2 citations · 2 across the 7 of their papers we have counts for
7 papers
Truthful Calibration Measures for Sequential Prediction
Anagha Gokul, Jason Hartline, Lunjia Hu +2
Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabilities. A calibration measure assigns numerical error to miscalibrated…
Testable and Actionable Calibration for Full Swap Regret
Konstantina Bairaktari, Lunjia Hu, Huy L. Nguyen +1
AI generated predictions increasingly inform decision making in critical tasks, and therefore must be trustworthy. One widely used measure of trustworthiness is calibration, which…
The Sample Complexity of Membership Inference and Privacy Auditing
Mahdi Haghifam, Adam Smith, Jonathan Ullman
A membership-inference attack gets the output of a learning algorithm, and a target individual, and tries to determine whether this individual is a member of the training data or a…
Black-Box Privacy Attacks on Shared Representations in Multitask Learning
John Abascal, Nicolás Berrios, Alina Oprea +3
Multitask learning (MTL) has emerged as a powerful paradigm that leverages similarities among multiple learning tasks, each with insufficient samples to train a standalone model, t…
Privacy in Metalearning and Multitask Learning: Modeling and Separations
Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith +2
Model personalization allows a set of individuals, each facing a different learning task, to train models that are more accurate for each person than those they could develop indiv…
Metalearning with Very Few Samples Per Task
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown +3
Metalearning and multitask learning are two frameworks for solving a group of related learning tasks more efficiently than we could hope to solve each of the individual tasks on th…
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
Harsh Chaudhari, Giorgio Severi, Alina Oprea +1
The integration of machine learning (ML) in numerous critical applications introduces a range of privacy concerns for individuals who provide their datasets for model training. One…