Practical Bayesian optimization with application to tuning machine learning algorithms
Bayesian optimization has recently emerged in the machine learning community as a very effective automatic alternative to the tedious task of handtuning algorithm hyperparameters. Although it is a relatively new aspect of machine learning, it has known roots in the Bayesian experimental design (Lindley, 1956; Chaloner and Verdinelli, 1995) the design and analysis of computer experiments (DACE; Sacks et al. 1989) Kriging (Krige, 1951) and multi armed bandits (Gittins, 1979) In this thesis, we motivate and introduce the model based optimization framework and provide some historical context to the technique that dates back as far as 1933 with application to clinical drug trials (Thompson, 1933) Contributions of this work include a Bayesian gap based exploration policy, inspired by Gabillon et al. (2012) a principled information theoretic portfolio strategy, out performing the portfolio of Hoffman et al. (2011) and a general practical technique circumventing the need for an initial bounding box. These various works each address existing practical challenges in the way of more widespread adoption of probabilistic model based optimization techniques. Finally, we conclude this thesis with important directions for future research, emphasizing scalability and computational feasibility of the approach as a general purpose optimizer.