
Representation Learning for OutOfDistribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tas...
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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Inducing causal relationships from observations is a classic problem in ...
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Interventional Assays for the Latent Space of Autoencoders
The encoders and decoders of autoencoders effectively project the input ...
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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Learning the causal structure that underlies data is a crucial step towa...
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Representation Learning in ContinuousTime ScoreBased Generative Models
Scorebased methods represented as stochastic differential equations on ...
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Pyfectious: An individuallevel simulator to discover optimal containment polices for epidemic diseases
Simulating the spread of infectious diseases in human communities is cri...
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NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Estimating an individual's potential response to interventions from obse...
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Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
How to improve generative modeling by better exploiting spatial regulari...
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Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and dev...
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On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying stru...
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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the unsupervised learning of disentangled representation...
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Function Contrastive Learning of Transferable Representations
Fewshotlearning seeks to find models that are capable of fastadaptati...
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CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Despite recent successes of reinforcement learning (RL), it remains a ch...
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Realtime Prediction of COVID19 related Mortality using Electronic Health Records
Coronavirus Disease 2019 (COVID19) is an emerging respiratory disease c...
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TriFinger: An OpenSource Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despi...
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A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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S2RMs: Spatially Structured Recurrent Modules
Capturing the structure of a datagenerating process by means of appropr...
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Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variabl...
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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...
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Structural Autoencoders Improve Representations for Generation and Transfer
We study the problem of structuring a learned representation to signific...
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predCOVID19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019
Coronavirus Disease 2019 (COVID19) is a rapidly emerging respiratory di...
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SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Gaussian processes are an important regression tool with excellent analy...
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Causal models for dynamical systems
A probabilistic model describes a system in its observational state. In ...
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Learning Neural Causal Models from Unknown Interventions
Metalearning over a set of distributions can be interpreted as learning...
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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with structurally disent...
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Disentangled State Space Representations
Sequential data often originates from diverse domains across which stati...
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On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
The problem of inferring the direct causal parents of a response variabl...
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AReS and MaRS  Adversarial and MMDMinimizing Regression for SDEs
Stochastic differential equations are an important modeling class in man...
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ODIN: ODEInformed Regression for Parameter and State Inference in TimeContinuous Dynamical Systems
Parameter inference in ordinary differential equations is an important p...
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Bayesian Online Detection and Prediction of Change Points
Online detection of instantaneous changes in the generative process of a...
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Learning Counterfactual Representations for Estimating Individual DoseResponse Curves
Estimating what would be an individual's potential response to varying l...
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
In recent years, the interest in unsupervised learning of disentangled r...
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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...
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Interventional Robustness of Deep Latent Variable Models
The ability to learn disentangled representations that split underlying ...
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Identifying Causal Structure in LargeScale Kinetic Systems
In the natural sciences, differential equations are widely used to descr...
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Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
We introduce a method which enables a recurrent dynamics model to be tem...
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Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Parameter identification and comparison of dynamical systems is a challe...
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Scalable Variational Inference for Dynamical Systems
Gradient matching is a promising tool for learning parameters and state ...
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MeanField Variational Inference for Gradient Matching with Gaussian Processes
Gradient matching with Gaussian processes is a promising tool for learni...
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Model Selection for Gaussian Process Regression by Approximation Set Coding
Gaussian processes are powerful, yet analytically tractable models for s...
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MultiOrgan Cancer Classification and Survival Analysis
Accurate and robust cell nuclei classification is the cornerstone for a ...
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Stefan Bauer
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