I wanted to discuss two really interesting papers in the field of Hyperparameter Optimization:
Li, L., Jamieson, K.G., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2017, April. Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. In ICLR (Poster).
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2017. Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 18(1), pp.6765–6816.
These two papers discuss a very novel way of identifying optimal set of input values (parameters) that when identified fine-tune the model to yield the best results. The goal of optimization in…