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.[-]

We study repeated bilateral trade where an adaptive σ-smooth adversary generates the valuations of sellers and buyers. We provide a complete characterization of the regret regimes for fixed-price mechanisms under different feedback models in the two cases where the learner can post either the same or different prices to buyers and sellers. We begin by showing that the minimax regret after $T$ rounds is of order $\sqrt{T}$ in the full-feedback scenario. Under partial feedback, any algorithm that has to post the same price to buyers and sellers suffers worst-case linear regret. However, when the learner can post two different prices at each round, we design an algorithm enjoying regret of order $T^{3/4}$ ignoring log factors. We prove that this rate is optimal by presenting a surprising $T^{3/4}$ lower bound, which is the main technical contribution of the paper.[-]