research
∙
08/09/2023
Do Diffusion Models Suffer Error Propagation? Theoretical Analysis and Consistency Regularization
While diffusion models have achieved promising performances in data synt...
share
research
∙
05/31/2023
Learning Representations without Compositional Assumptions
This paper addresses unsupervised representation learning on tabular dat...
research
∙
05/16/2023
Synthetic data, real errors: how (not) to publish and use synthetic data
Generating synthetic data through generative models is gaining interest ...
research
∙
03/03/2023
Causal Deep Learning
Causality has the potential to truly transform the way we solve a large ...
research
∙
02/24/2023
Neural Laplace Control for Continuous-time Delayed Systems
Many real-world offline reinforcement learning (RL) problems involve con...
research
∙
02/24/2023
Membership Inference Attacks against Synthetic Data through Overfitting Detection
Data is the foundation of most science. Unfortunately, sharing data can ...
research
∙
01/18/2023
Synthcity: facilitating innovative use cases of synthetic data in different data modalities
Synthcity is an open-source software package for innovative use cases of...
research
∙
12/01/2022
Navigating causal deep learning
Causal deep learning (CDL) is a new and important research area in the l...
research
∙
06/21/2022
D-CIPHER: Discovery of Closed-form PDEs
Closed-form differential equations, including partial differential equat...
research
∙
06/16/2022
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Estimating counterfactual outcomes over time has the potential to unlock...
research
∙
06/10/2022
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
Neural Ordinary Differential Equations model dynamical systems with ODEs...
research
∙
10/28/2021
Explaining Latent Representations with a Corpus of Examples
Modern machine learning models are complicated. Most of them rely on con...
research
∙
06/05/2021
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
Modeling a system's temporal behaviour in reaction to external stimuli i...
research
∙
02/11/2021
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge
Selecting causal inference models for estimating individualized treatmen...
research
∙
07/27/2020
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat...
research
∙
06/26/2020
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Modern neural networks have proven to be powerful function approximators...
research
∙
05/13/2020
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
The coronavirus disease 2019 (COVID-19) global pandemic has led many cou...
research
∙
05/13/2020
When to Lift the Lockdown? Global COVID-19 Scenario Planning and Policy Effects using Compartmental Gaussian Processes
The coronavirus disease 2019 (COVID-19) outbreak has led government offi...
research
∙
01/08/2020
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Comorbid diseases co-occur and progress via complex temporal patterns th...
research
∙
01/27/2017