Introduces action-dependent order-book feedback for online market making, yielding O(sqrt(T)) high-probability regret in stochastic i.i.d. and mean-reverting settings without smoothness assumptions, and O(T^{2/3}) in the adversarial case.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A restarting-based nonparametric online learning method for dynamic pricing with one-point revenue feedback that achieves regret bounds scaling with time horizon and total market variation.
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Online Market Making and the Value of Observing the Order Book
Introduces action-dependent order-book feedback for online market making, yielding O(sqrt(T)) high-probability regret in stochastic i.i.d. and mean-reverting settings without smoothness assumptions, and O(T^{2/3}) in the adversarial case.
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Nonparametric Learning and Earning with One-Point Feedback under Nonstationarity
A restarting-based nonparametric online learning method for dynamic pricing with one-point revenue feedback that achieves regret bounds scaling with time horizon and total market variation.