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Bachelor Thesis Proposition

The paper Augment and Reduce: Stochastic Inference for Large Categorical Distributions proposes to simplify multiclass classification for a large number of classes. The method proposed is based on probabilities but only aim at solving a maximum likelihood problem, the project would be to enlarge the scope of the algorithm with a prior by doing maximum-a-posteriori or maybe variational inference.

Please contact Theo Galy-Fajou if you are interested

Paper : https://arxiv.org/abs/1802.04220


Linear approaches to a stochastic mechanical control problem
Zitatschlüssel Mabrouk:2010:LAS
Autor Mahmoud Mabrouk
Jahr 2010
Schule TU Berlin
Zusammenfassung This thesis discusses a new method to linearize the Bellman equation for a special class of problems and tests its resulting algorithm with the state-of-the-art solutions. Reinforcement learning and Dynamic programming are presented and the state-of-the-art algorithms are discussed. The new framework and its mathematical foundations are then introduced. It results in a linear solution to the optimal action both in discrete and continuous domains, and in a new formulation of the cost-to-go function which exchanges the exhaustive search over actions with a linear solution. Later, an online and an offline algorithm are developed from the last results. They are tested against Policy Iteration and Q-Learning in a stochastic variant of the Mountain car problem. Results show a great improvement brought by the new algorithms both in speed and efficiency. Last, the limitations of the new framework are discussed.
Typ der Publikation Bachelor Thesis
Link zur Publikation Download Bibtex Eintrag

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