9 citations · 21 across the 19 of their papers we have counts for
21 papers
Quadratic Optimization over Probability Measures with Coupling Constraints
Hoang Anh Tran, Yong Sheng Soh
We consider solving an optimization instance in which the objective is quadratic and where the decision variable is a probability measure. Our class of problems are motivated by ap…
Fairness-Guaranteed Online Power Allocation Policies for EV Fast Charging Stations
Can Berk Saner, Yong-Sheng Soh, Antonios Varvitsiotis
The rapid expansion of electric vehicles (EVs) necessitates scalable fast charging station (FCS) infrastructure. These stations are often oversubscribed, with total port rating exc…
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…
Semidefinite Relaxations of the Gromov-Wasserstein Distance
Junyu Chen, Binh T. Nguyen, Shang Hui Koh +1
The Gromov-Wasserstein (GW) distance is an extension of the optimal transport problem that allows one to match objects between incomparable spaces. At its core, the GW distance is…
The Lovász Theta Function for Recovering Planted Clique Covers and Graph Colorings
Jiaxin Hou, Yong Sheng Soh, Antonios Varvitsiotis
The problems of computing graph colorings and clique covers are central challenges in combinatorial optimization. Both of these are known to be NP-hard, and thus computationally in…
Dictionary Learning under Symmetries via Group Representations
Subhroshekhar Ghosh, Aaron Y. R. Low, Yong Sheng Soh +2
The dictionary learning problem can be viewed as a data-driven process to learn a suitable transformation so that data is sparsely represented directly from example data. In this p…
Optimal Regularization for a Data Source
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh +1
In optimization-based approaches to inverse problems and to statistical estimation, it is common to augment criteria that enforce data fidelity with a regularizer that promotes des…
Multiplicative updates for symmetric-cone factorizations
Yong Sheng Soh, Antonios Varvitsiotis
Given a matrix with non-negative entries, the cone factorization problem over a cone concerns computing $\{ a_1,…
A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations
Yong Sheng Soh, Antonios Varvitsiotis
Given a matrix with nonnegative entries, a Positive Semidefinite (PSD) factorization of is a collection of -dimensional PSD matrices…
An Interpretable Intensive Care Unit Mortality Risk Calculator
Eugene T. Y. Ang, Milashini Nambiar, Yong Sheng Soh +1
Mortality risk is a major concern to patients have just been discharged from the intensive care unit (ICU). Many studies have been directed to construct machine learning models to…
Group Invariant Dictionary Learning
Yong Sheng Soh
The dictionary learning problem concerns the task of representing data as sparse linear sums drawn from a smaller collection of basic building blocks. In application domains where…
Collaborative Inference for Efficient Remote Monitoring
Chi Zhang, Yong Sheng Soh, Ling Feng +2
While current machine learning models have impressive performance over a wide range of applications, their large size and complexity render them unsuitable for tasks such as remote…
A Note on Convex Relaxations for the Inverse Eigenvalue Problem
Utkan Candogan, Yong Sheng Soh, Venkat Chandrasekaran
The affine inverse eigenvalue problem consists of identifying a real symmetric matrix with a prescribed set of eigenvalues in an affine space. Due to its ubiquity in applications,…
Fitting Tractable Convex Sets to Support Function Evaluations
Yong Sheng Soh, Venkat Chandrasekaran
The geometric problem of estimating an unknown compact convex set from evaluations of its support function arises in a range of scientific and engineering applications. Traditional…
Learning Semidefinite Regularizers
Yong Sheng Soh, Venkat Chandrasekaran
Regularization techniques are widely employed in optimization-based approaches for solving ill-posed inverse problems in data analysis and scientific computing. These methods are b…
High-Dimensional Change-Point Estimation: Combining Filtering with Convex Optimization
Yong Sheng Soh, Venkat Chandrasekaran
We consider change-point estimation in a sequence of high-dimensional signals given noisy observations. Classical approaches to this problem such as the filtered derivative method…