Probability theory: limit theorems for Markov chains, large deviation theory, stochastic
processes and stochastic ODEs
Dynamical systems and ergodic theory
Statistics with applications to data science
Machine Learning interpretability
A. Miroshnikov, K. Kotsiopoulos, R. Franks and A. Ravi Kannan, “Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics”, (2021), arxiv:2111.11259.
A. Miroshnikov, K. Kotsiopoulos, and A. Ravi Kannan, “Mutual information-based group explainers with coalition structure for machine learning model explanations”, (2021), arxiv:2102.10878.
A. Miroshnikov, K. Kotsiopoulos, R. Franks and A. Ravi Kannan, “Wasserstein-based fairness interpretability framework for machine learning models”, (2020), arxiv:2011.03156.
A. Miroshnikov, K. Kotsiopoulos and E. Conlon, “Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods“, Submitted (2018), arxiv:1710.09071.