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Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
Introduces Newton's method for solving non-linear equations iteratively, highlighting its fast convergence but also its potential failure to converge in some cases.
Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.
Explores multilinear regression for design optimization and orthogonality, covering teamwork, abstracts, linear and quadratic models, ANOVA, and alias structures.
Covers characterizing the Reserve Provision Capability Area of Active Distribution Networks and discusses collaboration with Swissgrid, linear modeling for DERS, and scenario-based robust optimization methods.