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Found 9 papers, 0 papers with code
Oracle Efficient Algorithms for Groupwise Regret
Our approach gives similar regret guarantees compared to [Blum & Lykouris]; however, we run in time linear in the number of groups, and are oracle-efficient in the hypothesis class.
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."
Pipeline Interventions
We consider two objectives: social welfare maximization, and a fairness-motivated maximin objective that seeks to maximize the value to the population (starting node) with the \emph{least} expected value.
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.
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.
Downstream Effects of Affirmative Action
We show that both goals can be achieved when the college does not report grades.
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Bandit learning is characterized by the tension between long-term exploration and short-term exploitation.
Finding Optimal 1-Endpoint-Crossing Trees
Dependency parsing algorithms capable of producing the types of crossing dependencies seen in natural language sentences have traditionally been orders of magnitude slower than algorithms for projective trees.
