no code implementations • 2 May 2024 • Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Mohammad Aliannejadi, Andrew Yates, Maarten de Rijke
So far, most NBR studies have focused on optimizing the accuracy of the recommendation, whereas optimizing for beyond-accuracy metrics, e. g., item fairness and diversity remains largely unexplored.
no code implementations • 26 May 2023 • Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth, Maarten de Rijke
Motivated by the efficiency of $L_2$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning while keeping an estimate of the full distribution of returns.
no code implementations • 30 May 2022 • Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke
We frame inventory restocking as a new reinforcement learning task that exhibits stochastic behavior conditioned on the agent's actions, making the environment partially observable.
Distributional Reinforcement Learning reinforcement-learning +1
no code implementations • 1 Nov 2021 • Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, Maarten de Rijke
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility.
1 code implementation • 29 Sep 2021 • Ming Li, Sami Jullien, Mozhdeh Ariannezhad, Maarten de Rijke
We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models.