3 citations · 16 across the 47 of their papers we have counts for
13 papers · 1 filter
Parameterized Projected Bellman Operator
Théo Vincent, Alberto Maria Metelli, Boris Belousov +3
Approximate value iteration (AVI) is a family of algorithms for reinforcement learning (RL) that aims to obtain an approximation of the optimal value function. Generally, AVI algor…
Causal Feature Selection via Transfer Entropy
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Machine learning algorithms are designed to capture complex relationships between features. In this context, the high dimensionality of data often results in poor model performance…
-Adaptive Regret Minimization in Heavy-Tailed Bandits
Gianmarco Genalti, Lupo Marsigli, Nicola Gatti +1
Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely s…
Pure Exploration under Mediators' Feedback
Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
Stochastic multi-armed bandits are a sequential-decision-making framework, where, at each interaction step, the learner selects an arm and observes a stochastic reward. Within the…
Nonlinear Feature Aggregation: Two Algorithms driven by Theory
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Many real-world machine learning applications are characterized by a huge number of features, leading to computational and memory issues, as well as the risk of overfitting. Ideall…
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes
Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli
A large variety of real-world Reinforcement Learning (RL) tasks is characterized by a complex and heterogeneous structure that makes end-to-end (or flat) approaches hardly applicab…
Truncating Trajectories in Monte Carlo Reinforcement Learning
Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
In Reinforcement Learning (RL), an agent acts in an unknown environment to maximize the expected cumulative discounted sum of an external reward signal, i.e., the expected return.…
Towards Theoretical Understanding of Inverse Reinforcement Learning
Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli
Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a reward function justifying the behavior demonstrated by an expert agent. A well-known…
A Tale of Sampling and Estimation in Discounted Reinforcement Learning
Alberto Maria Metelli, Mirco Mutti, Marcello Restelli
The most relevant problems in discounted reinforcement learning involve estimating the mean of a function under the stationary distribution of a Markov reward process, such as the…
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select only the relevant, non-redu…
Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice
Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli +1
We investigate the problem of bandits with expert advice when the experts are fixed and known distributions over the actions. Improving on previous analyses, we show that the regre…
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control
Amarildo Likmeta, Matteo Sacco, Alberto Maria Metelli +1
Uncertainty quantification has been extensively used as a means to achieve efficient directed exploration in Reinforcement Learning (RL). However, state-of-the-art methods for cont…
Best Arm Identification for Stochastic Rising Bandits
Marco Mussi, Alessandro Montenegro, Francesco Trovó +2
Stochastic Rising Bandits (SRBs) model sequential decision-making problems in which the expected reward of the available options increases every time they are selected. This settin…