1 citations · 1 across the 7 of their papers we have counts for
8 papers
No-Regret Revenue Maximization in Sequential Market Making Under Inventory Constraints
Davide Maran, Marcello Restelli, Francesco Emanuele Stradi
Market makers keep trading efficient by setting bid and ask prices for buyers and sellers. In sequential market making, trades happen independently based on price thresholds, requi…
Online Market Making and the Value of Observing the Order Book
Davide Maran, Marcello Restelli
We study an online market-making problem in which a learner sequentially posts bid and ask prices for a single asset while interacting with traders holding private valuations. Unli…
Sharper Guarantees for Misspecified Kernelized Bandit Optimization
Davide Maran, Csaba Szepesvári
Existing guarantees for misspecified kernelized bandit optimization pay for misspecification through kernel complexity: in generic offline bounds, the misspecification level $\vare…
Learning in Markov Decision Processes with Exogenous Dynamics
Davide Maran, Davide Salaorni, Marcello Restelli
Reinforcement learning algorithms are typically designed for generic Markov Decision Processes (MDPs), where any state-action pair can lead to an arbitrary transition distribution.…
Finite Sample Bounds for Non-Parametric Regression: Optimal Sample Efficiency and Space Complexity
Davide Maran, Marcello Restelli
We address the problem of learning an unknown smooth function and its derivatives from noisy pointwise evaluations under the supremum norm. While classical nonparametric regression…
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini +1
Achieving the no-regret property for Reinforcement Learning (RL) problems in continuous state and action-space environments is one of the major open problems in the field. Existing…
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini +1
We consider the problem of learning an -optimal policy in a general class of continuous-space Markov decision processes (MDPs) having smooth Bellman operators. Given a…
No-Regret Reinforcement Learning in Smooth MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini +1
Obtaining no-regret guarantees for reinforcement learning (RL) in the case of problems with continuous state and/or action spaces is still one of the major open challenges in the f…