1 citations · 2 across the 7 of their papers we have counts for
12 papers
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…
Moment Sum-of-Squares Hierarchy for Gromov Wasserstein: Continuous Extensions and Sample Complexity
Hoang Anh Tran, Binh Tuan Nguyen, Yong Sheng Soh
The Gromov-Wasserstein (GW) problem is an extension of the classical optimal transport problem to settings where the source and target distributions reside in incomparable spaces,…
Sum-of-Squares Hierarchy for the Gromov Wasserstein Problem
Hoang Anh Tran, Binh Tuan Nguyen, Yong Sheng Soh
The Gromov-Wasserstein (GW) problem is a variant of the classical optimal transport problem that allows one to compute meaningful transportation plans between incomparable spaces.…
Evaluating Policy Effects through Opinion Dynamics and Network Sampling
Eugene T. Y. Ang, Yong Sheng Soh
In the process of enacting or introducing a new policy, policymakers frequently consider the population's responses. These considerations are critical for effective governance. The…
Exactness Conditions for Semidefinite Relaxations of the Quadratic Assignment Problem
Junyu Chen, Yong Sheng Soh
The Quadratic Assignment Problem (QAP) is an important discrete optimization instance that encompasses many well-known combinatorial optimization problems, and has applications in…
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…