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Richard Duong

2 accepted papers

2026

Adapting Noise to Data: Generative Flows from Learned 1D Processes

ICML 2026poster

The default Gaussian latent in flow-based generative models poses challenges when learning certain distributions such as heavy-tailed ones. We introduce a general framework for learning data-adaptive latent distributions using one-dimensional quantile functions, optimized via the Wasserstein distanc…

Cited by 0SourceScholar
2025

Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry

ICLR 2025poster

We deal with the task of sampling from an unnormalized Boltzmann density $\rho_D$ by learning a Boltzmann curve given by energies $f_t$ starting in a simple density $\rho_Z$. First, we examine conditions under which Fisher-Rao flows are absolutely continuous in the Wasserstein geometry. Second, we a…

Cited by 4SourcePDFScholar