Search Results for author:
Found 16 papers, 5 papers with code
Oracle Efficient Online Multicalibration and Omniprediction
We develop a new online multicalibration algorithm that is well defined for infinite benchmark classes $F$, and is oracle efficient (i. e. for any class $F$, the algorithm has the form of an efficient reduction to a no-regret learning algorithm for $F$).
Batch Multivalid Conformal Prediction
Multivalid coverage guarantees are stronger than marginal coverage guarantees in two ways: (1) They hold even conditional on group membership -- that is, the target coverage level $1-\alpha$ holds conditionally on membership in each of an arbitrary (potentially intersecting) group in a finite collection $\mathcal{G}$ of regions in the feature space.
Multicalibrated Regression for Downstream Fairness
We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints.
Practical Adversarial Multivalid Conformal Prediction
It is computationally lightweight -- comparable to split conformal prediction -- but does not require having a held-out validation set, and so all data can be used for training models from which to derive a conformal score.
Distributionally Robust Data Join
Suppose we are given two datasets: a labeled dataset and unlabeled dataset which also has additional auxiliary features not present in the first dataset.
Adaptive Machine Unlearning
In this paper, we give a general reduction from deletion guarantees against adaptive sequences to deletion guarantees against non-adaptive sequences, using differential privacy and its connection to max information.
Online Multivalid Learning: Means, Moments, and Prediction Intervals
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples $(x, y)$.
Moment Multicalibration for Uncertainty Estimation
We show how to achieve the notion of "multicalibration" from H\'ebert-Johnson et al. [2018] not just for means, but also for variances and other higher moments.
Fair Prediction with Endogenous Behavior
There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e. g. in criminal justice) treat different demographic groups "fairly."
Metric-Free Individual Fairness in Online Learning
We study an online learning problem subject to the constraint of individual fairness, which requires that similar individuals are treated similarly.
A New Analysis of Differential Privacy's Generalization Guarantees
This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the posterior distribution after the mechanism has committed to its answers.
A Center in Your Neighborhood: Fairness in Facility Location
When selecting locations for a set of facilities, standard clustering algorithms may place unfair burden on some individuals and neighborhoods.
An Algorithmic Framework for Fairness Elicitation
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 complex and nuanced and thus require elicitation from individual or collective stakeholders.
Quantifying the Burden of Exploration and the Unfairness of Free Riding
We show that the free rider can achieve $O(1)$ regret in this setting whenever the free rider's context is a small (in $L_2$-norm) linear combination of other agents' contexts and all other agents pull each arm $\Omega (\log t)$ times with high probability.
Fair Algorithms for Learning in Allocation Problems
We formalize this fairness notion for allocation problems and investigate its algorithmic consequences.
Online Learning with an Unknown Fairness Metric
We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric.
