6 papers
Equilibrium and Pricing in Consumer Networks with Nonlinear Utilities: An Online Shape-Constrained Learning Approach
Daniele Bracale, George Michailidis
We study optimal monopoly pricing over consumer networks governed by general nonlinear utilities. In our framework, a consumer's utility is jointly determined by an individualized…
Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data
Jialiang Geng, George Michailidis
The paper studies the problem of detecting and locating change points in multivariate time-evolving data. The problem has a long history in statistics and signal processing and var…
Deep Learning-based Approaches for State Space Models: A Selective Review
Jiahe Lin, George Michailidis
State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of…
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
Jiahe Lin, Huitian Lei, George Michailidis
Granger causality has been widely used in various application domains to capture lead-lag relationships amongst the components of complex dynamical systems, and the focus in extant…
Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees
Jiahe Lin, Huitian Lei, George Michailidis
Structural discovery amongst a set of variables is of interest in both static and dynamic settings. In the presence of lead-lag dependencies in the data, the dynamics of the system…
Cardinality Constrained Mean-Variance Portfolios: A Penalty Decomposition Algorithm
Ahmad Mousavi, George Michailidis
The cardinality-constrained mean-variance portfolio problem has garnered significant attention within contemporary finance due to its potential for achieving low risk while effecti…