24 citations · 87 across the 36 of their papers we have counts for
46 papers
Multigroup Fairness and Omniprediction: Separations and Equivalences
Sílvia Casacuberta, Parikshit Gopalan, Varun Kanade +3
Omniprediction is a learning guarantee which requires a single predictor to be competitive relative to the best hypothesis from a benchmark class for any loss chosen from a family…
Flexible Routing via Uncertainty Decomposition
Charlotte Peale, Siddartha Devic, Parikshit Gopalan +2
A key strategy for balancing performance and cost in modern machine learning systems is to dynamically route queries to either a low-cost model or a more expensive oracle (such as…
The Importance of Being Smoothly Calibrated
Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar +1
Recent work has highlighted the centrality of smooth calibration [Kakade and Foster, 2008] as a robust measure of calibration error. We generalize, unify, and extend previous resul…
The communication complexity of distributed estimation
Parikshit Gopalan, Raghu Meka, Prasad Raghavendra +2
We study an extension of the standard two-party communication model in which Alice and Bob hold probability distributions and over domains and , respectively. Their…
Efficient Calibration for Decision Making
Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar +1
A decision-theoretic characterization of perfect calibration is that an agent seeking to minimize a proper loss in expectation cannot improve their outcome by post-processing a per…
Calibration through the Lens of Indistinguishability
Parikshit Gopalan, Lunjia Hu
Calibration is a classical notion from the forecasting literature which aims to address the question: how should predicted probabilities be interpreted? In a world where we only ge…