70 citations · 369 across the 48 of their papers we have counts for
6 papers · 1 filter
On importance sampling and independent Metropolis-Hastings with an unbounded weight function
George Deligiannidis, Pierre E. Jacob, El Mahdi Khribch +1
Importance sampling and independent Metropolis-Hastings are among the fundamental building blocks of Monte Carlo methods. Both require a proposal distribution that globally approxi…
Linear Convergence of Diffusion Models Under the Manifold Hypothesis
Peter Potaptchik, Iskander Azangulov, George Deligiannidis
Score-matching generative models have proven successful at sampling from complex high-dimensional data distributions. In many applications, this distribution is believed to concent…
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions
Iskander Azangulov, George Deligiannidis, Judith Rousseau
Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional data distributions and are widely used f…
Differentiable Cost-Parameterized Monge Map Estimators
Samuel Howard, George Deligiannidis, Patrick Rebeschini +1
Within the field of optimal transport (OT), the choice of ground cost is crucial to ensuring that the optimality of a transport map corresponds to usefulness in real-world applicat…
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets
Benjamin Dupuis, Paul Viallard, George Deligiannidis +1
We propose data-dependent uniform generalization bounds by approaching the problem from a PAC-Bayesian perspective. We first apply the PAC-Bayesian framework on "random sets" in a…
Particle Denoising Diffusion Sampler
Angus Phillips, Hai-Dang Dau, Michael John Hutchinson +3
Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate sampl…