1 citations · 1 across the 5 of their papers we have counts for
6 papers
Transfer Calibrated Prediction Powered Inference
Aditya T. Vadlamani, Jae Ho Chang, Srinivasan Parthasarathy +1
Prediction-powered inference (PPI) and its power-tuned extension (PPI++) improve confidence intervals by combining a small gold-standard labeled sample with a large AI model's pred…
Towards Auditing AI Systems in the Wild
Aditya T. Vadlamani, Anutam Srinivasan, Srinivasan Parthasarathy
AI systems are increasingly deployed in real-world settings where their behavior is shaped by dynamic environments, evolving data distributions, and complex interactions with users…
FedCF: Fair Federated Conformal Prediction
Anutam Srinivasan, Aditya T. Vadlamani, Amin Meghrazi +1
Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models. In its standard form, CP offers probabilistic guarantees on the coverag…
A Generic Framework for Conformal Fairness
Aditya T. Vadlamani, Anutam Srinivasan, Pranav Maneriker +2
Conformal Prediction (CP) is a popular method for uncertainty quantification with machine learning models. While conformal prediction provides probabilistic guarantees regarding th…
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
Yuntian He, Pranav Maneriker, Anutam Srinivasan +2
Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural n…
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
Pranav Maneriker, Aditya T. Vadlamani, Anutam Srinivasan +3
Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has bu…