activity
20152025
most citedOptimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search

14 citations · 52 across the 23 of their papers we have counts for

collaborators

29 papers

cs.AI2025★ 6 cited

Monte Carlo Graph Coloring

Tristan Cazenave, Benjamin Negrevergne, Florian Sikora

Graph Coloring is probably one of the most studied and famous problem in graph algorithms. Exact methods fail to solve instances with more than few hundred vertices, therefore, a l…

cs.AI2024★ 1 cited

Mixture of Public and Private Distributions in Imperfect Information Games

Jérôme Arjonilla, Abdallah Saffidine, Tristan Cazenave

In imperfect information games (e.g. Bridge, Skat, Poker), one of the fundamental considerations is to infer the missing information while at the same time avoiding the disclosure…

cs.IR2023★ 1 cited

On the Consistency of Average Embeddings for Item Recommendation

Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin +2

A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates t…

cs.IR2023★ 13 cited

A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services

Walid Bendada, Guillaume Salha-Galvan, Thomas Bouabça +1

Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their mus…

cs.AI2023★ 1 cited

Towards Tackling MaxSAT by Combining Nested Monte Carlo with Local Search

Hui Wang, Abdallah Saffidine, Tristan Cazenave

Recent work proposed the UCTMAXSAT algorithm to address Maximum Satisfiability Problems (MaxSAT) and shown improved performance over pure Stochastic Local Search algorithms (SLS).…

cs.AI2023

Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge

Quentin Cohen-Solal, Tristan Cazenave

In this paper, we extend the Descent framework, which enables learning and planning in the context of two-player games with perfect information, to the framework of stochastic game…