Daniele Calandriello
31 papers · 2013–2025 · 6 venues · across top CS/AI venues
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Achievements
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Conference Pioneer
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Research topics
Machine Learning
(29)
Optimization & Theory
(25)
Learning Types Β· Machine Learning
(13)
Artificial Intelligence
(11)
Reinforcement Learning
(11)
Core AI
(11)
Methods
(11)
Core Methods
(9)
Mathematics & Optimization
(7)
Optimization
(7)
Deep Learning
(3)
Learning Types Β· Deep Learning
(2)
Mathematics
(2)
Natural Language Processing
(1)
Application Areas
(1)
Keywords
reinforcement learning
(4)
regret bound
(4)
sample complexity
(3)
sampling algorithm
(3)
gaussian process
(3)
bayesian optimization
(3)
kernel methods
(2)
human feedback
(2)
alignment
(2)
subset selection
(2)
kernel ridge regression
(2)
policy optimization
(2)
determinantal point process
(2)
policy iteration
(2)
direct preference optimization
(2)
posterior sampling
(2)
matrix approximation
(2)
nash equilibrium
(2)
matrix sketching
(2)
ipo
(2)
Papers
Building Math Agents with Multi-Turn Iterative Preference Learning
ICLR 2025
On Teacher Hacking in Language Model Distillation
ICML 2025
A General Theoretical Paradigm to Understand Learning from Human Preferences
AISTATS 2024
Decoding-time Realignment of Language Models
ICML 2024
Demonstration-Regularized RL
ICLR 2024
Generalized Preference Optimization: A Unified Approach to Offline Alignment
ICML 2024
Human Alignment of Large Language Models through Online Preference Optimisation
ICML 2024
Multi-turn Reinforcement Learning with Preference Human Feedback
NeurIPS 2024
Nash Learning from Human Feedback
ICML 2024
Unlocking the Power of Representations in Long-term Novelty-based Exploration
ICLR 2024
Fast Rates for Maximum Entropy Exploration
ICML 2023
Model-free Posterior Sampling via Learning Rate Randomization
NeurIPS 2023
Understanding Self-Predictive Learning for Reinforcement Learning
ICML 2023
BYOL-Explore: Exploration by Bootstrapped Prediction
NeurIPS 2022
Information-theoretic Online Memory Selection for Continual Learning
ICLR 2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
NeurIPS 2022
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
ICML 2022
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
NeurIPS 2021
Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach
JMLR 2021
Near-linear time Gaussian process optimization with adaptive batching and resparsification
ICML 2020
Sampling from a k-DPP without looking at all items
NeurIPS 2020
Exact sampling of determinantal point processes with sublinear time preprocessing
NeurIPS 2019
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
COLT 2019
Improved large-scale graph learning through ridge spectral sparsification
ICML 2018
On Fast Leverage Score Sampling and Optimal Learning
NeurIPS 2018
Statistical and Computational Trade-Offs in Kernel K-Means
NeurIPS 2018
Distributed Adaptive Sampling for Kernel Matrix Approximation
AISTATS 2017
Efficient Second-Order Online Kernel Learning with Adaptive Embedding
NeurIPS 2017
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
ICML 2017
Sparse Multi-Task Reinforcement Learning
NeurIPS 2014
Safe Policy Iteration
ICML 2013