Paper-Conference

We propose HYPPO, a novel system to optimize pipelines encountered in exploratory machine learning. HYPPO exploits alternative computational paths of artifacts from past executions to derive better execution plans while reusing materialized artifacts.

We propose THOR a novel method to extract information from text, that unlike related approaches, neither relies on complex rules nor models trained with large annotated corpus. Instead, THOR is lightweight and exploits integrated data and its schema without the need for human annotations.

In many cases, it is important to ensure that a model does not discriminate against individuals on the basis of their location (place of origin, home address, etc.). We consider location as the protected attribute and we want the algorithm to exhibit spatial fairness For example, consider a model that predicts whether mortgage loan applications are accepted.

We propose novel fairness definitions concerning algorithmic recourse. For an individual that receives an undesirable outcome (e.g., loan application rejected), recourse is a way to reverse the outcome (e.g., increase down payment).