9 citations · 25 across the 10 of their papers we have counts for
12 papers
Averaged Mirror Descent and Dual Gradient Methods: Convergent Algorithms for Entropic Gromov-Wasserstein Problems
Joanna Marks, Joanna Mark, Gabriel Rioux +2
The Gromov-Wasserstein (GW) distance measures the discrepancy between metric measure (mm) spaces and identifies optimal alignments between them based solely on their intrinsic stru…
Discrete Gromov-Wasserstein Duality: Algorithms and Isomorphism Testing
Gabriel Rioux, Joanna Marks, Riccardo Passeggeri +1
The Gromov-Wasserstein (GW) distance provides a principled framework for aligning metric measure (mm) spaces based solely on their intrinsic structure. Its ability to identify isom…
On the Computational and Statistical Efficiency of the Empirical Maximum Entropy on the Mean Method
Matthew King-Roskamp, Gabriel Rioux, Rustum Choksi +1
The Maximum Entropy on the Mean (MEM) method provides a flexible computational framework for solving inverse problems by combining data fidelity with entropy-based regularization.…
Sliced Inner Product Gromov-Wasserstein Distances
Xiaoyun Gong, Gabriel Rioux, Ziv Goldfeld
The Gromov-Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical and computational scaling rem…
Approximation Analysis of the Entropic Penalty in Quadratic Programming
Venkatkrishna Karumanchi, Gabriel Rioux, Ziv Goldfeld
Quadratic assignment problems are a fundamental class of combinatorial optimization problems which are ubiquitous in applications, yet their exact resolution is NP-hard. To circumv…
Limit Laws for Gromov-Wasserstein Alignment with Applications to Testing Graph Isomorphisms
Gabriel Rioux, Ziv Goldfeld, Kengo Kato
The Gromov-Wasserstein (GW) distance enables comparing metric measure spaces based solely on their internal structure, making it invariant to isomorphic transformations. This prope…
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
Gabriel Rioux, Apoorva Nitsure, Mattia Rigotti +2
Stochastic dominance is an important concept in probability theory, econometrics and social choice theory for robustly modeling agents' preferences between random outcomes. While m…
Entropic Gromov-Wasserstein Distances: Stability and Algorithms
Gabriel Rioux, Ziv Goldfeld, Kengo Kato
The Gromov-Wasserstein (GW) distance quantifies discrepancy between metric measure spaces and provides a natural framework for aligning heterogeneous datasets. Alas, as exact compu…
Limit Theorems for Entropic Optimal Transport Maps and the Sinkhorn Divergence
Ziv Goldfeld, Kengo Kato, Gabriel Rioux +1
We study limit theorems for entropic optimal transport (EOT) maps, dual potentials, and the Sinkhorn divergence. The key technical tool we use is a first and second-order Hadamard…
Statistical inference with regularized optimal transport
Ziv Goldfeld, Kengo Kato, Gabriel Rioux +1
Optimal transport (OT) is a versatile framework for comparing probability measures, with many applications to statistics, machine learning, and applied mathematics. However, OT dis…
Limit distribution theory for smooth -Wasserstein distances
Ziv Goldfeld, Kengo Kato, Sloan Nietert +1
The Wasserstein distance is a metric on a space of probability measures that has seen a surge of applications in statistics, machine learning, and applied mathematics. However, sta…
The Maximum Entropy on the Mean Method for Image Deblurring
Gabriel Rioux, Rustum Choksi, Tim Hoheisel +2
Image deblurring is a notoriously challenging ill-posed inverse problem. In recent years, a wide variety of approaches have been proposed based upon regularization at the level of…