Search Results for author: Ashkan Soleymani

Found 8 papers, 2 papers with code

DRCFS: Doubly Robust Causal Feature Selection

no code implementations12 Jun 2023 Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer

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.

feature selection

GeneDisco: A Benchmark for Experimental Design in Drug Discovery

2 code implementations ICLR 2022 Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab

GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.

Active Learning Drug Discovery +1

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

1 code implementation24 Mar 2021 Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer

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 impacts of the outbreak.

Causal Feature Selection via Orthogonal Search

no code implementations6 Jul 2020 Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve

The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.

Causal Discovery feature selection

Cannot find the paper you are looking for? You can Submit a new open access paper.