This page contains my academic papers written in English.
Some other articles written in French are available here.
List of my publications:
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HTML,
BibTex.
My academic papers are also available on HAL - Inria (open archive).
Ph.D. Thesis in Computer Science
Hybridization of dynamic optimization methodologies
This thesis is dedicated to sequential decision making (also known as
multistage optimization) in uncertain complex environments. Studied algorithms
are essentially applied to power systems ("Unit Commitment" problems).
Among my main contributions, I present a new algorithm named "Direct Value
Search" (DVS), designed to solve large scale unit commitment problems with few
assumptions on the model, and to tackle some new challenges in the Power Systems
community.
Noisy optimization (a key component of the DVS algorithm) is studied and a
new convergence bound proved. Some variance reduction techniques aimed at
improving the convergence rate of graybox noisy optimization problems are also
presented.
Presented and publicly defended on November 28 2014 in Orsay, France.
Laboratory: Inria Saclay / LRI (Université Paris-Sud 11)
Advisor: Olivier Teytaud
Reviewers: Pierre-Olivier Malaterre and Liva Ralaivola
Jérémie Decock. Hybridization of dynamic optimization methodologies.
Theses, Université Paris-Sud, November 2014.
Academic Papers in Artificial Intelligence and Applied Maths
An Introduction to Direct Value Search
Direct Policy Search (DPS) is a widely used tool for
reinforcement learning; however, it is usually not suitable for
handling high-dimensional constrained action spaces such as
those arising in power system control (unit commitment
problems). We propose Direct Value Search, an hybridization of
DPS with Bellman decomposition techniques. We prove runtime
properties, and apply the results to an energy management
problem.
9èmes Journées Francophones de Planification, Décision et
Apprentissage (JFPDA'14), Liège (Belgique), May 2014.
Direct model predictive control
Due to simplicity and convenience, Model Predictive Control,
which consists in optimizing future decisions based on a
pessimistic deterministic forecast of the random processes, is
one of the main tools for stochastic control. Yet, it suffers
from a large computation time, unless the tactical horizon
(i.e. the number of future time steps included in the
optimization) is strongly reduced, and lack of real
stochasticity handling. We here propose a combination between
Model Predictive Control and Direct Policy Search.
Jean-Joseph Christophe, Jérémie Decock, and Olivier Teytaud.
Direct model predictive control.
In European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning (ESANN), Bruges, Belgique,
April 2014.
Linear Convergence of Evolution Strategies with Derandomized Sampling Beyond Quasi-Convex Functions
We study the linear convergence of a simple evolutionary
algorithm on non quasi-convex functions on continuous domains.
Assumptions include an assumption on the sampling performed by
the evolutionary algorithm (supposed to cover efficiently the
neighborhood of the current search point), the conditioning of
the objective function (so that the probability of improvement
is not too low at each time step, given a correct step size),
and the unicity of the optimum.
Jérémie Decock and Olivier Teytaud.
Linear Convergence of Evolution Strategies with Derandomized
Sampling Beyond Quasi-Convex Functions.
In EA - 11th Biennal International Conference on Artificial
Evolution - 2013, Lecture Notes in Computer Science, Bordeaux, France,
August 2013. Springer.
Noisy Optimization Complexity Under Locality Assumption
In spite of various recent publications on the subject, there
are still gaps between upper and lower bounds in evolutionary
optimization for noisy objective function. In this paper we
reduce the gap, and get tight bounds within logarithmic factors
in the case of small noise and no long-distance influence on
the objective function.
Jérémie Decock and Olivier Teytaud.
Noisy Optimization Complexity Under Locality Assumption.
In FOGA - Foundations of Genetic Algorithms XII - 2013,
Adelaide, Australie, February 2013.
Learning Cost-Efficient Control Policies with XCSF
In this paper we present a method based on the "learning from
demonstration" paradigm to get a cost-efficient control policy
in a continuous state and action space. The controlled plant is
a two degrees-of-freedom planar arm actuated by six muscles. We
learn a parametric control policy with xcsf from a few
near-optimal trajectories, and we study its capability to
generalize over the whole reachable space. Furthermore, we show
that an additional Cross-Entropy Policy Search method can
improve the global performance of the parametric controller.
Didier Marin, Jérémie Decock, Lionel Rigoux, and Olivier Sigaud.
Learning Cost-Efficient Control Policies with XCSF: Generalization
Capabilities and Further Improvement.
In Proceedings of the 13th annual conference on Genetic and
evolutionary computation (GECCO'11), pages 1235-1242, Dublin, Irlande, 2011.
