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Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs
Bayesian Optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications.
Convergence guarantee for consistency models
We provide the first convergence guarantees for the Consistency Models (CMs), a newly emerging type of one-step generative models that can generate comparable samples to those generated by Diffusion Models.
Reweighted Interacting Langevin Diffusions: an Accelerated Sampling Methodfor Optimization
We proposed a new technique to accelerate sampling methods for solving difficult optimization problems.
Universality of parametric Coupling Flows over parametric diffeomorphisms
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression.
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