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Finding Fair and Efficient Allocations When Valuations Don't Add Up
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}.
Weighted Envy-Freeness in Indivisible Item Allocation
We introduce and analyze new envy-based fairness concepts for agents with weights that quantify their entitlements in the allocation of indivisible items.
The Price of Quota-based Diversity in Assignment Problems
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.
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents
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.
How to show a probabilistic model is better
We present a simple theoretical framework, and corresponding practical procedures, for comparing probabilistic models on real data in a traditional machine learning setting.
