Aleksandar Bojchevski

According to our database, Aleksandar Bojchevski authored at least 38 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids.

Salah Ghamizi, Aleksandar Bojchevski, Aoxiang Ma, Jun Cao

CoRR, 2024

SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors.

Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi

CoRR, 2024

Robust Yet Efficient Conformal Prediction Sets.

Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Conformal Inductive Graph Neural Networks.

Soroush H. Zargarbashi, Aleksandar Bojchevski

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Rethinking Label Poisoning for GNNs: Pitfalls and Attacks.

Vijay Lingam, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Hierarchical Randomized Smoothing.

Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Are GATs Out of Balance?

Nimrah Mustafa, Aleksandar Bojchevski, Rebekka Burkholz

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Conformal Prediction Sets for Graph Neural Networks.

Soroush H. Zargarbashi, Simone Antonelli, Aleksandar Bojchevski

Proceedings of the International Conference on Machine Learning, 2023

Localized Randomized Smoothing for Collective Robustness Certification.

Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Unveiling the sampling density in non-uniform geometric graphs.

Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Probing Graph Representations.

Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks.

Yihan Wu, Aleksandar Bojchevski, Heng Huang

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Adversarial Weight Perturbation Improves Generalization in Graph Neural Network.

Yihan Wu, Aleksandar Bojchevski, Heng Huang

CoRR, 2022

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks.

Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Are Defenses for Graph Neural Networks Robust?

Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness.

Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Robustness of Graph Neural Networks at Scale.

Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks.

Jan Schuchardt, Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann

Proceedings of the 9th International Conference on Learning Representations, 2021

Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions.

Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Machine Learning on Graphs in the Presence of Noise and Adversaries.

Aleksandar Bojchevski

PhD thesis, 2020

Scaling Graph Neural Networks with Approximate PageRank.

Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More.

Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann

Proceedings of the 37th International Conference on Machine Learning, 2020

Group Centrality Maximization for Large-scale Graphs.

Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke

Proceedings of the Symposium on Algorithm Engineering and Experiments, 2020

2019

Certifiable Robustness to Graph Perturbations.

Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adversarial Attacks on Node Embeddings via Graph Poisoning.

Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the 36th International Conference on Machine Learning, 2019

Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Pitfalls of Graph Neural Network Evaluation.

Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann

CoRR, 2018

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank.

Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann

CoRR, 2018

Adversarial Attacks on Node Embeddings.

Aleksandar Bojchevski, Stephan Günnemann

CoRR, 2018

Dual-Primal Graph Convolutional Networks.

Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein

CoRR, 2018

LocText: relation extraction of protein localizations to assist database curation.

Juan Miguel Cejuela, Shrikant Vinchurkar, Tatyana Goldberg, Madhukar Sollepura Prabhu Shankar, Ashish Baghudana, Aleksandar Bojchevski, Carsten Uhlig, André Ofner, Pandu Raharja-Liu, Lars Juhl Jensen, Burkhard Rost

BMC Bioinform., 2018

NetGAN: Generating Graphs via Random Walks.

Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

Proceedings of the 35th International Conference on Machine Learning, 2018

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking.

Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the 6th International Conference on Learning Representations, 2018

Anomaly Detection in Car-Booking Graphs.

Oleksandr Shchur, Aleksandar Bojchevski, Mohamed Farghal, Stephan Günnemann, Yusuf Saber

Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure.

Aleksandar Bojchevski, Stephan Günnemann

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking.

Aleksandar Bojchevski, Stephan Günnemann

CoRR, 2017

nala: text mining natural language mutation mentions.

Juan Miguel Cejuela, Aleksandar Bojchevski, Carsten Uhlig, Rustem Bekmukhametov, Sanjeev Kumar Karn, Shpend Mahmuti, Ashish Baghudana, Ankit Dubey, Venkata P. Satagopam, Burkhard Rost

Bioinform., 2017

Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings.

Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017