5 citations · 5 across the 4 of their papers we have counts for
5 papers
Smoothed Elicitation Complexity for Approximate -calibration of Discrete Classification Tasks
Jessica Finocchiaro, Victor Ganson, Drona Khurana
One prominent method of evaluating machine learning model trustworthiness is the notion of calibration. In the binary outcome setting, a probabilistic predictor is calibrated if ou…
Consistency Conditions for Differentiable Surrogate Losses
Drona Khurana, Anish Thilagar, Dhamma Kimpara +1
The statistical consistency of surrogate losses for discrete prediction tasks is often checked via the condition of calibration. However, directly verifying calibration can be ardu…
Consistent Multiclass Algorithms for Complex Metrics and Constraints
Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker +3
We present consistent algorithms for multiclass learning with complex performance metrics and constraints, where the objective and constraints are defined by arbitrary functions of…
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…