no code implementations • 7 Oct 2023 • Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
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.
no code implementations • 18 Feb 2020 • Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra
There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e. g. in criminal justice) treat different demographic groups "fairly."
no code implementations • 16 Feb 2020 • Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
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.
no code implementations • 23 Aug 2019 • Christopher Jung, Sampath Kannan, Neil Lutz
When selecting locations for a set of facilities, standard clustering algorithms may place unfair burden on some individuals and neighborhoods.
no code implementations • 20 Oct 2018 • Christopher Jung, Sampath Kannan, Neil Lutz
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.
no code implementations • 27 Aug 2018 • Sampath Kannan, Aaron Roth, Juba Ziani
We show that both goals can be achieved when the college does not report grades.
no code implementations • NeurIPS 2018 • Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
Bandit learning is characterized by the tension between long-term exploration and short-term exploitation.
no code implementations • TACL 2013 • Emily Pitler, Sampath Kannan, Mitchell Marcus
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.