Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: The topic of hyperparameter optimisation is quite obviously pertinent to ICLR.
The approach the authors take seems straightforward and the results presented in the recently added full paper seem good. In particular the "real-time" updates of the forward mode optimisation are a useful innovation. I believe many ICLR attendants would find this interesting and useful.
review_title: appropriate subject, good analysis

final_score: rating: 6: Marginally above acceptance threshold
review_content: The paper considers an important topic in hyperparameter optimization of learning algorithms.
Unfortunately, due to the page limit of the workshop track the material is too compressed starting from 
page 1 where most variables can be guessed but are not defined (v_t, w_t, mu, eta, m and γ in "In this example, λ = (µ, γ)"). 
The paper might benefit from figures showing optimization trajectories of hyperparameters. 
The paper is missing the results regarding the scalability with the number of parameters and hyperparameters. 
The results with grid search baseline are preliminary as suggested by the authors. 
A supplementary/appendix figure (instead of Figure 1- Right) with the accuracy vs time for random search and the proposed approach might improve the presentation of the experimental results. 
review_title: Good paper

Decision: Accept