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08/15/2023
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Experimentation on real robots is demanding in terms of time and costs. ...
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07/28/2023
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
Learning policies from previously recorded data is a promising direction...
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07/11/2023
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
The practical utility of causality in decision-making is widespread and ...
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06/12/2023
DRCFS: Doubly Robust Causal Feature Selection
Knowing the features of a complex system that are highly relevant to a p...
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02/21/2023
Differentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optim...
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11/24/2022
Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery
Inferring causal structure from data is a challenging task of fundamenta...
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11/07/2022
FED-CD: Federated Causal Discovery from Interventional and Observational Data
Causal discovery, the inference of causal relations from data, is a core...
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10/25/2022
From Points to Functions: Infinite-dimensional Representations in Diffusion Models
Diffusion-based generative models learn to iteratively transfer unstruct...
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10/24/2022
Learning Latent Structural Causal Models
Causal learning has long concerned itself with the accurate recovery of ...
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08/19/2022
Predicting the protein-ligand affinity from molecular dynamics trajectories
The accurate protein-ligand binding affinity prediction is essential in ...
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07/12/2022
Latent Variable Models for Bayesian Causal Discovery
Learning predictors that do not rely on spurious correlations involves b...
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06/23/2022
Invariant Causal Mechanisms through Distribution Matching
Learning representations that capture the underlying data generating pro...
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06/15/2022
Diffusion Models for Video Prediction and Infilling
To predict and anticipate future outcomes or reason about missing inform...
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06/09/2022
On the Generalization and Adaption Performance of Causal Models
Learning models that offer robust out-of-distribution generalization and...
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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...
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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...
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03/03/2022
Interventions, Where and How? Experimental Design for Causal Models at Scale
Causal discovery from observational and interventional data is challengi...
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02/28/2022
Bayesian Structure Learning with Generative Flow Networks
In Bayesian structure learning, we are interested in inferring a distrib...
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01/31/2022
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks
Although reinforcement learning has seen remarkable progress over the la...
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01/20/2022
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
The widespread adoption of electronic health records (EHRs) and subseque...
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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...
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10/22/2021
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
In vitro cellular experimentation with genetic interventions, using for ...
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10/07/2021
Boxhead: A Dataset for Learning Hierarchical Representations
Disentanglement is hypothesized to be beneficial towards a number of dow...
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09/06/2021
Learning Neural Causal Models with Active Interventions
Discovering causal structures from data is a challenging inference probl...
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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...
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07/12/2021
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tas...
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07/02/2021
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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06/30/2021
Interventional Assays for the Latent Space of Autoencoders
The encoders and decoders of autoencoders effectively project the input ...
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06/14/2021
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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05/29/2021
Representation Learning in Continuous-Time Score-Based Generative Models
Score-based methods represented as stochastic differential equations on ...
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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...
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03/20/2021
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Estimating an individual's potential response to interventions from obse...
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03/16/2021
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
How to improve generative modeling by better exploiting spatial regulari...
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02/22/2021
Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and dev...
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10/27/2020
On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying stru...
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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...
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10/14/2020
Function Contrastive Learning of Transferable Representations
Few-shot-learning seeks to find models that are capable of fast-adaptati...
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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...
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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...
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08/08/2020
TriFinger: An Open-Source Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despi...
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07/28/2020
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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07/13/2020
S2RMs: Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropr...
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07/06/2020
Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variabl...
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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...
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06/14/2020
Structural Autoencoders Improve Representations for Generation and Transfer
We study the problem of structuring a learned representation to signific...
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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...
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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...
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01/17/2020
Causal models for dynamical systems
A probabilistic model describes a system in its observational state. In ...
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10/02/2019
Learning Neural Causal Models from Unknown Interventions
Meta-learning over a set of distributions can be interpreted as learning...
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06/07/2019
