b'Stefan Bauer'

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

Learning data representations that are useful for various downstream tas...
07/12/2021 ∙ by Andrea Dittadi, et al. ∙ 49 ∙ share

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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 ...
07/02/2021 ∙ by Nan Rosemary Ke, et al. ∙ 7 ∙ share

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Interventional Assays for the Latent Space of Autoencoders

The encoders and decoders of autoencoders effectively project the input ...
06/30/2021 ∙ by Felix Leeb, et al. ∙ 2 ∙ share

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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...
06/14/2021 ∙ by Yashas Annadani, et al. ∙ 0 ∙ share

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Representation Learning in Continuous-Time Score-Based Generative Models

Score-based methods represented as stochastic differential equations on ...
05/29/2021 ∙ by Korbinian Abstreiter, et al. ∙ 0 ∙ share

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

Simulating the spread of infectious diseases in human communities is cri...
03/24/2021 ∙ by Arash Mehrjou, et al. ∙ 0 ∙ share

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NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments

Estimating an individual's potential response to interventions from obse...
03/20/2021 ∙ by Sonali Parbhoo, et al. ∙ 0 ∙ share

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Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

How to improve generative modeling by better exploiting spatial regulari...
03/16/2021 ∙ by Đorđe Miladinović, et al. ∙ 12 ∙ share

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Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...
02/22/2021 ∙ by Bernhard Schölkopf, et al. ∙ 44 ∙ share

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On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying stru...
10/27/2020 ∙ by Andrea Dittadi, et al. ∙ 24 ∙ share

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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...
10/27/2020 ∙ by Francesco Locatello, et al. ∙ 27 ∙ share

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Function Contrastive Learning of Transferable Representations

Few-shot-learning seeks to find models that are capable of fast-adaptati...
10/14/2020 ∙ by Muhammad Waleed Gondal, et al. ∙ 8 ∙ share

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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...
10/08/2020 ∙ by Ossama Ahmed, et al. ∙ 5 ∙ share

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

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease c...
08/31/2020 ∙ by Patrick Schwab, et al. ∙ 0 ∙ share

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TriFinger: An Open-Source Robot for Learning Dexterity

Dexterous object manipulation remains an open problem in robotics, despi...
08/08/2020 ∙ by Manuel Wüthrich, et al. ∙ 1 ∙ share

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A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...
07/28/2020 ∙ by Francesco Locatello, et al. ∙ 15 ∙ share

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S2RMs: Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropr...
07/13/2020 ∙ by Nasim Rahaman, et al. ∙ 5 ∙ share

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Causal Feature Selection via Orthogonal Search

The problem of inferring the direct causal parents of a response variabl...
07/06/2020 ∙ by Anant Raj, et al. ∙ 47 ∙ share

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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...
06/14/2020 ∙ by Frederik Träuble, et al. ∙ 14 ∙ share

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Structural Autoencoders Improve Representations for Generation and Transfer

We study the problem of structuring a learned representation to signific...
06/14/2020 ∙ by Felix Leeb, et al. ∙ 26 ∙ share

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

Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory di...
05/17/2020 ∙ by Patrick Schwab, et al. ∙ 0 ∙ share

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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...
03/05/2020 ∙ by Emmanouil Angelis, et al. ∙ 0 ∙ share

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Causal models for dynamical systems

A probabilistic model describes a system in its observational state. In ...
01/17/2020 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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Learning Neural Causal Models from Unknown Interventions

Meta-learning over a set of distributions can be interpreted as learning...
10/02/2019 ∙ by Nan Rosemary Ke, et al. ∙ 31 ∙ share

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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...
06/07/2019 ∙ by Muhammad Waleed Gondal, et al. ∙ 4 ∙ share

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Disentangled State Space Representations

Sequential data often originates from diverse domains across which stati...
06/07/2019 ∙ by Đorđe Miladinović, et al. ∙ 5 ∙ share

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On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled ...
05/31/2019 ∙ by Francesco Locatello, et al. ∙ 6 ∙ share

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Disentangling Factors of Variation Using Few Labels

Learning disentangled representations is considered a cornerstone proble...
05/03/2019 ∙ by Francesco Locatello, et al. ∙ 12 ∙ share

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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...
03/06/2019 ∙ by Anant Raj, et al. ∙ 6 ∙ share

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

Stochastic differential equations are an important modeling class in man...
02/22/2019 ∙ by Gabriele Abbati, et al. ∙ 12 ∙ share

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

Parameter inference in ordinary differential equations is an important p...
02/17/2019 ∙ by Philippe Wenk, et al. ∙ 10 ∙ share

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Bayesian Online Detection and Prediction of Change Points

Online detection of instantaneous changes in the generative process of a...
02/12/2019 ∙ by Diego Agudelo-España, et al. ∙ 10 ∙ share

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Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Estimating what would be an individual's potential response to varying l...
02/03/2019 ∙ by Patrick Schwab, et al. ∙ 4 ∙ share

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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...
11/29/2018 ∙ by Francesco Locatello, et al. ∙ 4 ∙ share

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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...
11/05/2018 ∙ by Spyridon Bakas, et al. ∙ 0 ∙ share

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Interventional Robustness of Deep Latent Variable Models

The ability to learn disentangled representations that split underlying ...
10/31/2018 ∙ by Raphael Suter, et al. ∙ 14 ∙ share

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Identifying Causal Structure in Large-Scale Kinetic Systems

In the natural sciences, differential equations are widely used to descr...
10/28/2018 ∙ by Niklas Pfister, et al. ∙ 0 ∙ share

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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...
08/14/2018 ∙ by Alexander Neitz, et al. ∙ 4 ∙ share

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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...
04/12/2018 ∙ by Philippe Wenk, et al. ∙ 0 ∙ share

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Scalable Variational Inference for Dynamical Systems

Gradient matching is a promising tool for learning parameters and state ...
05/19/2017 ∙ by Nico S. Gorbach, et al. ∙ 0 ∙ share

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Mean-Field Variational Inference for Gradient Matching with Gaussian Processes

Gradient matching with Gaussian processes is a promising tool for learni...
10/21/2016 ∙ by Nico S. Gorbach, et al. ∙ 0 ∙ share

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Model Selection for Gaussian Process Regression by Approximation Set Coding

Gaussian processes are powerful, yet analytically tractable models for s...
10/04/2016 ∙ by Benjamin Fischer, et al. ∙ 0 ∙ share

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Multi-Organ Cancer Classification and Survival Analysis

Accurate and robust cell nuclei classification is the cornerstone for a ...
06/02/2016 ∙ by Stefan Bauer, et al. ∙ 0 ∙ share

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Stefan Bauer

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