George Deligiannidis

research

∙ 08/07/2023

Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Diffusion models are a powerful method for generating approximate sample...

0 Joe Benton, et al. ∙

research

∙ 06/12/2023

On the Expected Size of Conformal Prediction Sets

While conformal predictors reap the benefits of rigorous statistical gua...

0 Guneet S. Dhillon, et al. ∙

research

∙ 05/31/2023

A Unified Framework for U-Net Design and Analysis

U-Nets are a go-to, state-of-the-art neural architecture across numerous...

0 Christopher Williams, et al. ∙

research

∙ 05/26/2023

Error Bounds for Flow Matching Methods

Score-based generative models are a popular class of generative modellin...

0 Joe Benton, et al. ∙

research

∙ 02/06/2023

Generalization Bounds with Data-dependent Fractal Dimensions

Providing generalization guarantees for modern neural networks has been ...

0 Benjamin Dupuis, et al. ∙

research

∙ 01/19/2023

A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs

U-Net architectures are ubiquitous in state-of-the-art deep learning, ho...

10 Fabian Falck, et al. ∙

research

∙ 11/07/2022

From Denoising Diffusions to Denoising Markov Models

Denoising diffusions are state-of-the-art generative models which exhibi...

0 Joe Benton, et al. ∙

research

∙ 09/06/2022

A PAC-Bayes bound for deterministic classifiers

We establish a disintegrated PAC-Bayesian bound, for classifiers that ar...

19 Eugenio Clerico, et al. ∙

research

∙ 06/30/2022

Ranking in Contextual Multi-Armed Bandits

We study a ranking problem in the contextual multi-armed bandit setting....

0 Amitis Shidani, et al. ∙

research

∙ 05/30/2022

A Continuous Time Framework for Discrete Denoising Models

We provide the first complete continuous time framework for denoising di...

0 Andrew Campbell, et al. ∙

research

∙ 03/02/2022

Chained Generalisation Bounds

This work discusses how to derive upper bounds for the expected generali...

0 Eugenio Clerico, et al. ∙

research

∙ 03/01/2022

Neural Score Matching for High-Dimensional Causal Inference

Traditional methods for matching in causal inference are impractical for...

3 Oscar Clivio, et al. ∙

research

∙ 02/27/2022

Conditional Simulation Using Diffusion Schrödinger Bridges

Denoising diffusion models have recently emerged as a powerful class of ...

0 Yuyang Shi, et al. ∙

research

∙ 12/01/2021

On Mixing Times of Metropolized Algorithm With Optimization Step (MAO) : A New Framework

In this paper, we consider sampling from a class of distributions with t...

0 EL Mahdi Khribch, et al. ∙

research

∙ 10/22/2021

Conditional Gaussian PAC-Bayes

Recent studies have empirically investigated different methods to train ...

0 Eugenio Clerico, et al. ∙

research

∙ 08/18/2021

Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure

We establish the uniform in time stability, w.r.t. the marginals, of the...

0 George Deligiannidis, et al. ∙

research

∙ 06/17/2021

Wide stochastic networks: Gaussian limit and PAC-Bayesian training

The limit of infinite width allows for substantial simplifications in th...

0 Eugenio Clerico, et al. ∙

research

∙ 06/09/2021

Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms

Understanding generalization in deep learning has been one of the major ...

0 Alexander Camuto, et al. ∙

research

∙ 02/15/2021

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

Particle Filtering (PF) methods are an established class of procedures f...

7 Adrien Corenflos, et al. ∙

research

∙ 10/24/2020

Stable ResNet

Deep ResNet architectures have achieved state of the art performance on ...

1 Soufiane Hayou, et al. ∙

research

∙ 06/16/2020

Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks

Despite its success in a wide range of applications, characterizing the ...

1 Umut Şimşekli, et al. ∙

research

∙ 01/24/2020

Ensemble Rejection Sampling

We introduce Ensemble Rejection Sampling, a scheme for exact simulation ...

0 George Deligiannidis, et al. ∙

research

∙ 09/30/2019

Localised Generative Flows

We argue that flow-based density models based on continuous bijections a...

7 Rob Cornish, et al. ∙

research

∙ 05/08/2019

Non-Reversible Parallel Tempering: an Embarassingly Parallel MCMC Scheme

Parallel tempering (PT) methods are a popular class of Markov chain Mont...

0 Saifuddin Syed, et al. ∙

research

∙ 03/03/2019

Bernoulli Race Particle Filters

When the weights in a particle filter are not available analytically, st...

1 Sebastian M. Schmon, et al. ∙

research

∙ 02/05/2019

Unbiased Smoothing using Particle Independent Metropolis-Hastings

We consider the approximation of expectations with respect to the distri...

0 Lawrence Middleton, et al. ∙

research

∙ 01/28/2019

Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets

Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods ...

0 Robert Cornish, et al. ∙

research

∙ 08/13/2018

Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-Free Convergence Rates

The Bouncy Particle Sampler is a Markov chain Monte Carlo method based o...

0 George Deligiannidis, et al. ∙

research

∙ 07/23/2018

Unbiased Markov chain Monte Carlo for intractable target distributions

Performing numerical integration when the integrand itself cannot be eva...

0 Lawrence Middleton, et al. ∙

research

∙ 06/26/2018

Large Sample Asymptotics of the Pseudo-Marginal Method

The pseudo-marginal algorithm is a variant of the Metropolis-Hastings al...

1 Sebastian M. Schmon, et al. ∙

research

∙ 08/28/2017

Controlled Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a set of simulation-based techn...

0 Jeremy Heng, et al. ∙

George Deligiannidis

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