Mladen Kolar
Associate Professor of Econometrics and Statistics
University of Chicago Booth School of Business
Principal Investigator
Mladen Kolar is an Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research is focused on high-dimensional statistical methods, graphical models, varying-coefficient models and data mining, driven by the need to uncover interesting and scientifically meaningful structures from observational data.
Education
PhD in Machine Learning
2013
Carnegie Mellon University
Diploma in Computer Engineering
2006
University of Zagreb, Faculty of Electrical Engineering and Computing
Interests
Statistical machine learning
Probabilistic graphical models
Dynamic networks estimation
High-dimensional estimation and inference
Stochastic optimization with constraints
Distributed optimization and federated learning
Selected Publications
An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians
Mathematical Programming
Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems
Proceedings of the 2022 ACM SIGMETRICS
Two-sample inference for high-dimensional Markov networks
Journal of the Royal Statistical Society. Series B. Statistical Methodology, 83(5)
Estimating differential latent variable graphical models with applications to brain connectivity
Biometrika, 108(2)
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
Technical report
Kernel meets sieve: post-regularization confidence bands for sparse additive model
Journal of the American Statistical Association, 115(532)
Direct Estimation of Differential Functional Graphical Models
Advances in Neural Information Processing Systems, 32
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
Journal of Machine Learning Research
Contact
Visit Us
Harper Center, Suite 338
5807 South Woodlawn Ave
Chicago, IL 60637
United States