A Statistical Performance Analysis of Graph Clustering Algorithms. - Pierre Miasnikof; Alexander Y. Shestopaloff; Anthony J. Bonner; Yuri A. Lawryshyn (WAW, 2018)

Replica Conditional Sequential Monte Carlo. - Alexander Y. Shestopaloff; Arnaud Doucet (ICML, 2019)

A Statistical Test of Heterogeneous Subgraph Densities to Assess Clusterability. - Pierre Miasnikof; Liudmila Prokhorenkova; Alexander Y. Shestopaloff; Andrei M. Raigorodskii (LION, 2019)

A Statistical Density-Based Analysis of Graph Clustering Algorithm Performance. - Pierre Miasnikof; Alexander Y. Shestopaloff; Anthony J. Bonner; Yuri A. Lawryshyn; Panos M. Pardalos (CoRR, 2019)

Distances on a Graph. - Pierre Miasnikof; Alexander Y. Shestopaloff; Leonidas S. Pitsoulis; Alexander Ponomarenko; Yuri A. Lawryshyn (COMPLEX NETWORKS (1), 2020)

A density-based statistical analysis of graph clustering algorithm performance. - Pierre Miasnikof; Alexander Y. Shestopaloff; Anthony J. Bonner; Yuri A. Lawryshyn; Panos M. Pardalos; Ernesto Estrada (J. Complex Networks, 2020)

Graph Distances and Clustering. - Pierre Miasnikof; Alexander Y. Shestopaloff; Leonidas S. Pitsoulis; Yuri A. Lawryshyn (CoRR, 2020)

An empirical comparison of connectivity-based distances on a graph and their computational scalability. - Pierre Miasnikof; Alexander Y. Shestopaloff; Leonidas S. Pitsoulis; Alexander Ponomarenko (J. Complex Networks, 2021)

Statistical Network Similarity. - Pierre Miasnikof; Alexander Y. Shestopaloff; Cristián Bravo; Yuri A. Lawryshyn (COMPLEX NETWORKS (2), 2022)

Statistical network isomorphism. - Pierre Miasnikof; Alexander Y. Shestopaloff; Cristián Bravo; Yuri A. Lawryshyn (CoRR, 2022)

Low-rank extended Kalman filtering for online learning of neural networks from streaming data. - Peter G. Chang; Gerardo Duràn-Martín; Alexander Y. Shestopaloff; Matt Jones 002; Kevin Patrick Murphy (CoLLAs, 2023)

Empirical Study of Graph Spectra and Their Limitations. - Pierre Miasnikof; Alexander Y. Shestopaloff; Cristián Bravo; Yuri A. Lawryshyn (COMPLEX NETWORKS (1), 2023)

Dynamic Time Warping for Lead-Lag Relationship Detection in Lagged Multi-Factor Models. - Yichi Zhang; Mihai Cucuringu; Alexander Y. Shestopaloff; Stefan Zohren (ICAIF, 2023)

Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models. - Yichi Zhang; Mihai Cucuringu; Alexander Y. Shestopaloff; Stefan Zohren (CoRR, 2023)

Statistical power, accuracy, reproducibility and robustness of a graph clusterability test. - Pierre Miasnikof; Alexander Y. Shestopaloff; Andrei M. Raigorodskii (Int. J. Data Sci. Anal., 2023)

Outlier-robust Kalman Filtering through Generalised Bayes. - Gerardo Duran-Martin; Matías Altamirano; Alexander Y. Shestopaloff; Leandro Sánchez-Betancourt; Jeremias Knoblauch; Matt Jones 002; François-Xavier Briol; Kevin Patrick Murphy (ICML, 2024)

Stock Volume Forecasting with Advanced Information by Conditional Variational Auto-Encoder. - Parley R. Yang; Alexander Y. Shestopaloff (CoRR, 2024)

BONE: a unifying framework for Bayesian online learning in non-stationary environments. - Gerardo Duran-Martin; Leandro Sánchez-Betancourt; Alexander Y. Shestopaloff; Kevin Murphy 002 (CoRR, 2024)

A unifying framework for generalised Bayesian online learning in non-stationary environments. - Gerardo Duran-Martin; Leandro Sánchez-Betancourt; Alexander Y. Shestopaloff; Kevin Patrick Murphy (Trans. Mach. Learn. Res., 2025)

Generalized Factor Neural Network Model for High-dimensional Regression. - Zichuan Guo; Mihai Cucuringu; Alexander Y. Shestopaloff (CoRR, 2025)

A clusterability test for directed graphs. - Mario R. Guarracino; Pierre Miasnikof; Alexander Y. Shestopaloff; Houyem Demni; Cristián Bravo; Yuri A. Lawryshyn (CoRR, 2025)

Uncertainty Quantification in Bayesian Reduced-Rank Sparse Regressions. - Maria F. Pintado; Matteo Iacopini; Luca Rossini; Alexander Y. Shestopaloff (Stat. Comput., 2025)