Personal Information
Unternehmen/Arbeitsplatz
Paris Area, France France
Beruf
Research Fellow
Branche
Technology / Software / Internet
Webseite
www.lri.fr/~teytaud/
Info
My work is about computers which make decisions.
Tags
artificial intelligence
evolutionary algorithms
games
game of go
power systems
black-box optimization
mcts
monte-carlo tree search
uct
upper confidence tree
deep networks
noisy optimization
optimization
open source
parallel optimization
partial observation
energy management
fully parallel optimization
energy transition
intelligence artificielle
monte carlo tree search
simulation-based optimization
direct policy search
killall-go
quasi-random numbers
bandits
comparison-based optimization
minesweeper
exp3
nash equilibria
parallelism
computational complexity
derivative free optimization
undecidability
unit commitment
random search
tuning
hyperparameter search
hyperparameter tuning
numerical optimization
particle swarm optimization
differential evolution
bayesian optimization
game of havannah
game of hex
alpha-zero
deep learning
zero learning
alphazero
computer vision
environment
water
hydroelectricity
reinforcement learning
wald criterion
uncertainty
savage criterion
bias correction
bootstrap
jackknife
open data
investments
public
continuous non-linear optimization
apprentissage automatique
vision artificielle
réseaux neuronaux profonds
evolution strategies
uncertainties
monte carlo tree search;upper confidence trees
planning;power systems
réseau éléctrique;centrales électriques
électricité;centrales de production;réseau de t
statistics;101
fuzzy control
go;ai;portfolio;random seeds
vc-dimension
dynamic programming
bandit
ucb
texp3
metagaming
power systems;markov decision processes; optimizat
power systems; optimization
noisy optimization; simulation-based optimization
model predictive control;stochastic dynamic progra
computational intelligence
operation research; introduction
artificial intelligence; games
monte-carlo tree seach; subgoal learning; macro-ac
sequential decision making
games; partial information; artificial intelligenc
planning
hidden information
unstructured optimization
simple regrets
opening book generation
go
7x7
shape game
stochastic modelling
random positions
estimation of distribution algorithms
complexity
automatic parallelization
reweightings
weights
partially observable game
exact solving
phantom games
variance
bias
provocative statements
energy
openoffice
linux
concurrent version systems
svn
tao team
computer games
zermelo algorithm
alpha-beta
multiobjective optimization
expensive optimization
multimodal optimization
global optimization
decidability
applications
machine learning
france
functional programming
multistage problems
bilevel problems
cumulative step-size adaptation
self-adaptation
emna
bias and variance
evolutionary algorithm
very short introduction to direct policy search
programming
development rules
debugging
juego del go
intelligencia artificial
memory in games
blind games
kriegspiel
phantom-go
endgames
partially observable games
batch active learning
active learning
complexity bounds
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Präsentationen
(70)Personal Information
Unternehmen/Arbeitsplatz
Paris Area, France France
Beruf
Research Fellow
Branche
Technology / Software / Internet
Webseite
www.lri.fr/~teytaud/
Info
My work is about computers which make decisions.
Tags
artificial intelligence
evolutionary algorithms
games
game of go
power systems
black-box optimization
mcts
monte-carlo tree search
uct
upper confidence tree
deep networks
noisy optimization
optimization
open source
parallel optimization
partial observation
energy management
fully parallel optimization
energy transition
intelligence artificielle
monte carlo tree search
simulation-based optimization
direct policy search
killall-go
quasi-random numbers
bandits
comparison-based optimization
minesweeper
exp3
nash equilibria
parallelism
computational complexity
derivative free optimization
undecidability
unit commitment
random search
tuning
hyperparameter search
hyperparameter tuning
numerical optimization
particle swarm optimization
differential evolution
bayesian optimization
game of havannah
game of hex
alpha-zero
deep learning
zero learning
alphazero
computer vision
environment
water
hydroelectricity
reinforcement learning
wald criterion
uncertainty
savage criterion
bias correction
bootstrap
jackknife
open data
investments
public
continuous non-linear optimization
apprentissage automatique
vision artificielle
réseaux neuronaux profonds
evolution strategies
uncertainties
monte carlo tree search;upper confidence trees
planning;power systems
réseau éléctrique;centrales électriques
électricité;centrales de production;réseau de t
statistics;101
fuzzy control
go;ai;portfolio;random seeds
vc-dimension
dynamic programming
bandit
ucb
texp3
metagaming
power systems;markov decision processes; optimizat
power systems; optimization
noisy optimization; simulation-based optimization
model predictive control;stochastic dynamic progra
computational intelligence
operation research; introduction
artificial intelligence; games
monte-carlo tree seach; subgoal learning; macro-ac
sequential decision making
games; partial information; artificial intelligenc
planning
hidden information
unstructured optimization
simple regrets
opening book generation
go
7x7
shape game
stochastic modelling
random positions
estimation of distribution algorithms
complexity
automatic parallelization
reweightings
weights
partially observable game
exact solving
phantom games
variance
bias
provocative statements
energy
openoffice
linux
concurrent version systems
svn
tao team
computer games
zermelo algorithm
alpha-beta
multiobjective optimization
expensive optimization
multimodal optimization
global optimization
decidability
applications
machine learning
france
functional programming
multistage problems
bilevel problems
cumulative step-size adaptation
self-adaptation
emna
bias and variance
evolutionary algorithm
very short introduction to direct policy search
programming
development rules
debugging
juego del go
intelligencia artificial
memory in games
blind games
kriegspiel
phantom-go
endgames
partially observable games
batch active learning
active learning
complexity bounds
Mehr anzeigen