76 citations · 213 across the 23 of their papers we have counts for
7 papers · 1 filter
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson +4
In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that s…
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal +7
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise that the causal variables themselves are ob…
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste +4
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work in causal inference focuses on determin…
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh +3
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating…
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Sonali Parbhoo, Stefan Bauer, Patrick Schwab
Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economi…
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer +2
How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) a…