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08/10/2023
Unifying Distributionally Robust Optimization via Optimal Transport Theory
In the past few years, there has been considerable interest in two promi...
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03/12/2023
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
In this work, we present the Bregman Alternating Projected Gradient (BAP...
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02/09/2023
Outlier-Robust Gromov Wasserstein for Graph Data
Gromov Wasserstein (GW) distance is a powerful tool for comparing and al...
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01/30/2023
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
Graph alignment, which aims at identifying corresponding entities across...
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12/26/2022
Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization
Nonconvex-nonconcave minimax optimization has been the focus of intense ...
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11/28/2022
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls
The optimal design of experiments typically involves solving an NP-hard ...
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10/04/2022
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Distributionally robust optimization has been shown to offer a principle...
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09/22/2022
Nonsmooth Composite Nonconvex-Concave Minimax Optimization
Nonconvex-concave minimax optimization has received intense interest in ...
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05/31/2022
Rethinking Graph Neural Networks for Anomaly Detection
Graph Neural Networks (GNNs) are widely applied for graph anomaly detect...
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05/17/2022
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning
In this paper, we study the design and analysis of a class of efficient ...
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01/28/2022
Learning Proximal Operators to Discover Multiple Optima
Finding multiple solutions of non-convex optimization problems is a ubiq...
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07/12/2021
A Splitting Scheme for Flip-Free Distortion Energies
We introduce a robust optimization method for flip-free distortion energ...
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10/24/2020
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
Wasserstein Distributionally Robust Optimization (DRO) is concerned with...
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06/26/2020
Understanding Notions of Stationarity in Non-Smooth Optimization
Many contemporary applications in signal processing and machine learning...
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12/31/2019
The Gambler's Problem and Beyond
We analyze the Gambler's problem, a simple reinforcement learning proble...
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10/28/2019
A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression
Wasserstein distance-based distributionally robust optimization (DRO) ha...
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05/09/2018