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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).

Tutorial at The Web Conference 2022

We propose TokenJoin, a method for linking complex records, i.e., identifying similar pairs among a collection of complex records. A complex record is a set of simpler text entities, such as a set of addresses.

We develop a post-hoc, model-agnostic explanation mechanism for recommender systems. It returns counterfactual explanations defined as those minimal changes to the user’s interaction history that would result in the system not making the recommendation that is to be explained.