46 citations · 151 across the 31 of their papers we have counts for
5 papers · 1 filter
On the benefits of maximum likelihood estimation for Regression and Forecasting
Pranjal Awasthi, Abhimanyu Das, Rajat Sen +1
We advocate for a practical Maximum Likelihood Estimation (MLE) approach towards designing loss functions for regression and forecasting, as an alternative to the typical approach…
A Notion of Individual Fairness for Clustering
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
A common distinction in fair machine learning, in particular in fair classification, is between group fairness and individual fairness. In the context of clustering, group fairness…
Equalized odds postprocessing under imperfect group information
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Most approaches aiming to ensure a model's fairness with respect to a protected attribute (such as gender or race) assume to know the true value of the attribute for every data poi…
Guarantees for Spectral Clustering with Fairness Constraints
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi +1
Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion pro…
Fair k-Center Clustering for Data Summarization
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
In data summarization we want to choose prototypes in order to summarize a data set. We study a setting where the data set comprises several demographic groups and we are restr…