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Nathaniel Grammel

4 accepted papers

2025

Proportionally Fair Matching via Randomized Rounding

AAAI 2025technical

Given an edge-colored graph, the goal of the proportional fair matching problem is to find a maximum weight matching while ensuring proportional representation (with respect to the number of edges) of each color. The colors may correspond to demographic groups or other protected traits where we seek…

Cited by 0SourcePDFScholar
2021

Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience

AISTATS 2021poster

We study several generalizations of the Online Bipartite Matching problem. We consider settings with stochastic rewards, patience constraints, and weights (considering both vertex- and edge-weighted variants). We introduce a stochastic variant of the patience-constrained problem, where the patience…

Cited by 14SourcePDFScholar
2021

Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes

NeurIPS 2021poster

Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the input which has led to the study of stochastic matching models. Here, each edge in the graph has a known, independent p…

Cited by 25SourcePDFScholar
2021

PettingZoo: Gym for Multi-Agent Reinforcement Learning

NeurIPS 2021poster

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle (``"AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Re…