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ORCIDMykola Pechenizkiy
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- affiliation: Eindhoven University of Technology, Netherlands
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2020 – today
- 2025
[j67]Jiaxu Zhao
, Tianjin Huang, Shiwei Liu, Jie Yin, Yulong Pei, Meng Fang, Mykola Pechenizkiy:
FS-GNN: Improving Fairness in Graph Neural Networks via Joint Sparsification. Neurocomputing 648: 130641 (2025)- [j66]Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Clemens Kaptein, Mykola Pechenizkiy:
Rethinking Knowledge Transfer in Learning Using Privileged Information. Trans. Mach. Learn. Res. 2025 (2025) - [c201]Can Jin, Tianjin Huang, Yihua Zhang, Mykola Pechenizkiy, Sijia Liu, Shiwei Liu, Tianlong Chen:
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective. AAAI 2025: 4111-4119 - [c200]Yudi Zhang, Pei Xiao, Lu Wang, Chaoyun Zhang, Meng Fang, Yali Du, Yevgeniy Puzyrev, Randolph Yao, Si Qin, Qingwei Lin, Mykola Pechenizkiy, Dongmei Zhang, Saravan Rajmohan, Qi Zhang:
RuAG: Learned-rule-augmented Generation for Large Language Models. ICLR 2025 - [c199]Tristan Tomilin, Meng Fang, Mykola Pechenizkiy:
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents. ICLR 2025 - [c198]Boqian Wu, Qiao Xiao, Shunxin Wang, Nicola Strisciuglio, Mykola Pechenizkiy, Maurice van Keulen, Decebal Constantin Mocanu, Elena Mocanu:
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness. ICLR 2025 - [c197]Rik Litjens, Róbinson Medina, Nikos Avramis, Camiel Beckers, S. Steven Wilkins, Mykola Pechenizkiy:
Energy Consumption Prediction with Uncertainty Quantification for Electric Truck Operations: A Data-Driven Approach. VEHITS 2025: 166-177 - [e11]Mattia Cerrato, Alesia Vallenas Coronel, Petra Ahrweiler, Michele Loi, Mykola Pechenizkiy, Aurelia Tamò-Larrieux:
Proceedings of the 3rd European Workshop on Algorithmic Fairness, Mainz, Germany, July 1st to 3rd, 2024. CEUR Workshop Proceedings 3908, CEUR-WS.org 2025 [contents] - [i116]Bohdan Turbal, Anastasiia Mazur, Jiaxu Zhao, Mykola Pechenizkiy:
On Adversarial Robustness of Language Models in Transfer Learning. CoRR abs/2501.00066 (2025) - [i115]Yudi Zhang, Lu Wang, Meng Fang, Yali Du, Chenghua Huang, Jun Wang, Qingwei Lin, Mykola Pechenizkiy, Dongmei Zhang, Saravan Rajmohan, Qi Zhang:
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones? CoRR abs/2502.19557 (2025) - [i114]Tristan Tomilin, Meng Fang, Mykola Pechenizkiy:
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents. CoRR abs/2503.08241 (2025) - [i113]Xiang Li, Yong Tao, Siyuan Zhang, Siwei Liu, Zhitong Xiong, Chunbo Luo, Lu Liu, Mykola Pechenizkiy, Xiao Xiang Zhu, Tianjin Huang:
REOBench: Benchmarking Robustness of Earth Observation Foundation Models. CoRR abs/2505.16793 (2025) - [i112]Bram Grooten, Farid Hasanov, Chenxiang Zhang, Qiao Xiao, Boqian Wu, Zahra Atashgahi, Ghada Sokar, Shiwei Liu, Lu Yin, Elena Mocanu, Mykola Pechenizkiy, Decebal Constantin Mocanu:
NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling. CoRR abs/2505.17909 (2025) - [i111]Qiao Xiao, Alan Ansell, Boqian Wu, Lu Yin, Mykola Pechenizkiy, Shiwei Liu, Decebal Constantin Mocanu:
Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution. CoRR abs/2505.24037 (2025) - [i110]Qiao Xiao, Boqian Wu, Andrey Poddubnyy, Elena Mocanu, Phuong H. Nguyen, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity. CoRR abs/2506.00932 (2025) - [i109]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. CoRR abs/2506.10629 (2025) - [i108]Tristan Tomilin, Luka van den Boogaard, Samuel Garcin, Bram Grooten, Meng Fang, Mykola Pechenizkiy:
MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning. CoRR abs/2506.14990 (2025) - [i107]Wenhan Han, Yifan Zhang, Zhixun Chen, Binbin Li, Haobin Lin, Bingni Zhang, Taifeng Wang, Mykola Pechenizkiy, Meng Fang, Yin Zheng:
MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages. CoRR abs/2506.19468 (2025) - 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) - [c196]Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang:
Large Language Models Are Neurosymbolic Reasoners. AAAI 2024: 17985-17993 - [c195]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 - [c194]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 - [c193]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 - [c192]Yucheng Yang, Tianyi Zhou, Lei Han, Meng Fang, Mykola Pechenizkiy:
Automatic Curriculum for Unsupervised Reinforcement Learning. AAMAS 2024: 2002-2010 - [c191]Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Are Sparse Neural Networks Better Hard Sample Learners? BMVC 2024 - [c190]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. ECAI 2024: 2669-2676 - [c189]Wenhan Han, Meng Fang, Zihan Zhang, Yu Yin, Zirui Song, Ling Chen, Mykola Pechenizkiy, Qingyu Chen:
MedINST: Meta Dataset of Biomedical Instructions. EMNLP (Findings) 2024: 8221-8240 - [c188]Qin Zhang, Sihan Cai, Jiaxu Zhao, Mykola Pechenizkiy, Meng Fang:
CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models. EMNLP (Findings) 2024: 14883-14898 - [c187]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 - [c186]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 - [c185]Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Gen Li, Ajay Kumar Jaiswal, Mykola Pechenizkiy, Yi Liang, Michael Bendersky, Zhangyang Wang, Shiwei Liu:
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. ICML 2024 - [c184]Danil Provodin, Maurits Clemens Kaptein, Mykola Pechenizkiy:
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling. ICML 2024 - [c183]Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy:
A Structural-Clustering Based Active Learning for Graph Neural Networks. IDA (1) 2024: 28-40 - [c182]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. INTERSPEECH 2024 - [c181]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. NeurIPS 2024 - [c180]Zahra Atashgahi, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Adaptive Sparsity Level During Training for Efficient Time Series Forecasting with Transformers. ECML/PKDD (1) 2024: 3-20 - [c179]Rianne Margaretha Schouten, Wouter Duivesteijn, Pekka Räsänen, Jacob M. Paul, Mykola Pechenizkiy:
Exceptional Subitizing Patterns: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression. ECML/PKDD (10) 2024: 66-82 - [c178]Adam Dubowski, Hilde J. P. Weerts, Anouk Wolters, Mykola Pechenizkiy:
Subgroup Harm Assessor: Identifying Potential Fairness-Related Harms and Predictive Bias. ECML/PKDD (8) 2024: 413-417 - [p5]Dennis Collaris, Mykola Pechenizkiy, Jarke J. van Wijk:
RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance. Commit2Data 2024: 8:1-8:11 - [i106]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) - [i105]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) - [i104]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) - [i103]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) - [i102]Tim D'Hondt, Mykola Pechenizkiy, Robert Peharz:
One-Shot Federated Learning with Bayesian Pseudocoresets. CoRR abs/2406.02177 (2024) - [i101]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) - [i100]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) - [i99]Tianjin Huang, Meng Fang, Li Shen, Fan Liu, Yulong Pei, Mykola Pechenizkiy, Shiwei Liu, Tianlong Chen:
(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork. CoRR abs/2407.17412 (2024) - [i98]Wieger Wesselink, Bram Grooten, Qiao Xiao, Cassio de Campos, Mykola Pechenizkiy:
Nerva: a Truly Sparse Implementation of Neural Networks. CoRR abs/2407.17437 (2024) - [i97]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. CoRR abs/2408.04583 (2024) - [i96]Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy:
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation. CoRR abs/2408.07364 (2024) - [i95]Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet:
A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams. CoRR abs/2408.09324 (2024) - [i94]Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Kaptein, Mykola Pechenizkiy:
Rethinking Knowledge Transfer in Learning Using Privileged Information. CoRR abs/2408.14319 (2024) - [i93]Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Are Sparse Neural Networks Better Hard Sample Learners? CoRR abs/2409.09196 (2024) - [i92]Boqian Wu, Qiao Xiao, Shunxin Wang, Nicola Strisciuglio, Mykola Pechenizkiy, Maurice van Keulen, Decebal Constantin Mocanu, Elena Mocanu:
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness. CoRR abs/2410.03030 (2024) - [i91]Wenhan Han, Meng Fang, Zihan Zhang, Yu Yin, Zirui Song, Ling Chen, Mykola Pechenizkiy, Qingyu Chen:
MedINST: Meta Dataset of Biomedical Instructions. CoRR abs/2410.13458 (2024) - [i90]Yudi Zhang, Pei Xiao, Lu Wang, Chaoyun Zhang, Meng Fang, Yali Du, Yevgeniy Puzyrev, Randolph Yao, Si Qin, Qingwei Lin, Mykola Pechenizkiy, Dongmei Zhang, Saravan Rajmohan, Qi Zhang:
RuAG: Learned-rule-augmented Generation for Large Language Models. CoRR abs/2411.03349 (2024) - [i89]Rik Raes, Saskia Lensink, Mykola Pechenizkiy:
Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Data. CoRR abs/2411.09431 (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 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 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
[j67] [c200] [c199] [i115] [i114] [i109] [i108] [i107] [c196] [c195] [c193] [c192] [c189] [c188] [c186] [i106] [i99] [i91] [i90] [c177] [c176] [c175] [c167] [c165] [c164] [c163] [c162] [c161] [i85] [i84] [i81] [i80] [i79] [i78] [i72] [i69] [i68] [c160] [c151] [c150] [c143] [i63] [i62] [i59] [i54] [c135] [i36]
George Fletcher 0001
aka: George H. L. Fletcher
aka: George H. L. Fletcher
Maurits Kaptein
aka: Maurits Clemens Kaptein
aka: Maurits Clemens Kaptein
[j67] [c201] [i112] [i111] [c185] [c182] [c181] [i100] [i99] [j59] [c177] [c168] [c167] [c165] [c162] [c161] [i87] [i80] [i79] [i78] [i76] [i73] [i72] [i70] [j50] [c155] [c154] [c151] [c148] [i67] [i62] [i61] [i59] [i54] [i53] [j43] [j42] [c141] [c133] [c132] [c131] [i51] [i47] [i42] [i41] [i39] [c118] [i24] [i17] [i16] [i12]
[c198] [i112] [i111] [i110] [c194] [c193] [c191] [c190] [c182] [c181] [c180] [i101] [i100] [i97] [i93] [i92] [j59] [c174] [c168] [i88] [i87] [i82] [i70] [i68] [j53] [j50] [c155] [c154] [c152] [c151] [c149] [c148] [c146] [i67] [i62] [i61] [i60] [i55] [i54] [i53] [j45] [j44] [j43] [j42] [c139] [c133] [c132] [c131] [i52] [i51] [i50] [i48] [i47] [i43] [i42] [i41] [i32] [c123] [c118] [i28] [i24] [i23] [i18] [c113] [i17] [i16] [i15] [i14] [i12] [c106] [i6]
[j67] [j64] [c195] [c183] [i99] [i96] [c172] [c162] [i78] [i75] [i71] [j56] [j54] [j52] [c158] [c153] [c151] [c145] [c143] [i62] [i56] [i54] [j43] [c142] [c140] [c137] [c133] [c130] [i51] [i45] [i44] [i40] [i34] [i33] [i30] [j38] [j37] [j36] [j34] [c127] [c122] [i21] [i20] [j33] [c116] [c115] [i17] [j32] [j31] [c105] [c103] [i5] [c102] [c101] [c100] [c98] [c93] [c88]
Hilde J. P. Weerts
aka: Hilde Jacoba Petronella Weerts
aka: Hilde Jacoba Petronella Weerts
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