Score-based generative modeling (SGM) is a highly successful approach for learning a
probability distribution from data and generating further samples. We prove the first …

Score-based generative modeling (SGM) has grown to be a hugely successful method for
learning to generate samples from complex data distributions such as that of images and …

Classically, the continuous-time Langevin diffusion converges exponentially fast to its
stationary distribution π under the sole assumption that π satisfies a Poincaré inequality …

Abstract We study the Unadjusted Langevin Algorithm (ULA) for sampling from a probability
distribution $\nu= e^{-f} $ on $\R^ n $. We prove a convergence guarantee in Kullback …

It is a fundamental problem to understand the complexity of high-accuracy sampling from a
strongly log-concave density π on ℝ d. Indeed, in practice, high-accuracy samplers such as …

As an example of the nonlinear Fokker-Planck equation, the mean field Langevin dynamics
recently attracts attention due to its connection to (noisy) gradient descent on infinitely wide …

Abstract Underdamped Langevin Monte Carlo (ULMC) is an algorithm used to sample from
unnormalized densities by leveraging the momentum of a particle moving in a potential well …

Langevin diffusions are rapidly convergent under appropriate functional inequality
assumptions. Hence, it is natural to expect that with additional smoothness conditions to …

We propose a sampling algorithm that achieves superior complexity bounds in all the
classical settings (strongly log-concave, log-concave, Logarithmic-Sobolev inequality (LSI) …

Abstract Machine unlearning has raised significant interest with the adoption of laws
ensuring the``right to be forgotten''. Researchers have provided a probabilistic notion of …