Daniele Calandriello

31 papers · 2013–2025 · 6 conferences · across top CS/AI conferences

Achievements

+12 more ↓

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (6) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (12)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (9) 🤝 Dynamic Duo (21) 👑 Triple Crown 🔬 Deep Specialist (10) 💎 Century Club (31) 🚀 Conference Pioneer 🗃️ Keyword Collector (114) 📈 Trend Setter ⚡ Prolific Year (8) 🔥 Unstoppable (9)

Conferences

ICML (12) NIPS (11) ICLR (4) AISTATS (2) COLT (1) JMLR (1)

Top co-authors

Michal Valko (21) Rémi Munos (10) Bilal Piot (9) Alessandro Lazaric (8) Yunhao Tang (6) Lorenzo Rosasco (6) Mark Rowland (6) Daniil Tiapkin (5) Zhaohan Daniel Guo (5) Luigi Carratino (5)

Keywords

regret bound (4) bayesian optimization (3) sampling algorithm (3) reinforcement learning (3) gaussian process (3) matrix approximation (2) kernel methods (2) subset selection (2) determinantal point process (2) policy optimization (2) kernel ridge regression (2) posterior sampling (2) policy iteration (2) sample complexity (2) matrix sketching (2) graph learning (1) bayesian reinforcement learning (1) multi-task learning (1) bayesian inference (1) graph laplacian (1)

Papers

Building Math Agents with Multi-Turn Iterative Preference Learning ICLR 2025 On Teacher Hacking in Language Model Distillation ICML 2025 Nash Learning from Human Feedback ICML 2024 Decoding-time Realignment of Language Models ICML 2024 Human Alignment of Large Language Models through Online Preference Optimisation ICML 2024 Unlocking the Power of Representations in Long-term Novelty-based Exploration ICLR 2024 Generalized Preference Optimization: A Unified Approach to Offline Alignment ICML 2024 Multi-turn Reinforcement Learning with Preference Human Feedback NIPS 2024 A General Theoretical Paradigm to Understand Learning from Human Preferences AISTATS 2024 Demonstration-Regularized RL ICLR 2024 Understanding Self-Predictive Learning for Reinforcement Learning ICML 2023 Fast Rates for Maximum Entropy Exploration ICML 2023 Model-free Posterior Sampling via Learning Rate Randomization NIPS 2023 Information-theoretic Online Memory Selection for Continual Learning ICLR 2022 Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees NIPS 2022 BYOL-Explore: Exploration by Bootstrapped Prediction NIPS 2022 Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times ICML 2022 Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach JMLR 2021 ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions NIPS 2021 Near-linear time Gaussian process optimization with adaptive batching and resparsification ICML 2020 Sampling from a k-DPP without looking at all items NIPS 2020 Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret COLT 2019 Exact sampling of determinantal point processes with sublinear time preprocessing NIPS 2019 Statistical and Computational Trade-Offs in Kernel K-Means NIPS 2018 On Fast Leverage Score Sampling and Optimal Learning NIPS 2018 Improved large-scale graph learning through ridge spectral sparsification ICML 2018 Second-Order Kernel Online Convex Optimization with Adaptive Sketching ICML 2017 Distributed Adaptive Sampling for Kernel Matrix Approximation AISTATS 2017 Efficient Second-Order Online Kernel Learning with Adaptive Embedding NIPS 2017 Sparse Multi-Task Reinforcement Learning NIPS 2014 Safe Policy Iteration ICML 2013