5 citations · 11 across the 7 of their papers we have counts for
7 papers
Explaining Drift using Shapley Values
Narayanan U. Edakunni, Utkarsh Tekriwal, Anukriti Jain
Machine learning models often deteriorate in their performance when they are used to predict the outcomes over data on which they were not trained. These scenarios can often arise…
Simple is better: Making Decision Trees faster using random sampling
Vignesh Nanda Kumar, Narayanan U Edakunni
In recent years, gradient boosted decision trees have become popular in building robust machine learning models on big data. The primary technique that has enabled these algorithms…
Statistical Guarantees for Fairness Aware Plug-In Algorithms
Drona Khurana, Srinivasan Ravichandran, Sparsh Jain +1
A plug-in algorithm to estimate Bayes Optimal Classifiers for fairness-aware binary classification has been proposed in (Menon & Williamson, 2018). However, the statistical efficac…
FairXGBoost: Fairness-aware Classification in XGBoost
Srinivasan Ravichandran, Drona Khurana, Bharath Venkatesh +1
Highly regulated domains such as finance have long favoured the use of machine learning algorithms that are scalable, transparent, robust and yield better performance. One of the m…
Accurate and Intuitive Contextual Explanations using Linear Model Trees
Aditya Lahiri, Narayanan Unny Edakunni
With the ever-increasing use of complex machine learning models in critical applications within the finance domain, explaining the decisions of the model has become a necessity. Wi…
Probabilistic Dependency Networks for Prediction and Diagnostics
Narayanan U. Edakunni, Aditi Raghunathan, Abhishek Tripathi +2
Research in transportation frequently involve modelling and predicting attributes of events that occur at regular intervals. The event could be arrival of a bus at a bus stop, the…
Boosting as a Product of Experts
Narayanan U. Edakunni, Gary Brown, Tim Kovacs
In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure whic…