b'Stefan Bauer'

research

∙ 07/12/2022

Latent Variable Models for Bayesian Causal Discovery

Learning predictors that do not rely on spurious correlations involves b...

Jithendaraa Subramanian, et al. ∙

research

∙ 06/23/2022

Invariant Causal Mechanisms through Distribution Matching

Learning representations that capture the underlying data generating pro...

Mathieu Chevalley, et al. ∙

research

∙ 06/15/2022

Diffusion Models for Video Prediction and Infilling

To predict and anticipate future outcomes or reason about missing inform...

Tobias Höppe, et al. ∙

research

∙ 06/09/2022

On the Generalization and Adaption Performance of Causal Models

Learning models that offer robust out-of-distribution generalization and...

Nino Scherrer, et al. ∙

research

∙ 05/19/2022

Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks

This paper describes a deep reinforcement learning (DRL) approach that w...

Qiang Wang, et al. ∙

research

∙ 04/20/2022

Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database

Federated learning (FL) has been proposed as a method to train a model o...

Arash Mehrjou, et al. ∙

research

∙ 03/03/2022

Interventions, Where and How? Experimental Design for Causal Models at Scale

Causal discovery from observational and interventional data is challengi...

Panagiotis Tigas, et al. ∙

research

∙ 02/28/2022

Bayesian Structure Learning with Generative Flow Networks

In Bayesian structure learning, we are interested in inferring a distrib...

Tristan Deleu, et al. ∙

research

∙ 01/31/2022

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks

Although reinforcement learning has seen remarkable progress over the la...

Davide Mambelli, et al. ∙

research

∙ 01/20/2022

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

The widespread adoption of electronic health records (EHRs) and subseque...

Simon Bing, et al. ∙

research

∙ 01/15/2022

Physical Derivatives: Computing policy gradients by physical forward-propagation

Model-free and model-based reinforcement learning are two ends of a spec...

Arash Mehrjou, et al. ∙

research

∙ 10/22/2021

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

In vitro cellular experimentation with genetic interventions, using for ...

Arash Mehrjou, et al. ∙

research

∙ 10/07/2021

Boxhead: A Dataset for Learning Hierarchical Representations

Disentanglement is hypothesized to be beneficial towards a number of dow...

Yukun Chen, et al. ∙

research

∙ 09/06/2021

Learning Neural Causal Models with Active Interventions

Discovering causal structures from data is a challenging inference probl...

Nino Scherrer, et al. ∙

research

∙ 08/22/2021

Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger

We present a system for learning a challenging dexterous manipulation ta...

Arthur Allshire, et al. ∙

research

∙ 07/12/2021

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning

Learning data representations that are useful for various downstream tas...

Andrea Dittadi, et al. ∙

research

∙ 07/02/2021

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Inducing causal relationships from observations is a classic problem in ...

Nan Rosemary Ke, et al. ∙

research

∙ 06/30/2021

Interventional Assays for the Latent Space of Autoencoders

The encoders and decoders of autoencoders effectively project the input ...

Felix Leeb, et al. ∙

research

∙ 06/14/2021

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Learning the causal structure that underlies data is a crucial step towa...

Yashas Annadani, et al. ∙

research

∙ 05/29/2021

Representation Learning in Continuous-Time Score-Based Generative Models

Score-based methods represented as stochastic differential equations on ...

Korbinian Abstreiter, et al. ∙

research

∙ 03/24/2021

Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases

Simulating the spread of infectious diseases in human communities is cri...

Arash Mehrjou, et al. ∙

research

∙ 03/20/2021

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments

Estimating an individual's potential response to interventions from obse...

Sonali Parbhoo, et al. ∙

research

∙ 03/16/2021

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

How to improve generative modeling by better exploiting spatial regulari...

Đorđe Miladinović, et al. ∙

research

∙ 02/22/2021

Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...

Bernhard Schölkopf, et al. ∙

research

∙ 10/27/2020

On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying stru...

Andrea Dittadi, et al. ∙

research

∙ 10/27/2020

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

The idea behind the unsupervised learning of disentangled representation...

Francesco Locatello, et al. ∙

research

∙ 10/14/2020

Function Contrastive Learning of Transferable Representations

Few-shot-learning seeks to find models that are capable of fast-adaptati...

Muhammad Waleed Gondal, et al. ∙

research

∙ 10/08/2020

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Despite recent successes of reinforcement learning (RL), it remains a ch...

Ossama Ahmed, et al. ∙

research

∙ 08/31/2020

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease c...

Patrick Schwab, et al. ∙

research

∙ 08/08/2020

TriFinger: An Open-Source Robot for Learning Dexterity

Dexterous object manipulation remains an open problem in robotics, despi...

Manuel Wüthrich, et al. ∙

research

∙ 07/28/2020

A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...

Francesco Locatello, et al. ∙

research

∙ 07/13/2020

S2RMs: Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropr...

Nasim Rahaman, et al. ∙

research

∙ 07/06/2020

Causal Feature Selection via Orthogonal Search

The problem of inferring the direct causal parents of a response variabl...

Anant Raj, et al. ∙

research

∙ 06/14/2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open...

Frederik Träuble, et al. ∙

research

∙ 06/14/2020

Structural Autoencoders Improve Representations for Generation and Transfer

We study the problem of structuring a learned representation to signific...

Felix Leeb, et al. ∙

research

∙ 05/17/2020

predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019

Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory di...

Patrick Schwab, et al. ∙

research

∙ 03/05/2020

SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

Gaussian processes are an important regression tool with excellent analy...

Emmanouil Angelis, et al. ∙

research

∙ 01/17/2020

Causal models for dynamical systems

A probabilistic model describes a system in its observational state. In ...

Jonas Peters, et al. ∙

research

∙ 10/02/2019

Learning Neural Causal Models from Unknown Interventions

Meta-learning over a set of distributions can be interpreted as learning...

Nan Rosemary Ke, et al. ∙

research

∙ 06/07/2019

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Learning meaningful and compact representations with structurally disent...

Muhammad Waleed Gondal, et al. ∙

research

∙ 06/07/2019

Disentangled State Space Representations

Sequential data often originates from diverse domains across which stati...

Đorđe Miladinović, et al. ∙

research

∙ 05/31/2019

On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled ...

Francesco Locatello, et al. ∙

research

∙ 05/03/2019

Disentangling Factors of Variation Using Few Labels

Learning disentangled representations is considered a cornerstone proble...

Francesco Locatello, et al. ∙

research

∙ 03/06/2019

Orthogonal Structure Search for Efficient Causal Discovery from Observational Data

The problem of inferring the direct causal parents of a response variabl...

Anant Raj, et al. ∙

research

∙ 02/22/2019

AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

Stochastic differential equations are an important modeling class in man...

Gabriele Abbati, et al. ∙

research

∙ 02/17/2019

ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

Parameter inference in ordinary differential equations is an important p...

Philippe Wenk, et al. ∙

research

∙ 02/12/2019

Bayesian Online Detection and Prediction of Change Points

Online detection of instantaneous changes in the generative process of a...

Diego Agudelo-España, et al. ∙

research

∙ 02/03/2019

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Estimating what would be an individual's potential response to varying l...

Patrick Schwab, et al. ∙

research

∙ 11/29/2018

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled r...

Francesco Locatello, et al. ∙

research

∙ 11/05/2018

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different d...

Spyridon Bakas, et al. ∙

Stefan Bauer

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