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.