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08/07/2023
Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Diffusion models are a powerful method for generating approximate sample...
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06/12/2023
On the Expected Size of Conformal Prediction Sets
While conformal predictors reap the benefits of rigorous statistical gua...
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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...
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05/26/2023
Error Bounds for Flow Matching Methods
Score-based generative models are a popular class of generative modellin...
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02/06/2023
Generalization Bounds with Data-dependent Fractal Dimensions
Providing generalization guarantees for modern neural networks has been ...
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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...
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11/07/2022
From Denoising Diffusions to Denoising Markov Models
Denoising diffusions are state-of-the-art generative models which exhibi...
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09/06/2022
A PAC-Bayes bound for deterministic classifiers
We establish a disintegrated PAC-Bayesian bound, for classifiers that ar...
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06/30/2022
Ranking in Contextual Multi-Armed Bandits
We study a ranking problem in the contextual multi-armed bandit setting....
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05/30/2022
A Continuous Time Framework for Discrete Denoising Models
We provide the first complete continuous time framework for denoising di...
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03/02/2022
Chained Generalisation Bounds
This work discusses how to derive upper bounds for the expected generali...
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03/01/2022
Neural Score Matching for High-Dimensional Causal Inference
Traditional methods for matching in causal inference are impractical for...
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02/27/2022
Conditional Simulation Using Diffusion Schrödinger Bridges
Denoising diffusion models have recently emerged as a powerful class of ...
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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...
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10/22/2021
Conditional Gaussian PAC-Bayes
Recent studies have empirically investigated different methods to train ...
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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...
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06/17/2021
Wide stochastic networks: Gaussian limit and PAC-Bayesian training
The limit of infinite width allows for substantial simplifications in th...
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06/09/2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Understanding generalization in deep learning has been one of the major ...
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02/15/2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Particle Filtering (PF) methods are an established class of procedures f...
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10/24/2020
Stable ResNet
Deep ResNet architectures have achieved state of the art performance on ...
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06/16/2020
Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks
Despite its success in a wide range of applications, characterizing the ...
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01/24/2020
Ensemble Rejection Sampling
We introduce Ensemble Rejection Sampling, a scheme for exact simulation ...
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09/30/2019
Localised Generative Flows
We argue that flow-based density models based on continuous bijections a...
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05/08/2019
Non-Reversible Parallel Tempering: an Embarassingly Parallel MCMC Scheme
Parallel tempering (PT) methods are a popular class of Markov chain Mont...
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03/03/2019
Bernoulli Race Particle Filters
When the weights in a particle filter are not available analytically, st...
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02/05/2019
Unbiased Smoothing using Particle Independent Metropolis-Hastings
We consider the approximation of expectations with respect to the distri...
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01/28/2019
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods ...
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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...
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07/23/2018
Unbiased Markov chain Monte Carlo for intractable target distributions
Performing numerical integration when the integrand itself cannot be eva...
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06/26/2018
Large Sample Asymptotics of the Pseudo-Marginal Method
The pseudo-marginal algorithm is a variant of the Metropolis-Hastings al...
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08/28/2017
