1 citations · 2 across the 9 of their papers we have counts for
9 papers
The Binary Tree Mechanism is Optimal for Differentially Private Continual Counting
Konstantina Bairaktari, Kasper Green Larsen, Markus Engelund Dahl
Private continual counting is a fundamental problem in differential privacy: given a binary stream of length , where each corresponds to the contribution of one individual,…
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…
Sample-efficient Multiclass Calibration under Error
Konstantina Bairaktari, Huy L. Nguyen
Calibrating a multiclass predictor, that outputs a distribution over labels, is particularly challenging due to the exponential number of possible prediction values. In this work,…
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage
Konstantina Bairaktari, Jiayun Wu, Zhiwei Steven Wu
Conformal prediction is a powerful distribution-free framework for constructing prediction sets with coverage guarantees. Classical methods, such as split conformal prediction, pro…
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…
Multitask Learning via Shared Features: Algorithms and Hardness
Konstantina Bairaktari, Guy Blanc, Li-Yang Tan +2
We investigate the computational efficiency of multitask learning of Boolean functions over the -dimensional hypercube, that are related by means of a feature representation of…
Fair and Optimal Cohort Selection for Linear Utilities
Konstantina Bairaktari, Huy Le Nguyen, Jonathan Ullman
The rise of algorithmic decision-making has created an explosion of research around the fairness of those algorithms. While there are many compelling notions of individual fairness…
Fair and Useful Cohort Selection
Konstantina Bairaktari, Paul Langton, Huy L. Nguyen +2
A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, an…