add yaml

README.md

@@ -1,3 +1,11 @@
+---
+datasets:
+- maxcembalest/boston_housing
+language:
+- en
+tags:
+- code
+---
 # Representativity-based active learning for regression using Wasserstein distance and GroupSort Neural Networks
 
 You will find in this repository the codes used to test the performance of the WAR model on a fully labeled dataset
@@ -19,6 +27,4 @@ You will find in this repository the codes used to test the performance of the W
 
 
 ## Abstract
-This paper proposes a new active learning strategy called Wasserstein active regression (WAR) based on the principle of distribution-matching to measure the representativeness of our labeled dataset  compared to the global data distribution. We use GroupSort Neural Networks to compute the Wasserstein distance and provide theoretical foundations to justify the use of such networks with explicit bounds for their size and depth. We combine this solution with another diversity and uncertainty-based approach to sharpen our query strategy. Finally, we compare our method with other solutions and show empirically that we consistently achieve better estimations with less labeled data.
-
-
+This paper proposes a new active learning strategy called Wasserstein active regression (WAR) based on the principle of distribution-matching to measure the representativeness of our labeled dataset  compared to the global data distribution. We use GroupSort Neural Networks to compute the Wasserstein distance and provide theoretical foundations to justify the use of such networks with explicit bounds for their size and depth. We combine this solution with another diversity and uncertainty-based approach to sharpen our query strategy. Finally, we compare our method with other solutions and show empirically that we consistently achieve better estimations with less labeled data.