5 papers
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
Thejani Gamage, Hyemin Gu, Zhizhen Zhang +3
We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capt…
Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences
Ziyu Chen, Markos A. Katsoulakis, Benjamin J. Zhang
We introduce a novel Wasserstein-1 () path-space divergence for stochastic and deterministic dynamics and establish a Wasserstein Uncertainty Propagation (WUP) theorem that bo…
CurvZO: Adaptive Curvature-Guided Sparse Zeroth-Order Optimization for Efficient LLM Fine-Tuning
Shuo Wang, Ziyu Chen, Ming Tang
Fine-tuning large language models (LLMs) with backpropagation achieves high performance but incurs substantial memory overhead, limiting scalability on resource-constrained hardwar…
JEPA-Reasoner: Decoupling Latent Reasoning from Token Generation
Bingyang Kelvin Liu, Ziyu Patrick Chen, David P. Woodruff
Current autoregressive language models couple high-level reasoning and low-level token generation into a single sequential process, making the reasoning trajectory vulnerable to co…
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…