13 citations · 22 across the 5 of their papers we have counts for
5 papers
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?
Ming Li, Yuanna Liu, Sami Jullien +4
Next basket recommendation (NBR) is a special type of sequential recommendation that is increasingly receiving attention. So far, most NBR studies have focused on optimizing the ac…
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
Sami Jullien, Romain Deffayet, Jean-Michel Renders +2
Distributional reinforcement learning (RL) has proven useful in multiple benchmarks as it enables approximating the full distribution of returns and extracts rich feedback from env…
A Simulation Environment and Reinforcement Learning Method for Waste Reduction
Sami Jullien, Mozhdeh Ariannezhad, Paul Groth +1
In retail (e.g., grocery stores, apparel shops, online retailers), inventory managers have to balance short-term risk (no items to sell) with long-term-risk (over ordering leading…
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
Ana Lucic, Maurits Bleeker, Sami Jullien +2
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…
A Next Basket Recommendation Reality Check
Ming Li, Sami Jullien, Mozhdeh Ariannezhad +1
The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. Recently, a number of meth…