - [i44]Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy:
Direction-Aggregated Attack for Transferable Adversarial Examples. CoRR abs/2104.09172 (2021) - [i43]Ghada Sokar, Elena Mocanu
, Decebal Constantin Mocanu
, Mykola Pechenizkiy, Peter Stone:
Dynamic Sparse Training for Deep Reinforcement Learning. CoRR abs/2106.04217 (2021) - [i42]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. CoRR abs/2106.10404 (2021) - [i41]Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu
, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu
:
FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. CoRR abs/2106.14568 (2021) - [i40]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. CoRR abs/2107.02658 (2021) - [i39]Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy:
Hierarchical Semantic Segmentation using Psychometric Learning. CoRR abs/2107.03212 (2021) - [i38]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems. CoRR abs/2107.03415 (2021) - [i37]Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher, Mykola Pechenizkiy, Robin Burke, Milad Sabouri:
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation. CoRR abs/2108.03440 (2021) - [i36]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection. CoRR abs/2108.12229 (2021) - [i35]Xin Du, Subramanian Ramamoorthy, Wouter Duivesteijn, Jin Tian, Mykola Pechenizkiy:
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles. CoRR abs/2109.10432 (2021) - [i34]Akrati Saxena, Yulong Pei, Jan Veldsink, Werner van Ipenburg, George Fletcher, Mykola Pechenizkiy:
The Banking Transactions Dataset and its Comparative Analysis with Scale-free Networks. CoRR abs/2109.10703 (2021) - [i33]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Calibrated Adversarial Training. CoRR abs/2110.00623 (2021) - [i32]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning. CoRR abs/2110.05329 (2021) - [i31]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Bandits. CoRR abs/2111.02071 (2021) - [i30]Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing. CoRR abs/2112.09201 (2021) - 2020
- [j40]José María Luna, Mykola Pechenizkiy
, Wouter Duivesteijn
, Sebastián Ventura
:
Exceptional in so Many Ways - Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios. IEEE Access 8: 200982-200994 (2020) - [j39]Sanna Järvelä, Dragan Gasevic, Tapio Seppänen, Mykola Pechenizkiy
, Paul A. Kirschner:
Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning. Br. J. Educ. Technol. 51(6): 2391-2406 (2020) - [j38]Negar Ahmadi, Yulong Pei, Evelien Carrette
, Albert P. Aldenkamp, Mykola Pechenizkiy
:
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features. Brain Informatics 7(1): 6 (2020) - [j37]Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy
:
struc2gauss: Structural role preserving network embedding via Gaussian embedding. Data Min. Knowl. Discov. 34(4): 1072-1103 (2020) - [j36]Xin Du
, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy
:
Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling. Data Min. Knowl. Discov. 34(5): 1267-1290 (2020) - [j35]Yingjun Deng
, Alessandro Di Bucchianico
, Mykola Pechenizkiy
:
Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model. Reliab. Eng. Syst. Saf. 196: 106727 (2020) - [j34]Jianpeng Zhang
, Yulong Pei, George Fletcher
, Mykola Pechenizkiy
:
Evaluation of the Sample Clustering Process on Graphs. IEEE Trans. Knowl. Data Eng. 32(7): 1333-1347 (2020) - [c127]Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy
:
Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data. AAAI 2020: 3809-3816 - [c126]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher
, Robin Burke:
Feedback Loop and Bias Amplification in Recommender Systems. CIKM 2020: 2145-2148 - [c125]Afrizal Doewes, Mykola Pechenizkiy:
Structural Explanation of Automated Essay Scoring. EDM 2020 - [c124]Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher:
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. FLAIRS 2020: 193-196 - [c123]Anil Yaman
, Giovanni Iacca
, Decebal Constantin Mocanu
, George Fletcher, Mykola Pechenizkiy
:
Novelty producing synaptic plasticity. GECCO Companion 2020: 93-94 - [c122]Yulong Pei, Fang Lyu, Werner van Ipenburg, Mykola Pechenizkiy:
Subgraph anomaly detection in financial transaction networks. ICAIF 2020: 18:1-18:8 - [c121]Mostafa Mohammadpourfard, Fateme Ghanaatpishe, Marziyeh Mohammadi, Subhash Lakshminarayana, Mykola Pechenizkiy:
Generation of False Data Injection Attacks using Conditional Generative Adversarial Networks. ISGT-Europe 2020: 41-45 - [c120]Ricky Maulana Fajri, Samaneh Khoshrou, Robert Peharz, Mykola Pechenizkiy:
PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data. ECML/PKDD (5) 2020: 68-84 - [c119]Lu Yin, Vlado Menkovski, Mykola Pechenizkiy:
Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing. ECML/PKDD (2) 2020: 154-169 - [c118]Shiwei Liu, Tim van der Lee, Anil Yaman
, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy
, Decebal Constantin Mocanu
:
Topological Insights into Sparse Neural Networks. ECML/PKDD (3) 2020: 279-294 - [c117]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher
, Robin Burke:
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems. UMAP 2020: 154-162 - [i29]Yuhao Wang, Vlado Menkovski, Hao Wang, Xin Du, Mykola Pechenizkiy:
Causal Discovery from Incomplete Data: A Deep Learning Approach. CoRR abs/2001.05343 (2020) - [i28]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George H. L. Fletcher, Mykola Pechenizkiy:
Novelty Producing Synaptic Plasticity. CoRR abs/2002.03620 (2020) - [i27]Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher:
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. CoRR abs/2002.07786 (2020) - [i26]Lu Yin, Vlado Menkovski, Mykola Pechenizkiy:
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing. CoRR abs/2004.06353 (2020) - [i25]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems. CoRR abs/2005.01148 (2020) - [i24]Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar
, Mykola Pechenizkiy, Decebal Constantin Mocanu
:
Topological Insights in Sparse Neural Networks. CoRR abs/2006.14085 (2020) - [i23]Ghada Sokar
, Decebal Constantin Mocanu
, Mykola Pechenizkiy
:
SpaceNet: Make Free Space For Continual Learning. CoRR abs/2007.07617 (2020) - [i22]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
Feedback Loop and Bias Amplification in Recommender Systems. CoRR abs/2007.13019 (2020) - [i21]Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy:
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks. CoRR abs/2009.14738 (2020) - [i20]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Bridging the Performance Gap between FGSM and PGD Adversarial Training. CoRR abs/2011.05157 (2020) - [i19]Sahithya Ravi, Samaneh Khoshrou, Mykola Pechenizkiy:
ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in COVID-19 Streamline Diagnostic. CoRR abs/2011.14871 (2020) - [i18]Zahra Atashgahi, Ghada Sokar, Tim van der Lee, Elena Mocanu
, Decebal Constantin Mocanu
, Raymond N. J. Veldhuis, Mykola Pechenizkiy
:
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders. CoRR abs/2012.00560 (2020)
2010 – 2019
- 2019
- [j33]Jianpeng Zhang, Kaijie Zhu, Yulong Pei, George H. L. Fletcher, Mykola Pechenizkiy
:
Cluster-preserving sampling from fully-dynamic streaming graphs. Inf. Sci. 482: 279-300 (2019) - [c116]Yulong Pei, George H. L. Fletcher, Mykola Pechenizkiy
:
Joint role and community detection in networks via L2, 1 norm regularized nonnegative matrix tri-factorization. ASONAM 2019: 168-175 - [c115]Yulong Pei, Jianpeng Zhang, George H. L. Fletcher, Mykola Pechenizkiy
:
Infinite motif stochastic blockmodel for role discovery in networks. ASONAM 2019: 456-459 - [c114]Samaneh Khoshrou, Mykola Pechenizkiy
:
Adaptive Long-Term Ensemble Learning from Multiple High-Dimensional Time-Series. DS 2019: 511-521 - [c113]Anil Yaman
, Giovanni Iacca
, Decebal Constantin Mocanu
, George H. L. Fletcher, Mykola Pechenizkiy
:
Learning with delayed synaptic plasticity. GECCO 2019: 152-160 - [c112]Emilia Oikarinen
, Kai Puolamäki
, Samaneh Khoshrou
, Mykola Pechenizkiy
:
Supervised Human-Guided Data Exploration. PKDD/ECML Workshops (1) 2019: 85-101 - [c111]Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy:
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison. RMSE@RecSys 2019 - [c110]Yuhao Wang, Vlado Menkovski
, Ivan Wang Hei Ho
, Mykola Pechenizkiy
:
VANET Meets Deep Learning: The Effect of Packet Loss on the Object Detection Performance. VTC Spring 2019: 1-5 - [i17]Shiwei Liu, Decebal Constantin Mocanu, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei, Mykola Pechenizkiy:
Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware. CoRR abs/1901.09181 (2019) - [i16]Shiwei Liu, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Intrinsically Sparse Long Short-Term Memory Networks. CoRR abs/1901.09208 (2019) - [i15]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George H. L. Fletcher, Mykola Pechenizkiy:
Learning with Delayed Synaptic Plasticity. CoRR abs/1903.09393 (2019) - [i14]Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca, Matt Coler, George H. L. Fletcher, Mykola Pechenizkiy:
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. CoRR abs/1904.01709 (2019) - [i13]Xin Du, Lei Sun, Wouter Duivesteijn, Alexander G. Nikolaev, Mykola Pechenizkiy
:
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data. CoRR abs/1904.13335 (2019) - [i12]Shiwei Liu, Decebal Constantin Mocanu, Mykola Pechenizkiy:
On improving deep learning generalization with adaptive sparse connectivity. CoRR abs/1906.11626 (2019) - [i11]Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy:
A Human-Grounded Evaluation of SHAP for Alert Processing. CoRR abs/1907.03324 (2019) - [i10]Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy:
Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models. CoRR abs/1907.03334 (2019) - [i9]Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy:
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison. CoRR abs/1908.00831 (2019) - [i8]Iftitahu Ni'mah, Vlado Menkovski, Mykola Pechenizkiy:
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation. CoRR abs/1909.09485 (2019) - [i7]Masoud Mansoury, Himan Abdollahpouri, Joris Rombouts, Mykola Pechenizkiy:
The Relationship between the Consistency of Users' Ratings and Recommendation Calibration. CoRR abs/1911.00852 (2019) - 2018
- [j32]Rosa Sicilia
, Stella Lo Giudice
, Yulong Pei, Mykola Pechenizkiy
, Paolo Soda
:
Twitter rumour detection in the health domain. Expert Syst. Appl. 110: 33-40 (2018) - [j31]Jianpeng Zhang, Yulong Pei, George H. L. Fletcher, Mykola Pechenizkiy
:
A bounded-size clustering algorithm on fully-dynamic streaming graphs. Intell. Data Anal. 22(5): 1039-1058 (2018) - [j30]Negar Ahmadi, Rene M. H. Besseling, Mykola Pechenizkiy
:
Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series. Soc. Netw. Anal. Min. 8(1): 47:1-47:17 (2018) - [j29]José María Luna
, Francisco Padillo, Mykola Pechenizkiy
, Sebastián Ventura
:
Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data. IEEE Trans. Cybern. 48(10): 2851-2865 (2018) - [j28]José María Luna
, Mykola Pechenizkiy
, María José del Jesus
, Sebastián Ventura
:
Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming. IEEE Trans. Cybern. 48(11): 3030-3044 (2018) - [c109]Negar Ahmadi, Evelien Carrette, Albert P. Aldenkamp, Mykola Pechenizkiy
:
Finding Predictive EEG Complexity Features for Classification of Epileptic and Psychogenic Nonepileptic Seizures Using Imperialist Competitive Algorithm. CBMS 2018: 164-169 - [c108]Xin Du, Wouter Duivesteijn, Mykola Pechenizkiy:
ELBA: Exceptional Learning Behavior Analysis. EDM 2018 - [c107]Anil Yaman
, Giovanni Iacca
, Matt Coler
, George H. L. Fletcher, Mykola Pechenizkiy
:
Multi-strategy Differential Evolution. EvoApplications 2018: 617-633 - [c106]Anil Yaman
, Decebal Constantin Mocanu
, Giovanni Iacca
, George H. L. Fletcher, Mykola Pechenizkiy
:
Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution. GECCO 2018: 569-576 - [c105]Yulong Pei, Jianpeng Zhang, George H. L. Fletcher, Mykola Pechenizkiy
:
DyNMF: Role Analytics in Dynamic Social Networks. IJCAI 2018: 3818-3824 - [c104]Simon van der Zon, Wouter Duivesteijn, Werner van Ipenburg, Jan Veldsink, Mykola Pechenizkiy
:
ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations. MIDAS/PAP@PKDD/ECML 2018: 81-94 - [c103]Wouter Ligtenberg, Yulong Pei, George H. L. Fletcher, Mykola Pechenizkiy
:
Tink: A Temporal Graph Analytics Library for Apache Flink. WWW (Companion Volume) 2018: 71-72 - [i6]Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca, George H. L. Fletcher, Mykola Pechenizkiy:
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution. CoRR abs/1804.07234 (2018) - [i5]Yulong Pei, Xin Du, Jianpeng Zhang, George H. L. Fletcher, Mykola Pechenizkiy:
struc2gauss: Structure Preserving Network Embedding via Gaussian Embedding. CoRR abs/1805.10043 (2018) - [i4]Oren Zeev-Ben-Mordehai, Wouter Duivesteijn, Mykola Pechenizkiy:
Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally. CoRR abs/1808.07243 (2018) - [i3]Wenting Xiong, Iftitahu Ni'mah, Juan M. G. Huesca, Werner van Ipenburg, Jan Veldsink, Mykola Pechenizkiy
:
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN. CoRR abs/1811.03970 (2018) - 2017
- [j27]Elisa Costante, Jerry den Hartog, Milan Petkovic, Sandro Etalle, Mykola Pechenizkiy
:
A white-box anomaly-based framework for database leakage detection. J. Inf. Secur. Appl. 32: 27-46 (2017) - [c102]Rosa Sicilia
, Stella Lo Giudice, Yulong Pei, Mykola Pechenizkiy
, Paolo Soda
:
Health-related rumour detection on Twitter. BIBM 2017: 1599-1606 - [c101]Negar Ahmadi, Yulong Pei, Mykola Pechenizkiy
:
Detection of Alcoholism Based on EEG Signals and Functional Brain Network Features Extraction. CBMS 2017: 179-184 - [c100]Jianpeng Zhang, Kaijie Zhu, Yulong Pei, George H. L. Fletcher, Mykola Pechenizkiy
:
Clustering-Structure Representative Sampling from Graph Streams. COMPLEX NETWORKS 2017: 265-277 - [c99]Roberto Martínez Maldonado, Kalina Yacef
, Augusto Dias Pereira dos Santos, Simon Buckingham Shum
, Vanessa Echeverría
, Olga C. Santos
, Mykola Pechenizkiy
:
Towards Proximity Tracking and Sensemaking for Supporting Teamwork and Learning. ICALT 2017: 89-91 - [c98]Alexandr V. Maslov, Mykola Pechenizkiy
, Yulong Pei, Indre Zliobaite
, Alexander Shklyaev, Tommi Kärkkäinen, Jaakko Hollmén:
BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints. IJCNN 2017: 1916-1923 - [c97]Roberto Martínez Maldonado, Augusto Dias Pereira dos Santos, Vanessa Echeverría, Kalina Yacef, Mykola Pechenizkiy:
How to Capitalise on Mobility, Proximity and Motion Analytics to Support Formal and Informal Education? MMLA-CrossLAK@LAK 2017: 39-46 - [c96]Simon van der Zon, Oren Zeev-Ben-Mordehai, Tom Vrijdag, Werner van Ipenburg, Wouter Duivesteijn, Jan Veldsink, Mykola Pechenizkiy:
BoostEMM - Transparent Boosting using Exceptional Model Mining. MIDAS@PKDD/ECML 2017: 5-16 - [c95]Wouter Duivesteijn, Tara Farzami, Thijs Putman, Evertjan Peer, Hilde J. P. Weerts
, Jasper N. Adegeest, Gerson Foks, Mykola Pechenizkiy
:
Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining. ECML/PKDD (3) 2017: 114-126 - [c94]Roberto Martínez Maldonado, Mykola Pechenizkiy
, Simon Buckingham Shum
, Tamara Power
, Carolyn Hayes
, Carmen Axisa:
Modelling Embodied Mobility Teamwork Strategies in a Simulation-Based Healthcare Classroom. UMAP 2017: 308-312 - 2016
- [j26]Leonardo Onofri
, Paolo Soda
, Mykola Pechenizkiy
, Giulio Iannello
:
A survey on using domain and contextual knowledge for human activity recognition in video streams. Expert Syst. Appl. 63: 97-111 (2016) - [j25]José María Luna
, Mykola Pechenizkiy
, Sebastián Ventura:
Mining exceptional relationships with grammar-guided genetic programming. Knowl. Inf. Syst. 47(3): 571-594 (2016) - [j24]Dragan Gasevic
, Mykola Pechenizkiy:
Let's Grow Together: Tutorials on Learning Analytics Methods. J. Learn. Anal. 3(3): 5-8 (2016) - [j23]José María Luna
, Alberto Cano
, Mykola Pechenizkiy
, Sebastián Ventura
:
Speeding-Up Association Rule Mining With Inverted Index Compression. IEEE Trans. Cybern. 46(12): 3059-3072 (2016) - [j22]Peter Brusilovsky
, Mike Sharples, Gustavo R. Alves
, Tiffany Barnes, Sherry Y. Chen, Carol H. C. Chu, Hendrik Drachsler, Seiji Isotani
, Euan Lindsay
, Xavier Ochoa
, Mykola Pechenizkiy
, Ma. Mercedes T. Rodrigo
, Cristóbal Romero
, Sergey A. Sosnovsky, Stefaan Ternier, Katrien Verbert
:
Editorial: A Message from the Editorial Team and an Introduction to the January-March 2016 Issue. IEEE Trans. Learn. Technol. 9(1): 1-4 (2016) - [c93]Jianpeng Zhang, Yulong Pei, George H. L. Fletcher, Mykola Pechenizkiy
:
Structural measures of clustering quality on graph samples. ASONAM 2016: 345-348 - [c92]Negar Ahmadi, Mykola Pechenizkiy
:
Application of Horizontal Visibility Graph as a Robust Measure of Neurophysiological Signals Synchrony. CBMS 2016: 273-278 - [c91]Alejandro Montes García, Natalia Stash, Marc Fabri, Paul De Bra, George H. L. Fletcher, Mykola Pechenizkiy:
Adaptive web-based educational application for autistic students. HT (Extended Proceedings) 2016 - [c90]Harm Eggels, Ruud van Elk, Mykola Pechenizkiy:
Explaining Soccer Match Outcomes with Goal Scoring Opportunities Predictive Analytics. MLSA@PKDD/ECML 2016 - [c89]Alexander Nieuwenhuijse, Jorn Bakker, Mykola Pechenizkiy
:
Finding Incident-Related Social Media Messages for Emergency Awareness. ECML/PKDD (3) 2016: 67-70 - [c88]Jianpeng Zhang, Mykola Pechenizkiy
, Yulong Pei, Julia Efremova:
A robust density-based clustering algorithm for multi-manifold structure. SAC 2016: 832-838 - [c87]Wouter van Heeswijk, George H. L. Fletcher, Mykola Pechenizkiy
:
On structure preserving sampling and approximate partitioning of graphs. SAC 2016: 875-882 - [c86]Alexandr V. Maslov, Hoang Thanh Lam, Mykola Pechenizkiy
, Eric Bouillet, Tommi Kärkkäinen:
DOBRO: a prediction error correcting robot under drifts. SAC 2016: 945-948 - [c85]Alexandr V. Maslov, Mykola Pechenizkiy
, Indre Zliobaite
, Tommi Kärkkäinen:
Modelling Recurrent Events for Improving Online Change Detection. SDM 2016: 549-557 - [c84]Alejandro Montes García, Natalia Stash, Marc Fabri, Paul De Bra, George H. L. Fletcher, Mykola Pechenizkiy:
WiBAF into a CMS: Personalization in Learning Environments Made Easy. UMAP (Extended Proceedings) 2016 - 2015
- [c83]Sergey Chernov, Mykola Pechenizkiy
, Tapani Ristaniemi:
The influence of dataset size on the performance of cell outage detection approach in LTE-A networks. ICICS 2015: 1-5 - [c82]Georgios Aravanis, Anca I. D. Bucur, Mykola Pechenizkiy
:
Hippocrates: A Context-Aware, Collaboration Enabling Search Tool. CBMS 2015: 320-325 - [c81]Dragan Gasevic, Taylor Martin, Zachary A. Pardos, Mykola Pechenizkiy, John C. Stamper, Osmar R. Zaïane:
Ethics and Privacy in EDM. EDM 2015: 13 - [c80]Ryan S. Baker, Peter Brusilovsky, Dragan Gasevic, Neil T. Heffernan, Mykola Pechenizkiy, Alyssa Friend Wise:
Grand Challenges for EDM and Related Research Areas. EDM 2015: 15 - [c79]Mykola Pechenizkiy
:
Predictive analytics on evolving data streams anticipating and adapting to changes in known and unknown contexts. HPCS 2015: 658-659 - [p4]Erik Tromp, Mykola Pechenizkiy
:
Pattern-Based Emotion Classification on Social Media. Advances in Social Media Analysis 2015: 1-20 - [e7]Olga C. Santos, Jesus Boticario, Cristóbal Romero, Mykola Pechenizkiy, Agathe Merceron, Piotr Mitros, José María Luna, Marian Cristian Mihaescu, Pablo Moreno, Arnon Hershkovitz, Sebastián Ventura, Michel C. Desmarais:
Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29, 2015. International Educational Data Mining Society (IEDMS) 2015, ISBN 978-8-4606-9425-0 [contents] - 2014
- [j21]João Gama
, Indre Zliobaite
, Albert Bifet
, Mykola Pechenizkiy
, Abdelhamid Bouchachia
:
A survey on concept drift adaptation. ACM Comput. Surv. 46(4): 44:1-44:37 (2014) - [j20]Mykola Pechenizkiy
, Dragan Gasevic
:
Introduction into Sparks of the Learning Analytics Future. J. Learn. Anal. 1(3): 145-149 (2014) - [j19]R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst
, Indre Zliobaite
, Mykola Pechenizkiy
:
Dealing With Concept Drifts in Process Mining. IEEE Trans. Neural Networks Learn. Syst. 25(1): 154-171 (2014) - [c78]Hindra Kurniawan, Mykola Pechenizkiy
:
Towards the Stress Analytics Framework: Managing, Mining, and Visualizing Multi-modal Data for Stress Awareness. CBMS 2014: 541-542 - [c77]Elisa Costante, Jerry den Hartog, Milan Petkovic, Sandro Etalle, Mykola Pechenizkiy
:
Hunting the Unknown - White-Box Database Leakage Detection. DBSec 2014: 243-259 - [c76]Mykola Pechenizkiy, Pedro A. Toledo:
Learning to Teach like a Bandit. EDM 2014: 381-382 - [c75]Alejandro Montes García, Paul De Bra, George H. L. Fletcher, Mykola Pechenizkiy
:
A DSL based on CSS for hypertext adaptation. HT 2014: 313-315 - [c74]Julia Kiseleva, Alejandro Montes García, Yongming Luo, Mykola Pechenizkiy, Paul De Bra, Jaap Kamps:
Applying Learning to Rank Techniques to Contextual Suggestions. TREC 2014 - [i2]Erik Tromp, Mykola Pechenizkiy:
Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel. CoRR abs/1412.4682 (2014) - 2013
- [j18]Minseok Song
, H. Yang, Seyed Hossein Siadat
, Mykola Pechenizkiy
:
A comparative study of dimensionality reduction techniques to enhance trace clustering performances. Expert Syst. Appl. 40(9): 3722-3737 (2013) - [j17]Mykola Pechenizkiy
, Indre Zliobaite
:
Introduction to the special issue on handling concept drift in adaptive information systems. Evol. Syst. 4(1): 1-2 (2013) - [j16]Amelia Zafra
, Mykola Pechenizkiy
, Sebastián Ventura
:
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning. Inf. Sci. 222: 282-301 (2013) - [j15]Hock Hee Ang, Vivekanand Gopalkrishnan, Indre Zliobaite
, Mykola Pechenizkiy
, Steven C. H. Hoi
:
Predictive Handling of Asynchronous Concept Drifts in Distributed Environments. IEEE Trans. Knowl. Data Eng. 25(10): 2343-2355 (2013) - [c73]Hindra Kurniawan, Alexandr V. Maslov, Mykola Pechenizkiy
:
Stress detection from speech and Galvanic Skin Response signals. CBMS 2013: 209-214 - [c72]Ayoze Marrero, Juan A. Méndez
, Alexandr V. Maslov, Mykola Pechenizkiy
:
ACLAC: An approach for adaptive closed-loop anesthesia control. CBMS 2013: 285-290 - [c71]Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy
, Toon Calders:
Predicting Current User Intent with Contextual Markov Models. ICDM Workshops 2013: 391-398 - [c70]Erik Tromp, Mykola Pechenizkiy
:
RBEM: a rule based approach to polarity detection. WISDOM 2013: 8:1-8:9 - [c69]Erkin Demirtas, Mykola Pechenizkiy
:
Cross-lingual polarity detection with machine translation. WISDOM 2013: 9:1-9:8 - [c68]Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy
, Toon Calders:
Discovering temporal hidden contexts in web sessions for user trail prediction. WWW (Companion Volume) 2013: 1067-1074 - [p3]Faisal Kamiran, Toon Calders, Mykola Pechenizkiy
:
Techniques for Discrimination-Free Predictive Models. Discrimination and Privacy in the Information Society 2013: 223-239 - [e6]Mykola Pechenizkiy
, Marek Wojciechowski:
New Trends in Databases and Information Systems, Workshop Proceedings of the 16th East European Conference, ADBIS 2012, Poznań, Poland, September 17-21, 2012. Advances in Intelligent Systems and Computing 185, Springer 2013, ISBN 978-3-642-32517-5 [contents] - [e5]Pedro Pereira Rodrigues, Mykola Pechenizkiy, João Gama, Ricardo Cruz-Correia, Jiming Liu, Agma J. M. Traina, Peter J. F. Lucas, Paolo Soda:
Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, June 20-22, 2013. IEEE Computer Society 2013, ISBN 978-1-4799-1053-3 [contents] - [i1]Indre Zliobaite, Mykola Pechenizkiy:
Predictive User Modeling with Actionable Attributes. CoRR abs/1312.6558 (2013) - 2012
- [j14]Indre Zliobaite
, Jorn Bakker, Mykola Pechenizkiy
:
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? Expert Syst. Appl. 39(1): 806-815 (2012) - [j13]Amelia Zafra
, Mykola Pechenizkiy
, Sebastián Ventura
:
ReliefF-MI: An extension of ReliefF to multiple instance learning. Neurocomputing 75(1): 210-218 (2012) - [j12]Paolo Soda, Sameer K. Antani, Francesco Tortorella, Mario Cannataro, Mykola Pechenizkiy
, Alexey Tsymbal:
Trends in computer-based medical systems. SIGHIT Rec. 2(2): 46-50 (2012) - [c67]Mykola Pechenizkiy, Nikola Trcka, Paul De Bra, Pedro A. Toledo:
CurriM: Curriculum Mining. EDM 2012: 216-217 - [c66]Rafal Kocielnik, Mykola Pechenizkiy, Natalia Sidorova:
Stress Analytics in Education. EDM 2012: 236-237 - [c65]Jorn Bakker, Leszek Holenderski, Rafal Kocielnik
, Mykola Pechenizkiy
, Natalia Sidorova
:
Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching. IHI 2012: 673-678 - [c64]Lorraine Chambers, Erik Tromp, Mykola Pechenizkiy
, Mohamed Medhat Gaber
:
Mobile Sentiment Analysis. KES 2012: 470-479 - [c63]Edward Tersoo Apeh, Indre Zliobaite
, Mykola Pechenizkiy
, Bogdan Gabrys:
Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales. SGAI Conf. 2012: 213-218 - [e4]Paolo Soda, Francesco Tortorella, Sameer K. Antani, Mykola Pechenizkiy, Mario Cannataro, Alexey Tsymbal:
Proceedings of CBMS 2012, The 25th IEEE International Symposium on Computer-Based Medical Systems, June 20-22, 2012, Rome, Italy. IEEE Computer Society 2012, ISBN 978-1-4673-2051-1 [contents] - [e3]Mykola Pechenizkiy, Evgeny Knutov, Michael Yudelson, Fabian Abel, Geert-Jan Houben, Eelco Herder:
Proceedings of the Workshop on Dynamic and Adaptive Hypertext: Generic Frameworks, Approaches and Techniques, DAH@HT 2011, Eindhoven, The Netherlands, June 6, 2011. CEUR Workshop Proceedings 823, CEUR-WS.org 2012 [contents] - 2011
- [j11]Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy:
Generic Adaptation Framework: a Process-Oriented Perspective. J. Digit. Inf. 12(1) (2011) - [j10]Toon Calders, Mykola Pechenizkiy
:
Introduction to the special section on educational data mining. SIGKDD Explor. 13(2): 3-6 (2011) - [c62]R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst
, Indre Zliobaite
, Mykola Pechenizkiy
:
Handling Concept Drift in Process Mining. CAiSE 2011: 391-405 - [c61]Oleksiy Mazhelis, Indre Zliobaite
, Mykola Pechenizkiy
:
Context-Aware Personal Route Recognition. Discovery Science 2011: 221-235 - [c60]John Hannon, Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy, Kevin McCarthy, Barry Smyth:
Bridging Recommendation and Adaptation: Generic Adaptation Framework - Twittomender compliance case-study. DAH@HT 2011: 1-9 - [c59]Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy:
Adaptive Hypermedia Systems Analysis Approach by Means of the GAF Framework. DAH@HT 2011: 41-46 - [c58]Jorn Bakker, Mykola Pechenizkiy
, Natalia Sidorova
:
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data. ICDM Workshops 2011: 573-580 - [c57]Erik Tromp, Mykola Pechenizkiy
:
SentiCorr: Multilingual Sentiment Analysis of Personal Correspondence. ICDM Workshops 2011: 1247-1250 - [c56]Evgeny Knutov, Paul De Bra, David Smits, Mykola Pechenizkiy:
Bridging Navigation, Search and Adaptation - Adaptive Hypermedia Models Evolution. WEBIST 2011: 314-321 - [e2]Mykola Pechenizkiy, Toon Calders, Cristina Conati, Sebastián Ventura, Cristóbal Romero, John C. Stamper:
Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands, July 6-8, 2011. www.educationaldatamining.org 2011, ISBN 978-90-386-2537-9 [contents] - 2010
- [j9]Paolo Soda
, Mykola Pechenizkiy
, Francesco Tortorella
, Alexey Tsymbal:
Knowledge discovery and computer-based decision support in biomedicine. Artif. Intell. Medicine 50(1): 1-2 (2010) - [c55]Mykola Pechenizkiy
, Indre Zliobaite:
Handling concept drift in medical applications: Importance, challenges and solutions. CBMS 2010: 5 - [c54]Mykola Pechenizkiy
, Ekaterina Vasilyeva, Indre Zliobaite
, Aleksandra Tesanovic, Goran Manev:
Heart failure hospitalization prediction in remote patient management systems. CBMS 2010: 44-49 - [c53]Seppo Puuronen, Ekaterina Vasilyeva, Mykola Pechenizkiy
, Aleksandra Tesanovic:
A holistic framework for understanding acceptance of Remote Patient Management (RPM) systems by non-professional users. CBMS 2010: 426-431 - [c52]Cristóbal Romero, Sebastián Ventura, Ekaterina Vasilyeva, Mykola Pechenizkiy:
Class Association Rules Mining from Students' Test Data. EDM 2010: 317-318 - [c51]Ekaterina Vasilyeva, Mykola Pechenizkiy, Aleksandra Tesanovic, Evgeny Knutov, Sicco Verwer, Paul De Bra:
Towards EDM Framework for Personalization of Information Services in RPM Systems. EDM 2010: 331-332 - [c50]Amelia Zafra
, Mykola Pechenizkiy
, Sebastián Ventura:
Reducing Dimensionality in Multiple Instance Learning with a Filter Method. HAIS (2) 2010: 35-44 - [c49]Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy
:
Provenance meets adaptive hypermedia. HT 2010: 93-98 - [c48]Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy
:
Adaptation and search: from Dexter and AHAM to GAF. HT 2010: 281-282 - [c47]Faisal Kamiran, Toon Calders, Mykola Pechenizkiy
:
Discrimination Aware Decision Tree Learning. ICDM 2010: 869-874 - [c46]Indre Zliobaite
, Mykola Pechenizkiy
:
Learning with Actionable Attributes: Attention -- Boundary Cases! ICDM Workshops 2010: 1021-1028 - [c45]Amelia Zafra
, Mykola Pechenizkiy
, Sebastián Ventura:
Feature selection is the ReliefF for multiple instance learning. ISDA 2010: 525-532 - [c44]Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy:
Generic Adaptation Process. WABBWUAS@UMAP 2010: 13-24 - [p2]Seppo Puuronen, Mykola Pechenizkiy
:
Towards the Generic Framework for Utility Considerations in Data Mining Research. Data Mining for Business Applications 2010: 49-65 - [e1]Fabian Abel, Eelco Herder, Geert-Jan Houben, Mykola Pechenizkiy, Michael Yudelson:
Proceedings of the International Workshop on Architectures and Building Blocks of Web-Based User-Adaptive Systems, WABBWUAS@UMAP 2010, Hawaii, June 21, 2010. CEUR Workshop Proceedings 609, CEUR-WS.org 2010 [contents]
2000 – 2009
- 2009
- [j8]Mykola Pechenizkiy
, Alexey Tsymbal:
Guest editorial for DKE special issue on "Biomedical Data Mining". Data Knowl. Eng. 68(12): 1357-1358 (2009) - [j7]Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy
:
AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques. New Rev. Hypermedia Multim. 15(1): 5-38 (2009) - [j6]Mykola Pechenizkiy
, Jorn Bakker, Indre Zliobaite
, Andriy Ivannikov, Tommi Kärkkäinen:
Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift. SIGKDD Explor. 11(2): 109-116 (2009) - [c43]Aleksandra Tesanovic, Goran Manev, Mykola Pechenizkiy
, Ekaterina Vasilyeva:
eHealth personalization in the next generation RPM systems. CBMS 2009: 1-8 - [c42]Indre Zliobaite
, Jorn Bakker, Mykola Pechenizkiy
:
OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers. Discovery Science 2009: 272-286 - [c41]Gerben Dekker, Mykola Pechenizkiy, Jan Vleeshouwers:
Predicting Students Drop Out: A Case Study. EDM 2009: 41-50 - [c40]Mykola Pechenizkiy, Nikola Trcka, Ekaterina Vasilyeva, Wil M. P. van der Aalst, Paul De Bra:
Process Mining Online Assessment Data. EDM 2009: 279-288 - [c39]Paul De Bra, Mykola Pechenizkiy
:
Dynamic and adaptive hypertext: generic frameworks, approaches and techniques. Hypertext 2009: 387-388 - [c38]Toon Calders, Faisal Kamiran, Mykola Pechenizkiy
:
Building Classifiers with Independency Constraints. ICDM Workshops 2009: 13-18 - [c37]Indre Zliobaite
, Jorn Bakker, Mykola Pechenizkiy
:
Towards Context Aware Food Sales Prediction. ICDM Workshops 2009: 94-99 - [c36]Andriy Ivannikov, Mykola Pechenizkiy
, Jorn Bakker, Timo Leino, Mikko Jegoroff, Tommi Kärkkäinen, Sami Äyrämö
:
Online Mass Flow Prediction in CFB Boilers. ICDM 2009: 206-219 - [c35]Nikola Trcka, Mykola Pechenizkiy
:
From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining. ISDA 2009: 1114-1119 - [c34]Jorn Bakker, Mykola Pechenizkiy
:
Food Wholesales Prediction: What Is Your Baseline? ISMIS 2009: 493-502 - [c33]Jorn Bakker, Mykola Pechenizkiy
, Indre Zliobaite
, Andriy Ivannikov, Tommi Kärkkäinen:
Handling outliers and concept drift in online mass flow prediction in CFB boilers. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 13-22 - [c32]Toon Calders, Christian W. Günther, Mykola Pechenizkiy
, Anne Rozinat:
Using minimum description length for process mining. SAC 2009: 1451-1455 - 2008
- [j5]Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal:
Towards more relevance-oriented data mining research. Intell. Data Anal. 12(2): 237-249 (2008) - [j4]Alexey Tsymbal, Mykola Pechenizkiy
, Padraig Cunningham
, Seppo Puuronen:
Dynamic integration of classifiers for handling concept drift. Inf. Fusion 9(1): 56-68 (2008) - [c31]Ekaterina Vasilyeva, Mykola Pechenizkiy
, Paul De Bra:
Adaptation of Elaborated Feedback in e-Learning. AH 2008: 235-244 - [c30]Seppo Puuronen, Mykola Pechenizkiy
, Alexey Tsymbal:
Effectiveness of Local Feature Selection in Ensemble Learning for Prediction of Antimicrobial Resistance. CBMS 2008: 632-637 - [c29]Maurice Hendrix
, Paul De Bra, Mykola Pechenizkiy
, David Smits, Alexandra I. Cristea
:
Defining Adaptation in a Generic Multi Layer Model: CAM: The GRAPPLE Conceptual Adaptation Model. EC-TEL 2008: 132-143 - [c28]Ekaterina Vasilyeva, Paul De Bra, Mykola Pechenizkiy
:
Immediate Elaborated Feedback Personalization in Online Assessment. EC-TEL 2008: 449-460 - [c27]Mykola Pechenizkiy, Toon Calders, Ekaterina Vasilyeva, Paul De Bra:
Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study. EDM 2008: 187-191 - [c26]Ekaterina Vasilyeva, Paul De Bra, Mykola Pechenizkiy
, Seppo Puuronen:
Tailoring Feedback in Online Assessment: Influence of Learning Styles on the Feedback Preferences and Elaborated Feedback Effectiveness. ICALT 2008: 834-838 - [c25]Patrick Meulstee, Mykola Pechenizkiy
:
Food Sales Prediction: "If Only It Knew What We Know". ICDM Workshops 2008: 134-143 - [c24]Ekaterina Vasilyeva, Mykola Pechenizkiy
, Paul De Bra:
Tailoring of Feedback in Web-Based Learning: The Role of Response Certitude in the Assessment. Intelligent Tutoring Systems 2008: 771-773 - [p1]Mykola Pechenizkiy
, Seppo Puuronen, Alexey Tsymbal:
Does Relevance Matter to Data Mining Research?. Data Mining: Foundations and Practice 2008: 251-275 - 2007
- [j3]Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen, David W. Patterson:
Feature Extraction for Dynamic Integration of Classifiers. Fundam. Informaticae 77(3): 243-275 (2007) - [c23]Ekaterina Vasilyeva, Mykola Pechenizkiy
, Tatiana Gavrilova
, Seppo Puuronen:
Personalization of Immediate Feedback to Learning Styles. ICALT 2007: 622-624 - [c22]Joseph E. Beck, Toon Calders, Mykola Pechenizkiy
, Silvia Rita Viola:
Workshop on Educational Data Mining @ ICALT07 (EDM@ICALT07). ICALT 2007: 933-934 - 2006
- [j2]Mykola Pechenizkiy
, Alexey Tsymbal, Seppo Puuronen:
Local Dimensionality Reduction and Supervised Learning Within Natural Clusters for Biomedical Data Analysis. IEEE Trans. Inf. Technol. Biomed. 10(3): 533-539 (2006) - [c21]Alexey Tsymbal, Mykola Pechenizkiy
, Padraig Cunningham
, Seppo Puuronen:
Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections. CBMS 2006: 679-684 - [c20]Mykola Pechenizkiy
, Alexey Tsymbal, Seppo Puuronen, Oleksandr Pechenizkiy:
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction. CBMS 2006: 708-713 - [c19]Seppo Puuronen, Mykola Pechenizkiy
, Alexey Tsymbal:
Keynote Paper: Data Mining Researcher, Who is Your Customer? Some Issues Inspired by the Information Systems Field. DEXA Workshops 2006: 579-583 - [c18]Alexey Tsymbal, Mykola Pechenizkiy
, Padraig Cunningham
:
Dynamic Integration with Random Forests. ECML 2006: 801-808 - [c17]Ekaterina Vasilyeva, Mykola Pechenizkiy
, Seppo Puuronen:
The Challenge of Feedback Personalization to Learning Styles in a Web-Based Learning System. ICALT 2006: 1143-1144 - [c16]Mykola Pechenizkiy
, Seppo Puuronen, Alexey Tsymbal:
The impact of sample reduction on PCA-based feature extraction for supervised learning. SAC 2006: 553-558 - 2005
- [j1]Alexey Tsymbal, Mykola Pechenizkiy
, Padraig Cunningham
:
Diversity in search strategies for ensemble feature selection. Inf. Fusion 6(1): 83-98 (2005) - [c15]Mykola Pechenizkiy
:
The Impact of Feature Extraction on the Performance of a Classifier: kNN, Naïve Bayes and C4.5. Canadian AI 2005: 268-279 - [c14]Ekaterina Vasilyeva, Mykola Pechenizkiy
, Seppo Puuronen:
Towards the Framework of Adaptive User Interfaces for eHealth. CBMS 2005: 139-144 - [c13]Mykola Pechenizkiy
, Alexey Tsymbal, Seppo Puuronen:
Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis. CBMS 2005: 365-370 - [c12]Mykola Pechenizkiy:
Data Mining Strategy Selection via Empirical and Constructive Induction. Databases and Applications 2005: 59-64 - [c11]Mykola Pechenizkiy
, Alexey Tsymbal, Seppo Puuronen:
Knowledge Management Challenges in Knowledge Discovery Systems. DEXA Workshops 2005: 433-437 - [c10]Mykola Pechenizkiy
, Seppo Puuronen, Alexey Tsymbal:
Competitive Advantage from Data Mining: Some Lessons Learnt in the Information Systems Field. DEXA Workshops 2005: 733-737 - [c9]Alexey Tsymbal, Mykola Pechenizkiy, Padraig Cunningham:
Sequential Genetic Search for Ensemble Feature Selection. IJCAI 2005: 877-882 - [c8]Alexey Tsymbal, Mykola Pechenizkiy, Seppo Puuronen, Michael A. Shifrin, Irina Alexandrova:
Knowledge Discovery in Microbiology Data: Analysis of Antibiotic Resistance in Nosocomial Infections. Wissensmanagement 2005: 334-340 - [c7]Mykola Pechenizkiy
, Alexey Tsymbal, Seppo Puuronen, Michael A. Shifrin, Irina Alexandrova:
Knowledge Discovery from Microbiology Data: Many-Sided Analysis of Antibiotic Resistance in Nosocomial Infections. Wissensmanagement (LNCS Volume) 2005: 360-372 - 2004
- [c6]Mykola Pechenizkiy
, Alexey Tsymbal, Seppo Puuronen:
PCA-based Feature Transformation for Classification: Issues in Medical Diagnostics. CBMS 2004: 535- - [c5]Alexey Tsymbal, Mykola Pechenizkiy
, Padraig Cunningham
:
Diversity in Random Subspacing Ensembles. DaWaK 2004: 309-319 - 2003
- [c4]Alexey Tsymbal, Mykola Pechenizkiy
, Seppo Puuronen, David W. Patterson:
Dynamic Integration of Classifiers in the Space of Principal Components. ADBIS 2003: 278-292 - [c3]Alexey Tsymbal, Padraig Cunningham
, Mykola Pechenizkiy
, Seppo Puuronen:
Search Strategies for Ensemble Feature Selection in Medical Diagnostics. CBMS 2003: 124-129 - [c2]Mykola Pechenizkiy
, Seppo Puuronen, Alexey Tsymbal:
Feature Extraction for Classification in Knowledge Discovery Systems. KES 2003: 526-532 - 2002
- [c1]Alexey Tsymbal, Seppo Puuronen, Mykola Pechenizkiy, Matthias Baumgarten, David W. Patterson:
Eigenvector-Based Feature Extraction for Classification. FLAIRS 2002: 354-358
Coauthor Index
aka: George H. L. Fletcher
aka: Hilde Jacoba Petronella Weerts
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