Research

Research Interests

• 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

Publications

1. 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.
2. 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.
3. A. Miroshnikov, K. Kotsiopoulos, R. Franks and A. Ravi Kannan, “Wasserstein-based fairness interpretability framework for machine learning models”, (2020), arxiv:2011.03156.
4. 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.