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Mirco Mutti

20 papers · 2018–2025 · 6 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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+8 more ↓ πŸ—ΊοΈ Taxonomy Completionist (31) 🌍 Conference Polyglot Β· Silver πŸƒ Academic Marathon Β· Sprint πŸŒ‰ Interdisciplinary Bridge Β· Silver πŸ† Grand Slam
πŸ† Keyword Champion (2) πŸ† Grand Slam 🀝 Dynamic Duo (13) πŸ—ƒοΈ Keyword Collector (99) πŸ”₯ Unstoppable Β· Bronze ❓ The Questioner ⚑ Prolific Year Β· Productive πŸ’Ž Century Club Β· Bronze

Venues

ICML (8) AAAI (4) NeurIPS (3) AISTATS (2) ICLR (2) JMLR (1)

Top co-authors

Marcello Restelli (13) Riccardo De Santi (6) Alberto Maria Metelli (4) Aviv Tamar (2) Filippo Lazzati (2) Riccardo Zamboni (2) Piersilvio De Bartolomeis (2) Aldo Pacchiano (1) Mattia Mancassola (1) Alberto Marchesi (1)

Research topics

Machine Learning (17) Optimization & Theory (14) Reinforcement Learning (13) Methods (12) Learning Types Β· Machine Learning (8) Artificial Intelligence (7) Core AI (7) Applications (4) Mathematics & Optimization (2) Robotics (2) Bayesian & Probabilistic (2) Capabilities (2) Deep Learning (1) Core Methods (1) Learning Types Β· Deep Learning (1)

Keywords

reinforcement learning (6) policy gradient (5) markov decision process (4) sample efficiency (4) sample complexity (2) finite trial (2) convex reinforcement learning (2) model-based reinforcement learning (2) non-markovian policy (2) state entropy (2) regret bound (2) entropy maximization (2) policy optimization (2) inverse reinforcement learning (2) exploration strategy (2) state distribution (2) game theory (2) regret (2) posterior sampling (1) policy learning (1)

Papers

A Classification View on Meta Learning Bandits ICML 2025 A Theoretical Framework for Partially-Observed Reward States in RLHF ICLR 2025 Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story ICML 2025 Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning ICLR 2024 Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction ICML 2024 How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach NeurIPS 2024 How to Explore with Belief: State Entropy Maximization in POMDPs ICML 2024 Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms ICML 2024 Test-Time Regret Minimization in Meta Reinforcement Learning ICML 2024 A Tale of Sampling and Estimation in Discounted Reinforcement Learning AISTATS 2023 Convex Reinforcement Learning in Finite Trials JMLR 2023 Persuading Farsighted Receivers in MDPs: the Power of Honesty NeurIPS 2023 Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization AAAI 2023 Challenging Common Assumptions in Convex Reinforcement Learning NeurIPS 2022 Reward-Free Policy Space Compression for Reinforcement Learning AISTATS 2022 The Importance of Non-Markovianity in Maximum State Entropy Exploration ICML 2022 Unsupervised Reinforcement Learning in Multiple Environments AAAI 2022 Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate AAAI 2021 An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies AAAI 2020 Configurable Markov Decision Processes ICML 2018