Atasi Panda — accepted AI-conference papers

2026

Group Fair Matchings Using Convex Cost Functions

Atasi Panda, Harsh Sharma, Anand Louis, Prajakta Nimbhorkar

AAAI 2026technical

We consider the problem of assigning items to platforms where each item has a utility associated with each of the platforms to which it can be assigned. Each platform has a soft constraint over the total number of items it serves, modeled via a convex cost function. Additionally, items are partition

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2024

Individual Fairness under Group Fairness Constraints in Bipartite Matching - One Framework to Approximate Them All

Atasi Panda, Anand Louis, Prajakta Nimbhorkar

IJCAI 2024poster

We study the probabilistic assignment of items to platforms that satisfies both group and individual fairness constraints. Each item belongs to specific groups and has a preference ordering over platforms. Each platform enforces group fairness by limiting the number of items per group that can be as…

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