69 citations · 452 across the 35 of their papers we have counts for
11 papers · 1 filter
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym
Elena Raponi, Nathanael Rakotonirina Carraz, Jérémy Rapin +2
The growing ubiquity of machine learning (ML) has led it to enter various areas of computer science, including black-box optimization (BBO). Recent research is particularly concern…
Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants
Dennis J. N. J. Soemers, Vegard Mella, Eric Piette +3
In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games…
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
Laurent Meunier, Herilalaina Rakotoarison, Pak Kan Wong +5
Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and…
Adversarial Attacks on Linear Contextual Bandits
Evrard Garcelon, Baptiste Roziere, Laurent Meunier +4
Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education. In many of these domains, malicious…
Polygames: Improved Zero Learning
Tristan Cazenave, Yen-Chi Chen, Guan-Wei Chen +21
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connec…
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies
Laurent Meunier, Jamal Atif, Olivier Teytaud
We introduce a new black-box attack achieving state of the art performances. Our approach is based on a new objective function, borrowing ideas from -white box attacks…