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Mykola Pechenizkiy
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- affiliation: Eindhoven University of Technology, Netherlands
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2020 – today
- 2023
- [i70]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) - [i69]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) - [i68]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) - 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) - [c156]Tristan Tomilin, Tianhong Dai
, Meng Fang, Mykola Pechenizkiy:
LevDoom: A Benchmark for Generalization on Level Difficulty in Reinforcement Learning. CoG 2022: 72-79 - [c155]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 - [c154]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Linear Bandits. ICDM 2022: 1149-1154 - [c153]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 - [c152]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 - [c151]Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Semantic-Based Few-Shot Classification by Psychometric Learning. IDA 2022: 392-403 - [c150]Ghada Sokar, Elena Mocanu
, Decebal Constantin Mocanu
, Mykola Pechenizkiy, Peter Stone:
Dynamic Sparse Training for Deep Reinforcement Learning. IJCAI 2022: 3437-3443 - [c149]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 - [c148]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 - [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]