no code implementations • 3 Nov 2022 • Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta
Nonetheless, the heterogeneity nature of distributed data makes it challenging to define and ensure fairness among local agents.
no code implementations • 6 Feb 2022 • Vasilis Livanos, Ruta Mehta, Aniket Murhekar
We study two types of instances: (i) Separable, where the item set can be partitioned into goods and bads, and (ii) Restricted mixed goods (RMG), where for each item $j$, every agent has either a non-positive value for $j$, or values $j$ at the same $v_j>0$.
no code implementations • NeurIPS 2019 • Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi O. Koyejo
Metric Elicitation is a principled framework for selecting the performance metric that best reflects implicit user preferences.
no code implementations • 5 Jun 2018 • Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo
Given a binary prediction problem, which performance metric should the classifier optimize?
no code implementations • 6 Nov 2017 • Ziwei Ji, Ruta Mehta, Matus Telgarsky
Consider the seller's problem of finding optimal prices for her $n$ (divisible) goods when faced with a set of $m$ consumers, given that she can only observe their purchased bundles at posted prices, i. e., revealed preferences.
no code implementations • 30 Jul 2014 • Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani
In this work we advance this line of work by providing sample complexity guarantees and efficient algorithms for a number of important classes.