26 citations · 29 across the 4 of their papers we have counts for
7 papers
Post-processing for Individual Fairness
Felix Petersen, Debarghya Mukherjee, Yuekai Sun +1
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it…
On robust learning in the canonical change point problem under heavy tailed errors in finite and growing dimensions
Debarghya Mukherjee, Moulinath Banerjee, Ya'acov Ritov
This paper presents a number of new findings about the canonical change point estimation problem. The first part studies the estimation of a change point on the real line in a simp…
Outlier-Robust Optimal Transport
Debarghya Mukherjee, Aritra Guha, Justin Solomon +2
Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space. In light of recent advances in computational OT, OT dista…
Does enforcing fairness mitigate biases caused by subpopulation shift?
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training da…
Markovian And Non-Markovian Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic
Hamid Eftekhari, Debarghya Mukherjee, Moulinath Banerjee +1
We study and predict the evolution of Covid-19 in six US states from the period May 1 through August 31 using a discrete compartment-based model and prescribe active intervention p…
Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee +1
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness. Despite its benefits, it depends on a task specific f…