Task: Language Identification
Input (Spanish, English): The authors propose an approach to learning optimization algorithms by framing the problem as a policy search task, then using the guided policy search algorithm. The method is a nice contribution to the "learning to learn" framework, and actually was developed simultaneously to a few papers that have since already been published. It's definitely a useful addition to this space.    The biggest issue with this paper is that the results simply aren't that compelling. The methodology and proposed approach is nice, and the text is improved upon a previous version posted to Arxiv, but it seems that for most problems the results aren't that much better than some of the more common off-the-shelf optimization approaches that _don't_ require learning anything. Furthermore, in the one domain where the method does seem to (marginally) outperform the other methods, the neural net domain, it's unclear why we'd want to use the proposed approaches instead of SGD-based methods and their like (which of course everyone actually does for optimization).    Pros:  + Nice contribution to the learning to learn framework  + Takes a different approach from past (concurrent) work, namely one based upon policy search    Cons:  - Experiments are not particularly compelling    Overall, this work is a little borderline. Still, the PCs have determined that it was deserving of appearing at the conference. We hope the authors can strengthen the empirical validation for the camera ready version.
Output (English): en