Olivier Teytaud

发表

On the Hardness of Offline Multi-objective Optimization

Olivier Teytaud, O. Teytaud, 2007, Evolutionary Computation.

On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies

Olivier Teytaud, Fabien Teytaud, 2009, EvoWorkshops.

Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models pdf

Olivier Teytaud, Antoine Moreau, Jean-Pierre Bertoglio, 2006, ArXiv.

Intelligent agents for games and computer Go

Olivier Teytaud, Chang-Shing Lee, 2011, 2011 IEEE Symposium on Intelligent Agent (IA).

The Rectangular Seeds of Domineering, Atari-Go and Breakthrough

Olivier Teytaud, Jialin Liu, Tristan Cazenave, 2015 .

Change Point Detection and Meta-Bandits for Online Learning in Dynamic Environments

Michèle Sebag, Olivier Teytaud, Cédric Hartland, 2007 .

A simple Tsumego Generator

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2012 .

FML-based type-2 fuzzy ontology for computer go knowledge representation

Olivier Teytaud, Chang-Shing Lee, Mei-Hui Wang, 2010, 2010 International Conference on System Science and Engineering.

Bayesian Networks: a Non-Frequentist Approach for Parametrization, and a more Accurate Structural Complexity Measure Bayesian Networks Learning

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2006, Rev. d'Intelligence Artif..

Bandit-Based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis

Olivier Teytaud, Philippe Rolet, O. Teytaud, 2010, LION.

Learning opening books in partially observable games: Using random seeds in Phantom Go

Olivier Teytaud, Fabien Teytaud, Jialin Liu, 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

Designing an Optimal Search Algorithm with Respect to Prior Information

Olivier Teytaud, Emmanuel Vázquez, O. Teytaud, 2014, Theory and Principled Methods for the Design of Metaheuristics.

Fuzzy Ontologies for the Game of Go

Olivier Teytaud, Chang-Shing Lee, Mei-Hui Wang, 2013, On Fuzziness.

T2FS-Based Adaptive Linguistic Assessment System for Semantic Analysis and Human Performance Evaluation on Game of Go

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2015, IEEE Transactions on Fuzzy Systems.

On Averaging the Best Samples in Evolutionary Computation

Yann Chevaleyre, Olivier Teytaud, Laurent Meunier, 2020, PPSN.

Apprentissage statistique et programmation génétique: la croissance du code est-elle inévitable?

Olivier Teytaud, Marc Schoenauer, Sylvain Gelly, 2005, CAP.

Towards a Solution of 7x7 Go with Meta-MCTS

Olivier Teytaud, Hui-Min Wang, Shi-Jim Yen, 2011, ACG.

Adding Expert Knowledge and Exploration in Monte-Carlo Tree Search

Olivier Teytaud, Christophe Fiter, Arpad Rimmel, 2009, ACG.

Nash reweighting of Monte Carlo simulations: Tsumego

Olivier Teytaud, Jialin Liu, David Lupien St-Pierre, 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

Bandit-Based Genetic Programming

Olivier Teytaud, Jean-Baptiste Hoock, O. Teytaud, 2010, EuroGP.

Contrôle du risque multiple pour la sélection de règles d'association significatives

Olivier Teytaud, Stéphane Lallich, Elie Prudhomme, 2004, EGC.

A Statistical Learning Perspective of Genetic Programming

Christian Gagné, Olivier Teytaud, Marc Schoenauer, 2009, EuroGP.

Optimization of Energy Policies Using Direct Value Search

Olivier Teytaud, Jérémie Decock, Jean-Joseph Christophe, 2014 .

Inspirational Adversarial Image Generation pdf

Olivier Teytaud, Yann LeCun, Camille Couprie, 2019, IEEE Transactions on Image Processing.

Multi-armed Bandit, Dynamic Environments and Meta-Bandits

Michèle Sebag, Olivier Teytaud, Cédric Hartland, 2006 .

Criteria and Convergence Rates in Noisy Optimization

Olivier Teytaud, Sandra Astete Morales, Marie-Liesse Cauwet, 2015, GECCO.

Sharing Information in Adversarial Bandit

Olivier Teytaud, David Lupien St-Pierre, 2014, EvoApplications.

Evolution Strategies with Additive Noise: A Convergence Rate Lower Bound

Olivier Teytaud, Sandra Astete Morales, Marie-Liesse Cauwet, 2015, FOGA.

QR mutations improve many evolution strategies -a lot on highly multimodal problems

Olivier Teytaud, Fabien Teytaud, 2016 .

Go Complexities

Olivier Teytaud, Shi-Jim Yen, Abdallah Saffidine, 2015, ACG.

The grand challenge of computer Go

Michèle Sebag, Olivier Teytaud, Marc Schoenauer, 2012, Commun. ACM.

Linear Convergence of Evolution Strategies with Derandomized Sampling Beyond Quasi-Convex Functions

Olivier Teytaud, Jérémie Decock, 2013, Artificial Evolution.

Combining policies: the best of human expertise and neurocontrol

Olivier Teytaud, Vincent Berthier, Adrien Couëtoux, 2015 .

Grid Coevolution for Adaptive Simulations: Application to the Building of Opening Books in the Game of Go

Olivier Teytaud, Julien Perez, Arpad Rimmel, 2009, EvoWorkshops.

Artificial Intelligence for Games

Olivier Teytaud, Bruno Bouzy, Tristan Cazenave, 2020, A Guided Tour of Artificial Intelligence Research.

Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search

Olivier Teytaud, Hassen Doghmen, Vincent Berthier, 2010, LION.

Algorithm Portfolios for Noisy Optimization: Compare Solvers Early

Olivier Teytaud, Jialin Liu, Marie-Liesse Cauwet, 2014, LION.

Random positions in Go

Olivier Teytaud, Shi-Jim Yen, Fabien Teytaud, 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

Robust Optimizers for Nonlinear Programming in Approximate Dynamic Programming

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2009 .

Slightly Beyond Turing's Computability for Studying Genetic Programming

Olivier Teytaud, O. Teytaud, 2007, MCU.

Association Rule Interestingness: Measure and Statistical Validation

Olivier Teytaud, Stéphane Lallich, Elie Prudhomme, 2007, Quality Measures in Data Mining.

Online Sparse Bandit for Card Games

Olivier Teytaud, Quentin Louveaux, David Lupien St-Pierre, 2011, ACG.

Systèmes inductifs-déductifs: une approche statistique

Michèle Sebag, Olivier Teytaud, Nicolas Baskiotis, 2005, CAP.

Adaptive Noisy Optimization

Olivier Teytaud, Philippe Rolet, O. Teytaud, 2010, EvoApplications.

EvolGAN: Evolutionary Generative Adversarial Networks pdf

Olivier Teytaud, Fabien Teytaud, Vlad Hosu, 2020, ACCV.

Analysis of runtime of optimization algorithms for noisy functions over discrete codomains

Olivier Teytaud, Youhei Akimoto, Sandra Astete Morales, 2015, Theor. Comput. Sci..

Scenario-based decision-making for power systems investment planning pdf

Olivier Teytaud, Jialin Liu, O. Teytaud, 2016, ArXiv.

Complexity Bounds for Batch Active Learning in Classification

Olivier Teytaud, Philippe Rolet, O. Teytaud, 2010, ECML/PKDD.

Lemmas on partial observation, with application to phantom games

Olivier Teytaud, Fabien Teytaud, 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

Decidability of the halting problem for Matiyasevich deterministic machines

Olivier Teytaud, 2001, Theor. Comput. Sci..

Strategic Choices in Optimization

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2014, J. Inf. Sci. Eng..

On the Ultimate Convergence Rates for Isotropic Algorithms and the Best Choices Among Various Forms of Isotropy

Olivier Teytaud, Sylvain Gelly, Jérémie Mary, 2006, PPSN.

A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize

Olivier Teytaud, Marc Schoenauer, Fabien Teytaud, 2011, Artificial Evolution.

Genetic fuzzy markup language for game of NoGo

Hani Hagras, Olivier Teytaud, Chang-Shing Lee, 2012, Knowl. Based Syst..

Tarsier: Evolving Noise Injection in Super-Resolution GANs pdf

Olivier Teytaud, Camille Couprie, Vlad Hosu, 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

Deep Learning for General Game Playing with Ludii and Polygames

Olivier Teytaud, Dennis J. N. J. Soemers, Vegard Mella, 2021, J. Int. Comput. Games Assoc..

The IEEE SSCI 2011 Human vs. Computer Go Competition

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2011, J. Int. Comput. Games Assoc..

Simple and cumulative regret for continuous noisy optimization

Olivier Teytaud, Jialin Liu, Sandra Astete Morales, 2016, Theor. Comput. Sci..

Meta Monte-Carlo Tree Search for Automatic Opening Book Generation

Olivier Teytaud, Mark H. M. Winands, Julien Perez, 2009 .

Comparison-based complexity of multiobjective optimization

Olivier Teytaud, 2011, GECCO '11.

Adversarial Attacks on Linear Contextual Bandits pdf

Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta, 2020, NeurIPS.

Adaptive assessment system for human performance evaluation on game of go

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2013, 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY).

New Results - Continuous Optimization

Michèle Sebag, Olivier Teytaud, Anne Auger, 2013 .

Monte-Carlo Tree Search

Olivier Teytaud, Sylvain Gelly, Romaric Gaudel, 2012 .

Evolutionary optimization of low-discrepancy sequences

Christian Gagné, Olivier Teytaud, Denis Laurendeau, 2012, TOMC.

T2FML-based adaptive assessment system for computer game of Go

Olivier Teytaud, Hui-Min Wang, Shi-Jim Yen, 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

The Frontier of Decidability in Partially Observable Recursive Games

David Auger, Olivier Teytaud, O. Teytaud, 2012, Int. J. Found. Comput. Sci..

Computational Intelligence Meets Game of Go @ IEEE WCCI 2012 [Society Briefs]

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2012, IEEE Comput. Intell. Mag..

Modification of UCT with Patterns in Monte-Carlo Go

Olivier Teytaud, Sylvain Gelly, Rémi Munos, 2006 .

Variance Reduction in Population-Based Optimization: Application to Unit Commitment

Olivier Teytaud, Jialin Liu, Jérémie Decock, 2015, GECCO.

Analysis of Different Types of Regret in Continuous Noisy Optimization

Olivier Teytaud, Sandra Astete Morales, Marie-Liesse Cauwet, 2016, GECCO.

A statistical learning theory approach of bloat

Olivier Teytaud, Marc Schoenauer, Sylvain Gelly, 2005, GECCO '05.

Continuous lunches are free!

Olivier Teytaud, Anne Auger, 2007, GECCO '07.

Exploring the MLDA benchmark on the nevergrad platform

Olivier Teytaud, Mike Preuss, Marcus Gallagher, 2019, GECCO.

Proving μ>1 pdf

Yann Chevaleyre, Olivier Teytaud, Laurent Meunier, 2020, ArXiv.

Why one must use reweighting in estimation of distribution algorithms

Olivier Teytaud, Fabien Teytaud, 2009, GECCO '09.

Online Sparse Bandits

Olivier Teytaud, Quentin Louveaux, David L. Saint-Pierre, 2011, ACML 2011.

Algorithm portfolios for noisy optimization

Olivier Teytaud, Jialin Liu, Marie-Liesse Cauwet, 2015, Annals of Mathematics and Artificial Intelligence.

OpenDP a free Reinforcement Learning toolbox for discrete time control problems

Olivier Teytaud, Sylvain Gelly, 2006, NIPS 2006.

Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms

Olivier Teytaud, Anne Auger, O. Teytaud, 2010, Algorithmica.

A mathematically derived number of resamplings for noisy optimization

Olivier Teytaud, Jialin Liu, David Lupien St-Pierre, 2014, GECCO.

On the Codimension of the Set of Optima: Large Scale Optimisation with Few Relevant Variables

Olivier Teytaud, Vincent Berthier, O. Teytaud, 2015, Artificial Evolution.

Polygames: Improved Zero Learning pdf

Olivier Teytaud, Yen-Chi Chen, Shi-Jim Yen, 2020, J. Int. Comput. Games Assoc..

Intelligent for the Game of Go

Olivier Teytaud, Fabien Teytaud, Chang-Shing Lee, 2010 .

Toward Optimal Run Racing: Application to Deep Learning Calibration pdf

Michèle Sebag, Olivier Teytaud, Marc Schoenauer, 2017, ArXiv.

Application de l'apprentissage par renforcement a la gestion du risque

Olivier Teytaud, Arnaud Renaud, A Rene, 2003 .

Creating an Upper-Confidence-Tree Program for Havannah

Olivier Teytaud, Fabien Teytaud, O. Teytaud, 2009, ACG.

Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing

Olivier Teytaud, Philippe Rolet, Amine Bourki, 2010, ICINCO.

Improving the Exploration in Upper Confidence Trees

Olivier Teytaud, Hassen Doghmen, Adrien Couëtoux, 2012, LION.

Taylor-based pseudo-metrics for random process fitting in dynamic programming: expected loss minimization and risk management

Olivier Teytaud, Sylvain Gelly, Jérémie Mary, 2005, CAP.

Upper Confidence Trees with Short Term Partial Information

Olivier Teytaud, Sébastien Flory, O. Teytaud, 2011, EvoApplications.

Better Text Understanding Through Image-To-Text Transfer pdf

Olivier Teytaud, Sylvain Gelly, Olivier Bousquet, 2017, ArXiv.

Lower Bounds for Comparison Based Evolution Strategies Using VC-dimension and Sign Patterns

Olivier Teytaud, Hervé Fournier, O. Teytaud, 2011, Algorithmica.

Noisy optimization convergence rates

Olivier Teytaud, Jialin Liu, Sandra Astete Morales, 2013, GECCO '13 Companion.

A Principled Method for Exploiting Opening Books

Nataliya Sokolovska, Olivier Teytaud, Julien Perez, 2010, Computers and Games.

Computational and human intelligence in blind Go

Olivier Teytaud, Hui-Min Wang, Shi-Jim Yen, 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

Evolutionary algorithms converge towards evolved biological photonic structures

Olivier Teytaud, Antoine Moreau, Bodo D. Wilts, 2018, Scientific Reports.

Quasi-random Numbers Improve the CMA-ES on the BBOB Testbed

Olivier Teytaud, O. Teytaud, 2015, Artificial Evolution.

The rectangular seeds of Domineering

Olivier Teytaud, Jialin Liu, Tristan Cazenave, 2015, 2015 IEEE Conference on Computational Intelligence and Games (CIG).

Optimizing low-discrepancy sequences with an evolutionary algorithm

Christian Gagné, Olivier Teytaud, Denis Laurendeau, 2009, GECCO '09.

Yet another but more efficient black-box adversarial attack: tiling and evolution strategies pdf

Olivier Teytaud, Jamal Atif, Laurent Meunier, 2019, ArXiv.

Noisy Optimization: Fast Convergence Rates with Comparison-Based Algorithms

Olivier Teytaud, Marie-Liesse Cauwet, O. Teytaud, 2016, GECCO.

The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

Tzung-Pei Hong, Shang-Rong Tsai, Olivier Teytaud, 2009, IEEE Transactions on Computational Intelligence and AI in Games.

Noisy optimization complexity under locality assumption

Olivier Teytaud, Jérémie Decock, O. Teytaud, 2013, FOGA XII '13.

A Simple Yet Effective Resampling Rule in Noisy Evolutionary Optimization

Olivier Teytaud, Jialin Liu, O. Teytaud, 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

Sparse binary zero-sum games

David Auger, Olivier Teytaud, Jialin Liu, 2014, ACML.

Item response theory with fuzzy markup language for parameter estimation and validation

Olivier Teytaud, Chang-Shing Lee, Mei-Hui Wang, 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

On the adaptation of noise level for stochastic optimization

Olivier Teytaud, Anne Auger, O. Teytaud, 2007, 2007 IEEE Congress on Evolutionary Computation.

Bounds on the Generalization Ability of Bayesian Inference and Gibbs Algorithms

Olivier Teytaud, Hélène Paugam-Moisy, 2001, ICANN.

The Game of Go @ IEEE WCCI 2010 [Society Briefs]

Olivier Teytaud, Chang-Shing Lee, Mei-Hui Wang, 2010, IEEE Comput. Intell. Mag..

Progress Rate in Noisy Genetic Programming for Choosing λ

Olivier Teytaud, Jean-Baptiste Hoock, O. Teytaud, 2011 .

Creating an Upper-Condence-T ree program for Havannah

Olivier Teytaud, Fabien Teytaud, O. Teytaud, 2009 .

Lower Bounds for Evolution Strategies

Olivier Teytaud, O. Teytaud, 2011, Theory of Randomized Search Heuristics.

Evolutionary super-resolution

Olivier Teytaud, Camille Couprie, Vlad Hosu, 2020, GECCO Companion.

Kernel Based Image Classification

Olivier Teytaud, David Sarrut, O. Teytaud, 2001, ICANN.

Society briefs: the game of go @ IEEE WCCI 2010

Olivier Teytaud, Chang-Shing Lee, Mei-Hui Wang, 2009 .

Continuous Upper Confidence Trees with Polynomial Exploration - Consistency

David Auger, Olivier Teytaud, Adrien Couëtoux, 2013, ECML/PKDD.

PSO-Based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

Olivier Teytaud, Chang-Shing Lee, Mei-Hui Wang, 2018, IEEE Transactions on Fuzzy Systems.

DCMA: yet another derandomization in covariance-matrix-adaptation

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2007, GECCO '07.

Variance Reduction in Population-Based Optimization: Application to Unit Commitment

Olivier Teytaud, Jialin Liu, Jérémie Decock, 2015, Artificial Evolution.

9x9 GO AS BLACK WITH KOMI 7.5: AT LAST SOME GAMES WON AGAINST TOP PLAYERS IN THE DISADVANTAGEOUS SITUATION

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2009 .

Differential evolution for strongly noisy optimization: Use 1.01n resamplings at iteration n and reach the − 1/2 slope

Olivier Teytaud, Shi-Jim Yen, Fabien Teytaud, 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

New Results - Machine Learning for Stochastic Optimisation

Michèle Sebag, Olivier Teytaud, Anne Auger, 2007 .

A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns

Michèle Sebag, Olivier Teytaud, Julien Lefèvre, 2005, IJCAI.

Introduction de connaissances expertes en Bandit-Based Monte-Carlo Planning avec application au Computer-Go

Olivier Teytaud, Sylvain Gelly, Arpad Rimmel, 2008 .

On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers

Olivier Teytaud, Fabien Teytaud, Guillaume Chaslot, 2009, ESANN.

Learning for stochastic dynamic programming

Olivier Teytaud, Sylvain Gelly, Jérémie Mary, 2006, ESANN.

General Lower Bounds for Evolutionary Algorithms

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2006, PPSN.

Depth, balancing, and limits of the Elo model

Olivier Teytaud, Shi-Jim Yen, Tristan Cazenave, 2015, 2015 IEEE Conference on Computational Intelligence and Games (CIG).

Segmentation and Length Estimation of 3D Discrete Curves

Olivier Teytaud, Isabelle Debled-Rennesson, David Coeurjolly, 2000, Digital and Image Geometry.

Q-Learning with Double Progressive Widening: Application to Robotics

Nataliya Sokolovska, Olivier Teytaud, Mario Milone, 2011, ICONIP.

New Results - Fundamentals of Machine Learning, Knowledge Extraction and Data Mining

Olivier Teytaud, Antoine Cornuéjols, Michèle Sebag, 2004 .

Le bridge, nouveau défi de l'intelligence artificielle ?

Olivier Teytaud, Véronique Ventos, O. Teytaud, 2017, Rev. d'Intelligence Artif..

Continuous Rapid Action Value Estimates

Michèle Sebag, Olivier Teytaud, Mátyás Brendel, 2011, ACML 2011.

Active learning in regression, with application to stochastic dynamic programming

Olivier Teytaud, Sylvain Gelly, Jérémie Mary, 2007, ICINCO-ICSO.

The 2010 Contest: MOGOTW vs. Human Go Players

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2010 .

Fast Seed-Learning Algorithms for Games

Olivier Teytaud, Jialin Liu, Tristan Cazenave, 2016, Computers and Games.

Why Simulation-Based Approachs with Combined Fitness are a Good Approach for Mining Spaces of Turing-equivalent Functions

Olivier Teytaud, O. Teytaud, 2006, 2006 IEEE International Conference on Evolutionary Computation.

Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint

Olivier Teytaud, Marc Schoenauer, Sylvain Gelly, 2006, Rev. d'Intelligence Artif..

Adaptive estimation of normals and surface area for discrete 3-D objects: application to snow binary data from X-ray tomography

Olivier Teytaud, David Coeurjolly, Frédéric Flin, 2005, IEEE Transactions on Image Processing.

Nonlinear programming in approximate dynamic programming - bang-bang solutions, stock-management and unsmooth penalties

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2007, ICINCO-ICSO.

Exact Distributed Training: Random Forest with Billions of Examples pdf

Olivier Teytaud, Mathieu Guillame-Bert, O. Teytaud, 2018, ArXiv.

Human vs. Computer Go Competition in FUZZ-IEEE 2011

Olivier Teytaud, Shi-Jim Yen, Fabien Teytaud, 2011, J. Int. Comput. Games Assoc..

Learning a Move-Generator for Upper Confidence Trees

Olivier Teytaud, Hassen Doghmen, Adrien Couëtoux, 2013 .

Direct Model-Predictive Control

Olivier Teytaud, Jérémie Decock, Jean-Joseph Christophe, 2014, ESANN.

On the huge benefit of decisive moves in Monte-Carlo Tree Search algorithms

Olivier Teytaud, Fabien Teytaud, O. Teytaud, 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

A novel ontology for computer go knowledge management

Tzung-Pei Hong, Yau-Hwang Kuo, Olivier Teytaud, 2009, 2009 IEEE International Conference on Fuzzy Systems.

Surprising Strategies Obtained by Stochastic Optimization in Partially Observable Games

Olivier Teytaud, Marie-Liesse Cauwet, O. Teytaud, 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

Multigrid Convergence and Surface Area Estimation

Laure Tougne, Olivier Teytaud, David Coeurjolly, 2002, Theoretical Foundations of Computer Vision.

Comparison-Based Algorithms Are Robust and Randomized Algorithms Are Anytime

Olivier Teytaud, Sylvain Gelly, Sylvie Ruette, 2007, Evolutionary Computation.

New Results - Optimal Decision Making

Nataliya Sokolovska, David Auger, Michèle Sebag, 2011 .

Critical Hyper-Parameters: No Random, No Cry pdf

Olivier Teytaud, Sylvain Gelly, Olivier Bousquet, 2017, ArXiv.

Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms

Olivier Teytaud, Fabien Teytaud, O. Teytaud, 2014, Theory and Principled Methods for the Design of Metaheuristics.

Log-log Convergence for Noisy Optimization

Olivier Teytaud, Jialin Liu, Sandra Astete Morales, 2013, Artificial Evolution.

Conditioning, Halting Criteria and Choosing lambda

Olivier Teytaud, 2007, Artificial Evolution.

Meta Online Learning: Experiments on a Unit Commitment Problem

Olivier Teytaud, Jialin Liu, 2014, ESANN.

Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking pdf

Olivier Teytaud, Carola Doerr, Laurent Meunier, 2020, ArXiv.

New Results - Optimal Decision Making under Uncertainty

Michèle Sebag, Olivier Teytaud, Marc Schoenauer, 2013 .

Optimal robust expensive optimization is tractable

Michèle Sebag, Olivier Teytaud, Philippe Rolet, 2009, GECCO.

Combiner connaissances expertes, hors-ligne, transientes et en ligne pour l'exploration Monte-Carlo. Apprentissage et MC

Olivier Teytaud, Christophe Fiter, Julien Perez, 2009, Rev. d'Intelligence Artif..

Local and global order 3/2 convergence of a surrogate evolutionary algorithm

Olivier Teytaud, Anne Auger, Marc Schoenauer, 2005, GECCO '05.

The Nash and the bandit approaches for adversarial portfolios

Olivier Teytaud, David Lupien St-Pierre, 2014, 2014 IEEE Conference on Computational Intelligence and Games.

Variance Reduction for Better Sampling in Continuous Domains pdf

Olivier Teytaud, Carola Doerr, Laurent Meunier, 2020, PPSN.

Special Issue on Monte Carlo Techniques and Computer Go

Olivier Teytaud, Chang-Shing Lee, Martin Müller, 2010, IEEE Trans. Comput. Intell. AI Games.

Log(lambda) Modifications for Optimal Parallelism

Olivier Teytaud, Fabien Teytaud, O. Teytaud, 2010, PPSN.

Statistical asymptotic and non-asymptotic consistency of bayesian networks: convergence to the right structure and consistent probability estimates

Olivier Teytaud, Sylvain Gelly, 2005, CAP.

Intelligent Agents for the Game of Go

Olivier Teytaud, Fabien Teytaud, Chang-Shing Lee, 2010, IEEE Computational Intelligence Magazine.

Multi-objective Multi-modal Optimization for Mining Spatio-temporal Patterns

Michèle Sebag, Olivier Teytaud, Julien Lefèvre, 2005, CAP.

Bias and variance in continuous EDA

Olivier Teytaud, Fabien Teytaud, 2009 .

Population control meets doob's martingale theorems: the noise-free multimodal case

Olivier Teytaud, Marie-Liesse Cauwet, 2020, GECCO Companion.

Efficient Decision Making under Uncertainty in a Power System Investment Problem

Olivier Teytaud, Jialin Liu, O. Teytaud, 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

Biasing Monte-Carlo Simulations through RAVE Values

Olivier Teytaud, Fabien Teytaud, Arpad Rimmel, 2010, Computers and Games.

Openly revisiting derivative-free optimization

Olivier Teytaud, Camille Couprie, Nicolas Vasilache, 2019, GECCO.

Identification de la langue et catégorisation de textes basées sur les N-grammes

Olivier Teytaud, Radwan Jalam, 2001, EGC.

Parallel Evolutionary Algorithms Performing Pairwise Comparisons

Olivier Teytaud, Shi-Jim Yen, Fabien Teytaud, 2015, FOGA.

Resource-Aware Parameterizations of EDA

Christian Gagné, Olivier Teytaud, Sylvain Gelly, 2006, 2006 IEEE International Conference on Evolutionary Computation.

Revisiting Monte-Carlo Tree Search on a Normal Form Game: NoGo

Olivier Teytaud, Shi-Jim Yen, Cheng-Wei Chou, 2011, EvoApplications.

Boosting a Bridge Artificial Intelligence

Olivier Teytaud, Véronique Ventos, Yves Costel, 2017, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).

Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits

Michèle Sebag, Olivier Teytaud, Nicolas Galichet, 2013, ACML.

Algorithms (X, sigma, eta): Quasi-random Mutations for Evolution Strategies

Olivier Teytaud, Anne Auger, Mohamed Jebalia, 2005, Artificial Evolution.

Handling expensive optimization with large noise

Nataliya Sokolovska, Olivier Teytaud, Philippe Rolet, 2011, FOGA '11.

On the Parallelization of Monte-Carlo planning

Olivier Teytaud, Sylvain Gelly, Arpad Rimmel, 2008, ICINCO 2008.

Ontology-based Intelligent Agent for Game of Go

Olivier Teytaud, Chang-Shing Lee, Mei-Hui Wang, 2010, 2010 International Conference on Technologies and Applications of Artificial Intelligence.

Automatically Reinforcing a Game AI pdf

Olivier Teytaud, Fabien Teytaud, Jialin Liu, 2016, ArXiv.

Lower Bounds for Training and Leave-One-Out Estimates of the Generalization Error

Olivier Teytaud, Gérald Gavin, 2002, ICANN.

Multivariate bias reduction in capacity expansion planning

Olivier Teytaud, Marie-Liesse Cauwet, 2016, 2016 Power Systems Computation Conference (PSCC).

Current Frontiers in Computer Go

Shang-Rong Tsai, Olivier Teytaud, Shi-Jim Yen, 2010, IEEE Transactions on Computational Intelligence and AI in Games.

The Parallelization of Monte-Carlo Planning - Parallelization of MC-Planning

Olivier Teytaud, Sylvain Gelly, Arpad Rimmel, 2008, ICINCO-ICSO.

First Computer Wins in 9x9 Go for Black with Komi 7.5

Olivier Teytaud, C-S. Lee, S. Billouet, 2009 .

New Results - Stochastic Optimisation for ML and DM

Olivier Teytaud, Xiangliang Zhang, Antoine Cornuéjols, 2007 .

Simple tools for multimodal optimization

Olivier Teytaud, Marc Schoenauer, Fabien Teytaud, 2011, GECCO '11.

When Does Quasi-random Work?

Olivier Teytaud, O. Teytaud, 2008, PPSN.

Open source evolutionary structured optimization

Olivier Teytaud, Jeremy Rapin, Daniel Haziza, 2020, GECCO Companion.

Continuous Upper Confidence Trees

Nataliya Sokolovska, Olivier Teytaud, Jean-Baptiste Hoock, 2011, LION.

Human vs. Machine Go Competition in IEEE WCCCI 2012

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2012, J. Int. Comput. Games Assoc..

Human vs. Computer-Go Competition

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2009 .

Convergence proofs, convergence rates and stopping criterions for multi-modal or multi-objective evolutionary algorithms

Michèle Sebag, Olivier Teytaud, Yann Bonnemay, 2004 .

Upper Confidence Tree-Based Consistent Reactive Planning Application to MineSweeper

Michèle Sebag, Olivier Teytaud, 2012, LION.

Lower Bounds for Evolution Strategies Using VC-Dimension

Olivier Teytaud, Hervé Fournier, O. Teytaud, 2008, PPSN.

Scalability and Parallelization of Monte-Carlo Tree Search

Thomas Hérault, Olivier Teytaud, Fabien Teytaud, 2010, Computers and Games.

QR Mutations Improve Many Evolution Strategies: A Lot On Highly Multimodal Problems

Olivier Teytaud, Fabien Teytaud, O. Teytaud, 2016, GECCO.

Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm

Michèle Sebag, Olivier Teytaud, Philippe Rolet, 2009, ECML/PKDD.

Versatile black-box optimization

Olivier Teytaud, Fabien Teytaud, Mike Preuss, 2020, GECCO.

Sieves method in fuzzy control: Logarithmically increase the number of rules

Olivier Teytaud, Vincent Berthier, O. Teytaud, 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

Consistent population control: generate plenty of points, but with a bit of resampling

Olivier Teytaud, Jeremy Rapin, Vasil Khalidov, 2019, FOGA '19.

Strategic Choices: Small Budgets and Simple Regret

Olivier Teytaud, Shi-Jim Yen, Chang-Shing Lee, 2012, 2012 Conference on Technologies and Applications of Artificial Intelligence.

HUMAN-COMPUTER GO REVOLUTION 2008

Shang-Rong Tsai, Olivier Teytaud, Chang-Shing Lee, 2008 .

Asymptotic convergence rates for averaging strategies

Yann Chevaleyre, Olivier Teytaud, Laurent Meunier, 2021, FOGA.

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research pdf

Olivier Teytaud, Brandon Cui, Hugh Leather, 2021, 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO).

Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants pdf

Olivier Teytaud, Dennis J. N. J. Soemers, Cameron Browne, 2021, ArXiv.

Seed Optimization Framework on Draughts

Olivier Teytaud, Shi-Jim Yen, Ching-Nung Lin, 2021, J. Inf. Sci. Eng..

Convergence speed of deformable models

Olivier Teytaud, David Sarrut, O. Teytaud, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Consistent Belief State Estimation, with Application to Mines

Olivier Teytaud, Adrien Couëtoux, O. Teytaud, 2011, 2011 International Conference on Technologies and Applications of Artificial Intelligence.

Statistical inference and data mining: false discoveries control

Olivier Teytaud, Stéphane Lallich, O. Teytaud, 2006 .

Contribution of statistical learning to validation of association rules

Olivier Teytaud, O. Teytaud, S. Lallich, 2001 .

An Experimental Evaluation of Reinforcement Learning for Gain Scheduling

Eric Moulines, Olivier Teytaud, Alessandra Russo, 2003, HIS.

Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint

Olivier Teytaud, Marc Schoenauer, Nicolas Bredeche, 2006, Rev. d'Intelligence Artif..