2 citations · 2 across the 4 of their papers we have counts for
4 papers
Human Alignment of Large Language Models through Online Preference Optimisation
Daniele Calandriello, Daniel Guo, Remi Munos +10
Ensuring alignment of language models' outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensiv…
Unlocking the Power of Representations in Long-term Novelty-based Exploration
Alaa Saade, Steven Kapturowski, Daniele Calandriello +6
We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for…
Fast Rates for Maximum Entropy Exploration
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +7
We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards. In this work, we study the maxim…
Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting
Daniele Calandriello, Alessandro Lazaric, Michal Valko
We derive a new proof to show that the incremental resparsification algorithm proposed by Kelner and Levin (2013) produces a spectral sparsifier in high probability. We rigorously…