76 citations · 213 across the 23 of their papers we have counts for
6 papers · 1 filter
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
Clare Lyle, Arash Mehrjou, Pascal Notin +4
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the…
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Nico Gürtler, Felix Widmaier, Cansu Sancaktar +21
Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and be…
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu +2
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exis…
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
Nico Gürtler, Sebastian Blaes, Pavel Kolev +5
Learning policies from previously recorded data is a promising direction for real-world robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular…
DRCFS: Doubly Robust Causal Feature Selection
Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet +2
Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science. Existing approaches are often…
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Yashas Annadani, Nick Pawlowski, Joel Jennings +3
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However…