no code implementations • 16 Mar 2020 • Nawal Benabbou, Mithun Chakraborty, Ayumi Igarashi, Yair Zick
In this paper, we present new results on the fair and efficient allocation of indivisible goods to agents whose preferences correspond to {\em matroid rank functions}.
no code implementations • 23 Sep 2019 • Mithun Chakraborty, Ayumi Igarashi, Warut Suksompong, Yair Zick
We introduce and analyze new envy-based fairness concepts for agents with weights that quantify their entitlements in the allocation of indivisible items.
no code implementations • 28 Nov 2017 • Nawal Benabbou, Mithun Chakraborty, Vinh Ho Xuan, Jakub Sliwinski, Yair Zick
The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair.
no code implementations • NeurIPS 2015 • Mithun Chakraborty, Sanmay Das
A market scoring rule (MSR) – a popular tool for designing algorithmic prediction markets – is an incentive-compatible mechanism for the aggregation of probabilistic beliefs from myopic risk-neutral agents.
no code implementations • 11 Feb 2015 • Mithun Chakraborty, Sanmay Das, Allen Lavoie
We present a simple theoretical framework, and corresponding practical procedures, for comparing probabilistic models on real data in a traditional machine learning setting.