45 citations · 59 across the 6 of their papers we have counts for
7 papers · 1 filter
Batch Multivalid Conformal Prediction
Christopher Jung, Georgy Noarov, Ramya Ramalingam +1
We develop fast distribution-free conformal prediction algorithms for obtaining multivalid coverage on exchangeable data in the batch setting. Multivalid coverage guarantees are st…
Multicalibrated Regression for Downstream Fairness
Ira Globus-Harris, Varun Gupta, Christopher Jung +3
We show how to take a regression function that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier sati…
Adaptive Machine Unlearning
Varun Gupta, Christopher Jung, Seth Neel +3
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for seq…
Moment Multicalibration for Uncertainty Estimation
Christopher Jung, Changhwa Lee, Mallesh M. Pai +2
We show how to achieve the notion of "multicalibration" from Hébert-Johnson et al. [2018] not just for means, but also for variances and other higher moments. Informally, it means…
An Algorithmic Framework for Fairness Elicitation
Christopher Jung, Michael Kearns, Seth Neel +3
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more com…
Fair Algorithms for Learning in Allocation Problems
Hadi Elzayn, Shahin Jabbari, Christopher Jung +4
Settings such as lending and policing can be modeled by a centralized agent allocating a resource (loans or police officers) amongst several groups, in order to maximize some objec…