1 citations · 1 across the 6 of their papers we have counts for
6 papers
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
Thejani Gamage, Hyemin Gu, Zhizhen Zhang +4
In many high-stakes domains, extreme events carry substantial consequences, yet learning the heavy-tailed distributions that govern them from finite samples remains challenging: th…
Certifying Residual Architectures from Their Primitives: A Sharp Stability Threshold
Hyemin Gu, Michael Tyrrell, Tuhin Sahai +1
Whether a deep residual architecture trains stably is usually determined by training it, which is expensive and answers the question only for the architecture, depth, and floating-…
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
Zhizhen Zhang, Hyemin Gu, Benjamin J. Zhang +6
Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce ISOMORPH, the first public di…
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows
Hyemin Gu, Markos A. Katsoulakis, Luc Rey-Bellet +1
We formulate well-posed continuous-time generative flows for learning distributions that are supported on low-dimensional manifolds through Wasserstein proximal regularizations of…
Robust Generative Learning with Lipschitz-Regularized -Divergences Allows Minimal Assumptions on Target Distributions
Ziyu Chen, Hyemin Gu, Markos A. Katsoulakis +2
This paper demonstrates the robustness of Lipschitz-regularized -divergences as objective functionals in generative modeling, showing they enable stable learning across a wide r…
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
Hyemin Gu, Panagiota Birmpa, Yannis Pantazis +2
We build a new class of generative algorithms capable of efficiently learning an arbitrary target distribution from possibly scarce, high-dimensional data and subsequently generate…