Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings. - Aleksandar Bojchevski; Yves Matkovic; Stephan Günnemann (KDD, 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)

Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking. - Aleksandar Bojchevski; Stephan Günnemann (CoRR, 2017)

Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. - Aleksandar Bojchevski; Stephan Günnemann (AAAI, 2018)

Anomaly Detection in Car-Booking Graphs. - Oleksandr Shchur; Aleksandar Bojchevski; Mohamed Farghal; Stephan Günnemann; Yusuf Saber (ICDM Workshops, 2018)

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. - Aleksandar Bojchevski; Stephan Günnemann (ICLR (Poster), 2018)

NetGAN: Generating Graphs via Random Walks. - Aleksandar Bojchevski; Oleksandr Shchur; Daniel Zügner; Stephan Günnemann (ICML, 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)

Dual-Primal Graph Convolutional Networks. - Federico Monti; Oleksandr Shchur; Aleksandar Bojchevski; Or Litany; Stephan Günnemann; Michael M. Bronstein (CoRR, 2018)

Adversarial Attacks on Node Embeddings. - 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)

Pitfalls of Graph Neural Network Evaluation. - Oleksandr Shchur; Maximilian Mumme; Aleksandar Bojchevski; Stephan Günnemann (CoRR, 2018)

Predict then Propagate: Graph Neural Networks meet Personalized PageRank. - Johannes Klicpera; Aleksandar Bojchevski; Stephan Günnemann (ICLR (Poster), 2019)

Adversarial Attacks on Node Embeddings via Graph Poisoning. - Aleksandar Bojchevski; Stephan Günnemann (ICML, 2019)

Certifiable Robustness to Graph Perturbations. - Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2019)

Group Centrality Maximization for Large-scale Graphs. - Eugenio Angriman; Alexander van der Grinten; Aleksandar Bojchevski; Daniel Zügner; Stephan Günnemann; Henning Meyerhenke (ALENEX, 2020)

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. - Aleksandar Bojchevski; Johannes Klicpera; Stephan Günnemann (ICML, 2020)

Scaling Graph Neural Networks with Approximate PageRank. - Aleksandar Bojchevski; Johannes Klicpera; Bryan Perozzi; Amol Kapoor; Martin Blais; Benedek Rózemberczki; Michal Lukasik; Stephan Günnemann (KDD, 2020)

Machine Learning on Graphs in the Presence of Noise and Adversaries. - Aleksandar Bojchevski (2020)

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 (AISTATS, 2021)

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. - Jan Schuchardt; Aleksandar Bojchevski; Johannes Klicpera; Stephan Günnemann (ICLR, 2021)

Robustness of Graph Neural Networks at Scale. - Simon Geisler; Tobias Schmidt; Hakan Sirin; Daniel Zügner; Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2021)

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. - Simon Geisler; Johanna Sommer; Jan Schuchardt; Aleksandar Bojchevski; Stephan Günnemann (ICLR, 2022)

Are Defenses for Graph Neural Networks Robust? - Felix Mujkanovic; Simon Geisler; Stephan Günnemann; Aleksandar Bojchevski (NeurIPS, 2022)

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. - Yan Scholten; Jan Schuchardt; Simon Geisler; Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2022)

Adversarial Weight Perturbation Improves Generalization in Graph Neural Network. - Yihan Wu; Aleksandar Bojchevski; Heng Huang (CoRR, 2022)

Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks. - Yihan Wu; Aleksandar Bojchevski; Heng Huang (AAAI, 2023)

Probing Graph Representations. - Mohammad Sadegh Akhondzadeh; Vijay Lingam; Aleksandar Bojchevski (AISTATS, 2023)

Unveiling the sampling density in non-uniform geometric graphs. - Raffaele Paolino; Aleksandar Bojchevski; Stephan Günnemann; Gitta Kutyniok; Ron Levie (ICLR, 2023)

Localized Randomized Smoothing for Collective Robustness Certification. - Jan Schuchardt; Tom Wollschläger; Aleksandar Bojchevski; Stephan Günnemann (ICLR, 2023)

Conformal Prediction Sets for Graph Neural Networks. - Soroush H. Zargarbashi; Simone Antonelli; Aleksandar Bojchevski (ICML, 2023)

Are GATs Out of Balance? - Nimrah Mustafa; Aleksandar Bojchevski; Rebekka Burkholz (NeurIPS, 2023)

Hierarchical Randomized Smoothing. - Yan Scholten; Jan Schuchardt; Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2023)

Rethinking Label Poisoning for GNNs: Pitfalls and Attacks. - Vijay Lingam; Mohammad Sadegh Akhondzadeh; Aleksandar Bojchevski (ICLR, 2024)

Conformal Inductive Graph Neural Networks. - Soroush H. Zargarbashi; Aleksandar Bojchevski (ICLR, 2024)

Robust Yet Efficient Conformal Prediction Sets. - Soroush H. Zargarbashi; Mohammad Sadegh Akhondzadeh; Aleksandar Bojchevski (ICML, 2024)

SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors. - Vijay Lingam; Atula Neerkaje; Aditya Vavre; Aneesh Shetty; Gautham Krishna Gudur; Joydeep Ghosh; Eunsol Choi; Alex Dimakis; Aleksandar Bojchevski; Sujay Sanghavi (NeurIPS, 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)

SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids. - Salah Ghamizi; Aleksandar Bojchevski; Aoxiang Ma; Jun Cao (CoRR, 2024)

Robust Conformal Prediction with a Single Binary Certificate. - Soroush H. Zargarbashi; Aleksandar Bojchevski (ICLR, 2025)

Node-Level Data Valuation on Graphs. - Simone Antonelli; Aleksandar Bojchevski (Trans. Mach. Learn. Res., 2025)

KurTail : Kurtosis-based LLM Quantization. - Mohammad Sadegh Akhondzadeh; Aleksandar Bojchevski; Evangelos Eleftheriou; Martino Dazzi (CoRR, 2025)

Optimal Conformal Prediction under Epistemic Uncertainty. - Alireza Javanmardi; Soroush H. Zargarbashi; Santo M. A. R. Thies; Willem Waegeman; Aleksandar Bojchevski; Eyke Hüllermeier (CoRR, 2025)

Evaluating the transferability of adversarial robustness to target domains. - Anna-Kathrin Kopetzki; Aleksandar Bojchevski; Stephan Günnemann (Knowl. Inf. Syst., 2025)

One Sample is Enough to Make Conformal Prediction Robust. - Soroush H. Zargarbashi; Mohammad Sadegh Akhondzadeh; Aleksandar Bojchevski (CoRR, 2025)

EvA: Evolutionary Attacks on Graphs. - Mohammad Sadegh Akhondzadeh; Soroush H. Zargarbashi; Jimin Cao; Aleksandar Bojchevski (CoRR, 2025)