

Wasserstein gradient flows and applications to sampling in machine learning - lecture 1
By Anna Korba


Wasserstein gradient flows and applications to sampling in machine learning - lecture 2
By Anna Korba
By Ali Aouad
Appears in collection : From matchings to markets. A tale of Mathematics, Economics and Computer Science. / Des matchings aux marchés. Une histoire de mathématiques
This talk will cover two recent advancements in the theory of online algorithms for dynamic matching markets. The first set of results concern a stochastic model of matching with Poisson arrivals and memoryless departures over edge-weighted graphs. The second set of results focus on the incorporation of serial correlation properties in classical online stochastic matching models. We develop new mathematical programming relaxations and correlated rounding schemes, yielding the first constant-factor performance guarantees in such settings.