Welcome to Marie-Liesse Cauwet's Homepage

I am a postdoctoral researcher at University of Liège in Belgium. I am working with Prof. Louis Wehenkel within the SYSTMOD research group. I completed my Ph.D. thesis on uncertainties in optimization at INRIA. My academic background and computer skills can be found in the following CV.

Contact
@ mlcauwet/at/ulg/dot/ac/dot/be
+32 4 366 37 70
Montefiore Institute, Quartier Polytech 1,
10, Allée de la découverte,
4000 Liège, Belgium

On the web:

My main interests are on power systems optimization. In particular, I am focusing on some aspects of blackout risk mitigation in the context of a massive introduction of renewable energy sources such as wind and solar power.

The tools used to deal with this topic come from the optimization community (black-box optimization, evolutionary algorithms, mathematical optimization), statistic/probabilistic community (concentration inequalities, Markov chain, model selection, VC-dimension) and machine learning community (supervised learning, reinforcement learning, neural networks).

Ph.D. Thesis

  • Uncertainties in Optimization. Thesis, Université Paris-Sud.[pdf]
  • Conference

    1. Noisy Optimization: Fast Convergence Rates with Comparison-Based Algorithms. Marie-Liesse Cauwet, Olivier Teytaud. Genetic and Evolutionary Computation Conference (GECCO), 2016. [pdf]
    2. Analysis of Different Types of Regret in Continuous Noisy Optimization. Sandra Astete-Morales, Marie-Liesse Cauwet, Olivier Teytaud. Genetic and Evolutionary Computation Conference (GECCO), 2016. [pdf]
    3. Multivariate bias reduction in capacity expansion planning. Marie-Liesse Cauwet, Olivier Teytaud. (Accepted) 19th Power System Computation Conference, 2016. [pdf]
    4. Depth, balancing, and limits of the Elo model. Marie-Liesse Cauwet, Olivier Teytaud, Tristan Cazenave, Abdallah Saffidine, Hua-Min Liang, Shi-Jim Yen, Hung-Hsuan Lin, I-Chen Wu. 2015 IEEE Conference on Computational Intelligence and Games. [pdf]
    5. Criteria and Convergence Rates in Noisy Optimization. Sandra Astete-Morales, Marie-Liesse Cauwet, Olivier Teytaud. (Short paper) Genetic and Evolutionary Computation Conference (GECCO), 2015. [pdf]
    6. Parallel Evolutionary Algorithms Performing Pairwise Comparisons. Marie-Liesse Cauwet, Olivier Teytaud, Shih-Yuan Chiu, Kuo-Min Lin, Shi-Jim Yen et al. Foundations of Genetic Algorithms, 2015. [pdf]
    7. Evolution Strategies with Additive Noise: A Convergence Rate Lower Bound. Sandra Astete-Morales, Marie-Liesse Cauwet, Olivier Teytaud. Foundations of Genetic Algorithms, 2015. [pdf]
    8. Noisy optimization : Convergence with a Fixed Number of Resamplings. Marie- Liesse Cauwet. Evostar, 2014. [pdf]
    9. Algorithm Portfolios for Noisy Optimization: Compare Solvers Early. Marie-Liesse Cauwet, Jialin Liu and Olivier Teytaud. The 8th Learning and Intelligent OptimizatioN Conference (LION8), 2014. [pdf]

    Journal

    1. Algorithm Portfolios for Noisy Optimization. Marie-Liesse Cauwet, Jialin Liu, Baptiste Rozière, Olivier Teytaud. Annals of Mathematics and Artificial Intelligence. [pdf]
    2. Simple and Cumulative Regret for Continuous Noisy Optimization. Sandra Astete-Morales, Marie-Liesse Cauwet, Jialin Liu, Olivier Teytaud. Theoretical Computer Science. [pdf]

    General audience

    1. Jeu de go : l’ordinateur plus fort que l’humain ? Marie-Liesse Cauwet, Olivier Teytaud. Libération 03/03/2016 [webpage]. CNRS, le journal, 03/03/2016. [webpage]
    2. La transition énergétique via les smarts grids. Marie-Liesse Cauwet, Olivier Teytaud. VRS n° 403, 12/2015, pages 32-35. [webpage]

    Teaching: 170h