b'Arnaud Doucet'

research

∙ 09/05/2023

Diffusion Generative Inverse Design

Inverse design refers to the problem of optimizing the input of an objec...

0 Marin Vlastelica, et al. ∙

research

∙ 08/27/2023

Diffusion Schrödinger Bridges for Bayesian Computation

Denoising diffusion models are a novel class of generative models that h...

0 Jeremy Heng, et al. ∙

research

∙ 08/17/2023

Reinforced Self-Training (ReST) for Language Modeling

Reinforcement learning from human feedback (RLHF) can improve the qualit...

0 Caglar Gulcehre, et al. ∙

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

∙ 07/18/2023

Conformal prediction under ambiguous ground truth

In safety-critical classification tasks, conformal prediction allows to ...

0 David Stutz, et al. ∙

research

∙ 07/05/2023

Evaluating AI systems under uncertain ground truth: a case study in dermatology

For safety, AI systems in health undergo thorough evaluations before dep...

0 David Stutz, 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

∙ 05/26/2023

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters

Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at ...

0 Maxence Noble, et al. ∙

research

∙ 05/25/2023

Trans-Dimensional Generative Modeling via Jump Diffusion Models

We propose a new class of generative models that naturally handle data o...

0 Andrew Campbell, et al. ∙

research

∙ 03/29/2023

Diffusion Schrödinger Bridge Matching

Solving transport problems, i.e. finding a map transporting one given di...

0 Yuyang Shi, et al. ∙

research

∙ 02/27/2023

Denoising Diffusion Samplers

Denoising diffusion models are a popular class of generative models prov...

0 Francisco Vargas, et al. ∙

research

∙ 02/22/2023

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

Since their introduction, diffusion models have quickly become the preva...

0 Yilun Du, et al. ∙

research

∙ 02/05/2023

SE(3) diffusion model with application to protein backbone generation

The design of novel protein structures remains a challenge in protein en...

0 Jason Yim, 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

∙ 01/17/2023

Causal Falsification of Digital Twins

Digital twins hold substantial promise in many applications, but rigorou...

12 Rob Cornish, et al. ∙

research

∙ 12/14/2022

Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving

Multi-object state estimation is a fundamental problem for robotic appli...

2 Angad Singh, et al. ∙

research

∙ 11/28/2022

Continuous diffusion for categorical data

Diffusion models have quickly become the go-to paradigm for generative m...

0 Sander Dieleman, 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

∙ 10/26/2022

Categorical SDEs with Simplex Diffusion

Diffusion models typically operate in the standard framework of generati...

0 Pierre H. Richemond, et al. ∙

research

∙ 10/26/2022

Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference

We introduce two synthetic likelihood methods for Simulation-Based Infer...

0 Pierre Glaser, et al. ∙

research

∙ 10/12/2022

Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics

Several algorithms involving the Variational Rényi (VR) bound have been ...

0 Kamélia Daudel, et al. ∙

research

∙ 09/28/2022

Spectral Diffusion Processes

Score-based generative modelling (SGM) has proven to be a very effective...

8 Angus Phillips, et al. ∙

research

∙ 09/16/2022

Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows

Solving Fredholm equations of the first kind is crucial in many areas of...

0 Francesca R Crucinio, 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

∙ 08/16/2022

Score-Based Diffusion meets Annealed Importance Sampling

More than twenty years after its introduction, Annealed Importance Sampl...

0 Arnaud Doucet, et al. ∙

research

∙ 07/07/2022

Riemannian Diffusion Schrödinger Bridge

Score-based generative models exhibit state of the art performance on de...

32 James Thornton, et al. ∙

research

∙ 07/05/2022

An Empirical Study of Implicit Regularization in Deep Offline RL

Deep neural networks are the most commonly used function approximators i...

12 Caglar Gulcehre, 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

∙ 06/09/2022

Conformal Off-Policy Prediction in Contextual Bandits

Most off-policy evaluation methods for contextual bandits have focused o...

0 Muhammad Faaiz Taufiq, 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

∙ 05/26/2022

Towards Learning Universal Hyperparameter Optimizers with Transformers

Meta-learning hyperparameter optimization (HPO) algorithms from prior ex...

9 Yutian Chen, 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

∙ 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

∙ 02/23/2022

On PAC-Bayesian reconstruction guarantees for VAEs

Despite its wide use and empirical successes, the theoretical understand...

55 Badr-Eddine Chérief-Abdellatif, et al. ∙

research

∙ 02/06/2022

Riemannian Score-Based Generative Modeling

Score-based generative models (SGMs) are a novel class of generative mod...

5 Valentin De Bortoli, et al. ∙

research

∙ 02/05/2022

Importance Weighting Approach in Kernel Bayes' Rule

We study a nonparametric approach to Bayesian computation via feature me...

0 Liyuan Xu, et al. ∙

research

∙ 01/31/2022

Continual Repeated Annealed Flow Transport Monte Carlo

We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT...

0 Alexander G. D. G. Matthews, et al. ∙

research

∙ 01/30/2022

COIN++: Data Agnostic Neural Compression

Neural compression algorithms are typically based on autoencoders that r...

0 Emilien Dupont, et al. ∙

research

∙ 11/14/2021

Simulating Diffusion Bridges with Score Matching

We consider the problem of simulating diffusion bridges, i.e. diffusion ...

9 Valentin De Bortoli, et al. ∙

research

∙ 10/26/2021

Online Variational Filtering and Parameter Learning

We present a variational method for online state estimation and paramete...

0 Andrew Campbell, 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

∙ 10/18/2021

Learning Optimal Conformal Classifiers

Modern deep learning based classifiers show very high accuracy on test d...

0 David Stutz, et al. ∙

research

∙ 10/11/2021

Lithological Tomography with the Correlated Pseudo-Marginal Method

We consider lithological tomography in which the posterior distribution ...

0 Lea Friedli, et al. ∙

research

∙ 08/24/2021

Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale

Increasing interest in privacy-preserving machine learning has led to ne...

4 Sahra Ghalebikesabi, 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

∙ 06/30/2021

Monte Carlo Variational Auto-Encoders

Variational auto-encoders (VAE) are popular deep latent variable models ...

0 Achille Thin, 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

∙ 06/01/2021

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling

Progressively applying Gaussian noise transforms complex data distributi...

21 Valentin De Bortoli, et al. ∙

research

∙ 05/21/2021

On Instrumental Variable Regression for Deep Offline Policy Evaluation

We show that the popular reinforcement learning (RL) strategy of estimat...

26 Yutian Chen, et al. ∙

Arnaud Doucet

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