Pechenizkiy, Mykola

64 publications

ECML-PKDD 2025 Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well Xin Du, Sikun Yang, Wouter Duivesteijn, Mykola Pechenizkiy

ICLR 2025 Dynamic Sparse Training Versus Dense Training: The Unexpected Winner in Image Corruption Robustness Boqian Wu, Qiao Xiao, Shunxin Wang, Nicola Strisciuglio, Mykola Pechenizkiy, Maurice van Keulen, Decebal Constantin Mocanu, Elena Mocanu

ICLR 2025 HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents Tristan Tomilin, Meng Fang, Mykola Pechenizkiy

ECML-PKDD 2025 Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IX Inês Dutra, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama

ECML-PKDD 2025 Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part X Inês Dutra, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama

ECML-PKDD 2025 Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part VIII Bernhard Pfahringer, Nathalie Japkowicz, Pedro Larrañaga, Rita P. Ribeiro, Inês Dutra, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama

ICML 2025 Preference Controllable Reinforcement Learning with Advanced Multi-Objective Optimization Yucheng Yang, Tianyi Zhou, Mykola Pechenizkiy, Meng Fang

NeurIPS 2025 REOBench: Benchmarking Robustness of Earth Observation Foundation Models Xiang Li, Yong Tao, Siyuan Zhang, Siwei Liu, Zhitong Xiong, Chunbo Luo, Lu Liu, Mykola Pechenizkiy, Xiao Xiang Zhu, Tianjin Huang

TMLR 2025 Rethinking Knowledge Transfer in Learning Using Privileged Information Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Clemens Kaptein, Mykola Pechenizkiy

ICLR 2025 RuAG: Learned-Rule-Augmented Generation for Large Language Models 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

AAAI 2025 Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective Can Jin, Tianjin Huang, Yihua Zhang, Mykola Pechenizkiy, Sijia Liu, Shiwei Liu, Tianlong Chen

NeurIPSW 2024 A Causality-Inspired Spatial-Temporal Return Decomposition Approach for Multi-Agent Reinforcement Learning Yudi Zhang, Yali Du, Biwei Huang, Meng Fang, Mykola Pechenizkiy

ECML-PKDD 2024 Adaptive Sparsity Level During Training for Efficient Time Series Forecasting with Transformers Zahra Atashgahi, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu

JAIR 2024 Can Fairness Be Automated? Guidelines and Opportunities for Fairness-Aware AutoML Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter

NeurIPS 2024 E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu

ICML 2024 Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret with Posterior Sampling Danil Provodin, Maurits Clemens Kaptein, Mykola Pechenizkiy

ECML-PKDD 2024 Exceptional Subitizing Patterns: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression Rianne Margaretha Schouten, Wouter Duivesteijn, Pekka Räsänen, Jacob M. Paul, Mykola Pechenizkiy

ICMLW 2024 Exploring the Development of Complexity over Depth and Time in Deep Neural Networks Hannah Pinson, Aurélien Boland, Vincent Ginis, Mykola Pechenizkiy

AAAI 2024 Large Language Models Are Neurosymbolic Reasoners Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang

NeurIPSW 2024 On Adversarial Robustness of Language Models in Transfer Learning Bohdan Turbal, Anastasiia Mazur, Jiaxu Zhao, Mykola Pechenizkiy

ICML 2024 Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity 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

ICLRW 2024 Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity 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

NeurIPSW 2024 Rethinking Knowledge Transfer in Learning Using Privileged Information Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Clemens Kaptein, Mykola Pechenizkiy

ECML-PKDD 2024 Subgroup Harm Assessor: Identifying Potential Fairness-Related Harms and Predictive Bias Adam Dubowski, Hilde J. P. Weerts, Anouk Wolters, Mykola Pechenizkiy

AISTATS 2024 Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks Kaiting Liu, Zahra Atashgahi, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu

ICLR 2024 Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning Yucheng Yang, Tianyi Zhou, Qiang He, Lei Han, Mykola Pechenizkiy, Meng Fang

ICML 2023 Are Large Kernels Better Teachers than Transformers for ConvNets? Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu

NeurIPS 2023 COOM: A Game Benchmark for Continual Reinforcement Learning Tristan Tomilin, Meng Fang, Yudi Zhang, Mykola Pechenizkiy

NeurIPS 2023 Dynamic Sparsity Is Channel-Level Sparsity Learner Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang "Atlas" Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu

ECML-PKDD 2023 Enhancing Adversarial Training via Reweighting Optimization Trajectory Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy

NeurIPSW 2023 Generating Privacy-Preserving Longitudinal Synthetic Data Robin van Hoorn, Tom Bakkes, Zoi Tokoutsi, Ymke de Jong, R. Arthur Bouwman, Mykola Pechenizkiy

NeurIPS 2023 Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy

AAAI 2023 Lottery Pools: Winning More by Interpolating Tickets Without Increasing Training or Inference Cost Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy

ICLR 2023 More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 Using Sparsity Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi Kärkkäinen, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang

ECML-PKDD 2023 REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training Jiaxu Zhao, Lu Yin, Shiwei Liu, Meng Fang, Mykola Pechenizkiy

TMLR 2023 Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu

MLJ 2022 A Brain-Inspired Algorithm for Training Highly Sparse Neural Networks Zahra Atashgahi, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy

MLJ 2022 Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification Ben Halstead, Yun Sing Koh, Patricia Riddle, Russel Pears, Mykola Pechenizkiy, Albert Bifet, Gustavo Olivares, Guy Coulson

ECML-PKDD 2022 Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy

ICLR 2022 Deep Ensembling with No Overhead for Either Training or Testing: The All-Round Blessings of Dynamic Sparsity Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu

NeurIPS 2022 Dynamic Sparse Network for Time Series Classification: Learning What to “See” Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu

IJCAI 2022 Dynamic Sparse Training for Deep Reinforcement Learning Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, Peter Stone

ECML-PKDD 2022 Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy

MLJ 2022 Quick and Robust Feature Selection: The Strength of Energy-Efficient Sparse Training for Autoencoders Zahra Atashgahi, Ghada Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy

MLJ 2022 ResGCN: Attention-Based Deep Residual Modeling for Anomaly Detection on Attributed Networks Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy

UAI 2022 Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy

ICLR 2022 The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy

NeurIPS 2022 Where to Pay Attention in Sparse Training for Feature Selection? Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu

LoG 2022 You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu

ACML 2021 Calibrated Adversarial Training Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

ICML 2021 Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

ACML 2021 Hierarchical Semantic Segmentation Using Psychometric Learning Lu Yin, Vlado Menkovski, Shwei Liu, Mykola Pechenizkiy

ECML-PKDD 2021 On Generalization of Graph Autoencoders with Adversarial Training Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy

ICML 2021 Selfish Sparse RNN Training Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy

NeurIPS 2021 Sparse Training via Boosting Pruning Plasticity with Neuroregeneration Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu

AAAI 2020 Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy

ECML-PKDD 2020 Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing Lu Yin, Vlado Menkovski, Mykola Pechenizkiy

ECML-PKDD 2020 PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data Ricky Maulana Fajri, Samaneh Khoshrou, Robert Peharz, Mykola Pechenizkiy

ECML-PKDD 2020 Topological Insights into Sparse Neural Networks Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu

IJCAI 2018 DyNMF: Role Analytics in Dynamic Social Networks Yulong Pei, Jianpeng Zhang, George H. L. Fletcher, Mykola Pechenizkiy

ECML-PKDD 2017 Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining Wouter Duivesteijn, Tara Farzami, Thijs Putman, Evertjan Peer, Hilde J. P. Weerts, Jasper N. Adegeest, Gerson Foks, Mykola Pechenizkiy

ECML-PKDD 2016 Finding Incident-Related Social Media Messages for Emergency Awareness Alexander Nieuwenhuijse, Jorn Bakker, Mykola Pechenizkiy

ECML-PKDD 2006 Dynamic Integration with Random Forests Alexey Tsymbal, Mykola Pechenizkiy, Padraig Cunningham

IJCAI 2005 Sequential Genetic Search for Ensemble Feature Selection Alexey Tsymbal, Mykola Pechenizkiy, Padraig Cunningham