Matteo Papini
25 papers · 2017–2026 · 8 venues · across top CS/AI venues
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Achievements
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(14)
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(2)
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Century Club Β· Bronze
Top co-authors
Research topics
Reinforcement Learning
(21)
Methods
(21)
Machine Learning
(18)
Optimization & Theory
(12)
Learning Types
(11)
Artificial Intelligence
(7)
Core AI
(5)
Mathematics & Optimization
(3)
Optimization
(3)
Application Areas
(2)
Applications Β· Reinforcement Learning
(2)
Core Methods
(2)
Data Science & Analytics
(1)
Deep Learning
(1)
Applications Β· Data Science & Analytics
(1)
Keywords
policy gradient
(8)
reinforcement learning
(7)
regret bound
(6)
contextual bandit
(5)
sample complexity
(4)
policy optimization
(4)
online learning
(4)
function approximation
(4)
continuous control
(4)
importance sampling
(4)
markov decision process
(4)
representation learning
(3)
regret minimization
(3)
linear mdp
(2)
online algorithm
(2)
stochastic policy
(2)
policy search
(2)
off-policy learning
(2)
variance reduction
(2)
constant regret
(2)
Papers
Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning
AAAI 2026
Convergence Analysis of Policy Gradient Methods with Dynamic Stochasticity
ICML 2025
Importance-Weighted Offline Learning Done Right
ALT 2024
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
NeurIPS 2024
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
ICML 2024
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
NeurIPS 2024
No-Regret Reinforcement Learning in Smooth MDPs
ICML 2024
Offline Primal-Dual Reinforcement Learning for Linear MDPs
AISTATS 2024
Online Learning with Off-Policy Feedback in Adversarial MDPs
IJCAI 2024
Optimistic Information Directed Sampling
COLT 2024
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
COLT 2024
Online Learning with Off-Policy Feedback
ALT 2023
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
NeurIPS 2022
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
NeurIPS 2022
Leveraging Good Representations in Linear Contextual Bandits
ICML 2021
Policy Optimization as Online Learning with Mediator Feedback
AAAI 2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
NeurIPS 2021
Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration
AISTATS 2020
Gradient-Aware Model-Based Policy Search
AAAI 2020
Importance Sampling Techniques for Policy Optimization
JMLR 2020
Risk-Averse Trust Region Optimization for Reward-Volatility Reduction
IJCAI 2020
Optimistic Policy Optimization via Multiple Importance Sampling
ICML 2019
Policy Optimization via Importance Sampling
NeurIPS 2018
Stochastic Variance-Reduced Policy Gradient
ICML 2018
Adaptive Batch Size for Safe Policy Gradients
NeurIPS 2017