Research Post
We study a dynamic non-bipartite matching problem. There is a fixed set of agent types, and agents of a given type arrive and depart according to type-specific Poisson processes. The value of a match is determined by the types of the matched agents. We present an online algorithm that is (1/8)-competitive with respect to the value of the optimal-in-hindsight policy, for arbitrary weighted graphs. This is the first result to achieve a constant competitive ratio when both arrivals and departures are random and unannounced. Our algorithm treats agents heterogeneously, interpolating between immediate and delayed matching in order to thicken the market while still matching valuable agents opportunistically.
Feb 15th 2022
Research Post
Read this research paper, co-authored by Amii Fellow and Canada CIFAR AI Chair Osmar Zaiane: UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer
Sep 27th 2021
Research Post
Sep 17th 2021
Research Post
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