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09/05/2023
Diffusion Generative Inverse Design
Inverse design refers to the problem of optimizing the input of an objec...
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08/27/2023
Diffusion Schrödinger Bridges for Bayesian Computation
Denoising diffusion models are a novel class of generative models that h...
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08/17/2023
Reinforced Self-Training (ReST) for Language Modeling
Reinforcement learning from human feedback (RLHF) can improve the qualit...
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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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07/18/2023
Conformal prediction under ambiguous ground truth
In safety-critical classification tasks, conformal prediction allows to ...
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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...
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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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05/26/2023
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at ...
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05/25/2023
Trans-Dimensional Generative Modeling via Jump Diffusion Models
We propose a new class of generative models that naturally handle data o...
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03/29/2023
Diffusion Schrödinger Bridge Matching
Solving transport problems, i.e. finding a map transporting one given di...
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02/27/2023
Denoising Diffusion Samplers
Denoising diffusion models are a popular class of generative models prov...
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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...
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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...
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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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01/17/2023
Causal Falsification of Digital Twins
Digital twins hold substantial promise in many applications, but rigorou...
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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...
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11/28/2022
Continuous diffusion for categorical data
Diffusion models have quickly become the go-to paradigm for generative m...
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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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10/26/2022
Categorical SDEs with Simplex Diffusion
Diffusion models typically operate in the standard framework of generati...
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10/26/2022
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference
We introduce two synthetic likelihood methods for Simulation-Based Infer...
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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 ...
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09/28/2022
Spectral Diffusion Processes
Score-based generative modelling (SGM) has proven to be a very effective...
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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...
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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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08/16/2022
Score-Based Diffusion meets Annealed Importance Sampling
More than twenty years after its introduction, Annealed Importance Sampl...
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07/07/2022
Riemannian Diffusion Schrödinger Bridge
Score-based generative models exhibit state of the art performance on de...
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07/05/2022
An Empirical Study of Implicit Regularization in Deep Offline RL
Deep neural networks are the most commonly used function approximators i...
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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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06/09/2022
Conformal Off-Policy Prediction in Contextual Bandits
Most off-policy evaluation methods for contextual bandits have focused o...
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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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05/26/2022
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior ex...
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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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02/27/2022
Conditional Simulation Using Diffusion Schrödinger Bridges
Denoising diffusion models have recently emerged as a powerful class of ...
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02/23/2022
On PAC-Bayesian reconstruction guarantees for VAEs
Despite its wide use and empirical successes, the theoretical understand...
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02/06/2022
Riemannian Score-Based Generative Modeling
Score-based generative models (SGMs) are a novel class of generative mod...
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02/05/2022
Importance Weighting Approach in Kernel Bayes' Rule
We study a nonparametric approach to Bayesian computation via feature me...
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01/31/2022
Continual Repeated Annealed Flow Transport Monte Carlo
We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT...
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01/30/2022
COIN++: Data Agnostic Neural Compression
Neural compression algorithms are typically based on autoencoders that r...
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11/14/2021
Simulating Diffusion Bridges with Score Matching
We consider the problem of simulating diffusion bridges, i.e. diffusion ...
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10/26/2021
Online Variational Filtering and Parameter Learning
We present a variational method for online state estimation and paramete...
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10/22/2021
Conditional Gaussian PAC-Bayes
Recent studies have empirically investigated different methods to train ...
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10/18/2021
Learning Optimal Conformal Classifiers
Modern deep learning based classifiers show very high accuracy on test d...
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10/11/2021
Lithological Tomography with the Correlated Pseudo-Marginal Method
We consider lithological tomography in which the posterior distribution ...
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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...
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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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06/30/2021
Monte Carlo Variational Auto-Encoders
Variational auto-encoders (VAE) are popular deep latent variable models ...
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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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06/01/2021
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Progressively applying Gaussian noise transforms complex data distributi...
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05/21/2021