3 papers
math.OC2025
Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh
Regularization is a central tool for addressing ill-posedness in inverse problems and statistical estimation, with the choice of a suitable penalty often determining the reliabilit…
cs.LG2025
The Uniformly Rotated Mondrian Kernel
Calvin Osborne, Eliza O'Reilly
Random feature maps are used to decrease the computational cost of kernel machines in large-scale problems. The Mondrian kernel is one such example of a fast random feature approxi…
cs.LG2024
The Star Geometry of Critic-Based Regularizer Learning
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh
Variational regularization is a classical technique to solve statistical inference tasks and inverse problems, with modern data-driven approaches parameterizing regularizers via de…