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- affiliation: Eindhoven University of Technology, Netherlands
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2020 – today
- 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) - [c171]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 - [c170]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 - [c169]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 - [c168]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 - [c167]Sheetal Borar, Hilde J. P. Weerts, Binyam Gebre, Mykola Pechenizkiy:
Improving Recommender System Diversity with Variational Autoencoders. BIAS 2023: 85-99 - [c166]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 - [c165]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 - [c164]Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet:
FALL: A Modular Adaptive Learning Platform for Streaming Data. ICDE 2023: 3619-3622 - [c163]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 - [c162]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 - [c161]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 - [c160]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 - [c159]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 - [i82]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) - [i81]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) - [i80]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) - [i79]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) - [i78]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) - [i77]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) - [i76]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) - [i75]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) - [i74]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) - [i73]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) - [i72]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) - [i71]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) - [i70]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) - [i69]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) - [i68]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) - 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) - [c158]Tristan Tomilin, Tianhong Dai, Meng Fang, Mykola Pechenizkiy:
LevDoom: A Benchmark for Generalization on Level Difficulty in Reinforcement Learning. CoG 2022: 72-79 - [c157]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 - [c156]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Linear Bandits. ICDM 2022: 1149-1154 - [c155]Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. ICLR 2022 - [c154]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy:
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. ICLR 2022 - [c153]Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Semantic-Based Few-Shot Classification by Psychometric Learning. IDA 2022: 392-403 - [c152]Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, Peter Stone:
Dynamic Sparse Training for Deep Reinforcement Learning. IJCAI 2022: 3437-3443 - [c151]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. LoG 2022: 8 - [c150]Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy:
Phrase-level Textual Adversarial Attack with Label Preservation. NAACL-HLT (Findings) 2022: 1095-1112 - [c149]Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? NeurIPS 2022 - [c148]Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu:
Dynamic Sparse Network for Time Series Classification: Learning What to "See". NeurIPS 2022 - [c147]Dennis Collaris, Hilde J. P. Weerts, Daphne Miedema, Jarke J. van Wijk, Mykola Pechenizkiy:
Characterizing Data Scientists' Mental Models of Local Feature Importance. NordiCHI 2022: 9:1-9:12 - [c146]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks. ECML/PKDD (3) 2022: 85-101 - [c145]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks. ECML/PKDD (1) 2022: 225-241 - [c144]Rianne Margaretha Schouten, Wouter Duivesteijn, Mykola Pechenizkiy:
Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). SDM 2022: 585-593 - [c143]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy:
Superposing many tickets into one: A performance booster for sparse neural network training. UAI 2022: 2267-2277 - [i67]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy:
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. CoRR abs/2202.02643 (2022) - [i66]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Linear Bandits. CoRR abs/2202.06657 (2022) - [i65]Hilde J. P. Weerts, Lambèr Royakkers, Mykola Pechenizkiy:
Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning. CoRR abs/2202.08536 (2022) - [i64]Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, Mykola Pechenizkiy:
Survey on Fair Reinforcement Learning: Theory and Practice. CoRR abs/2205.10032 (2022) - [i63]Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy:
Phrase-level Textual Adversarial Attack with Label Preservation. CoRR abs/2205.10710 (2022) - [i62]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu:
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. CoRR abs/2205.15322 (2022) - [i61]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang:
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. CoRR abs/2207.03620 (2022) - [i60]Zahra Atashgahi, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy:
Memory-free Online Change-point Detection: A Novel Neural Network Approach. CoRR abs/2207.03932 (2022) - [i59]Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy:
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. CoRR abs/2208.10842 (2022) - [i58]Akrati Saxena, George Fletcher, Mykola Pechenizkiy:
FairSNA: Algorithmic Fairness in Social Network Analysis. CoRR abs/2209.01678 (2022) - [i57]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning. CoRR abs/2209.03596 (2022) - [i56]Ricky Maulana Fajri, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy:
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering. CoRR abs/2209.12756 (2022) - [i55]Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? CoRR abs/2211.14627 (2022) - [i54]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. CoRR abs/2211.15335 (2022) - [i53]Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu:
Dynamic Sparse Network for Time Series Classification: Learning What to "see". CoRR abs/2212.09840 (2022) - 2021
- [j47]Ben Halstead, Yun Sing Koh, Patricia Riddle, Russel Pears, Mykola Pechenizkiy, Albert Bifet:
Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency. Data Min. Knowl. Discov. 35(3): 796-836 (2021) - [j46]Xin Du, Lei Sun, Wouter Duivesteijn, Alexander G. Nikolaev, Mykola Pechenizkiy:
Adversarial balancing-based representation learning for causal effect inference with observational data. Data Min. Knowl. Discov. 35(4): 1713-1738 (2021) - [j45]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy:
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. Evol. Comput. 29(3): 391-414 (2021) - [j44]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
SpaceNet: Make Free Space for Continual Learning. Neurocomputing 439: 1-11 (2021) - [j43]Shiwei Liu, Decebal Constantin Mocanu, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei, Mykola Pechenizkiy:
Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. Neural Comput. Appl. 33(7): 2589-2604 (2021) - [j42]Shiwei Liu, Iftitahu Ni'mah, Vlado Menkovski, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Efficient and effective training of sparse recurrent neural networks. Neural Comput. Appl. 33(15): 9625-9636 (2021) - [j41]Toon Calders, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Salvatore Ruggieri:
Introduction to The Special Section on Bias and Fairness in AI. SIGKDD Explor. 23(1): 1-3 (2021) - [c142]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
calibrated adversarial training. ACML 2021: 626-641 - [c141]Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy:
Hierarchical Semantic Segmentation using Psychometric Learning. ACML 2021: 798-813 - [c140]Akrati Saxena, Yulong Pei, Jan Veldsink, Werner van Ipenburg, George Fletcher, Mykola Pechenizkiy:
The banking transactions dataset and its comparative analysis with scale-free networks. ASONAM 2021: 283-296 - [c139]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Self-Attention Meta-Learner for Continual Learning. AAMAS 2021: 1658-1660 - [c138]Ben Halstead, Yun Sing Koh, Patricia Riddle, Russel Pears, Mykola Pechenizkiy, Albert Bifet, Gustavo Olivares, Guy Coulson:
Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification. DSAA 2021: 1-2 - [c137]Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy:
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks. DSAA 2021: 1-2 - [c136]Afrizal Doewes, Mykola Pechenizkiy:
On the Limitations of Human-Computer Agreement in Automated Essay Scoring. EDM 2021 - [c135]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection. EMNLP (Findings) 2021: 1606-1617 - [c134]Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet, Russel Pears:
Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information. ICDE 2021: 1056-1067 - [c133]Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy:
Selfish Sparse RNN Training. ICML 2021: 6893-6904 - [c132]Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. ICML 2021: 6989-7000 - [c131]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. NeurIPS 2021: 9908-9922 - [c130]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. ECML/PKDD (2) 2021: 367-382 - [c129]Hilde Jacoba Petronella Weerts, Mykola Pechenizkiy:
Teaching Responsible Machine Learning to Engineers. Teaching ML 2021: 40-45 - [c128]Akrati Saxena, George Fletcher, Mykola Pechenizkiy:
How Fair is Fairness-aware Representative Ranking? WWW (Companion Volume) 2021: 161-165 - [e9]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e8]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents] - [i52]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Learning Invariant Representation for Continual Learning. CoRR abs/2101.06162 (2021) - [i51]Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy:
Selfish Sparse RNN Training. CoRR abs/2101.09048 (2021) - [i50]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Self-Attention Meta-Learner for Continual Learning. CoRR abs/2101.12136 (2021) - [i49]Akrati Saxena, George Fletcher, Mykola Pechenizkiy:
NodeSim: Node Similarity based Network Embedding for Diverse Link Prediction. CoRR abs/2102.00785 (2021) - [i48]Selima Curci, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Truly Sparse Neural Networks at Scale. CoRR abs/2102.01732 (2021) - [i47]Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. CoRR abs/2102.02887 (2021) - [i46]Akrati Saxena, George Fletcher, Mykola Pechenizkiy:
How Fair is Fairness-aware Representative Ranking and Methods for Fair Ranking. CoRR abs/2103.01335 (2021) - [i45]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks. CoRR abs/2104.07917 (2021) - [i44]Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy:
Direction-Aggregated Attack for Transferable Adversarial Examples. CoRR abs/2104.09172 (2021) - [i43]Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, Peter Stone:
Dynamic Sparse Training for Deep Reinforcement Learning. CoRR abs/2106.04217 (2021) - [i42]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. CoRR abs/2106.10404 (2021) - [i41]Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. CoRR abs/2106.14568 (2021) - [i40]