11 citations · 29 across the 7 of their papers we have counts for
10 papers
Fairness in generative modeling
Mariia Zameshina, Olivier Teytaud, Fabien Teytaud +4
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling. More precisely, to design fair algorithms for as many sensitive variab…
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj +6
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available compu…
EvolGAN: Evolutionary Generative Adversarial Networks
Baptiste Roziere, Fabien Teytaud, Vlad Hosu +4
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new…
Versatile Black-Box Optimization
Jialin Liu, Antoine Moreau, Mike Preuss +4
Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization. It is also a good tool for making evolutionary algorith…
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…
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
M. -L. Cauwet, C. Couprie, J. Dehos +5
Space-filling designs such as scrambled-Hammersley, Latin Hypercube Sampling and Jittered Sampling have been proposed for fully parallel hyperparameter search, and were shown to be…
BCMA-ES: A Bayesian approach to CMA-ES
Eric Benhamou, David Saltiel, Sebastien Verel +1
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum fol…
Automatically Reinforcing a Game AI
David L. St-Pierre, Jean-Baptiste Hoock, Jialin Liu +2
A recent research trend in Artificial Intelligence (AI) is the combination of several programs into one single, stronger, program; this is termed portfolio methods. We here investi…
Learning opening books in partially observable games: using random seeds in Phantom Go
Tristan Cazenave, Jialin Liu, Fabien Teytaud +1
Many artificial intelligences (AIs) are randomized. One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is…
A Critical Reassessment of Evolutionary Algorithms on the cryptanalysis of the simplified data encryption standard algorithm
Fabien Teytaud, Cyril Fonlupt
In this paper we analyze the cryptanalysis of the simplified data encryption standard algorithm using meta-heuristics and in particular genetic algorithms. The classic fitness func…