Paper:
Workshop track -ICLR 2018 ACCELERATING NEURAL ARCHITECTURE SEARCH US- ING PERFORMANCE PREDICTION
Methods for neural network hyperparameter optimization and architecture search are computationally expensive due to the need to train a large number of model configurations. In this paper, we show that simple regression models can predict the final performance of partially trained model configurations using features based on network architectures, hyperparameters, and time-series validation performance data. We empirically show that our performance prediction models are much more accurate than prominent Bayesian counterparts, are simpler to implement, and are faster to train. Our models can predict final performance in both visual classification and language modeling domains, are effective for predicting performance of drastically varying model architectures, and can even generalize between model classes. Using these prediction models, we also implement an early stopping method for hyperparameter optimization and architecture search, which obtains a speedup of a factor up to 6x in both hyperparameter optimization and architecture search. Finally, we empirically show that our early stopping method can be seamlessly incorporated into both reinforcement learning-based architecture selection algorithms and bandit based search methods. Through extensive experimentation, we empirically show our performance prediction models and early stopping algorithm are state-of-the-art in terms of prediction accuracy and speedup achieved while still identifying the optimal model configurations.

Review:
This paper shows a simple method for predicting the performance that neural networks will achieve with a given architecture, hyperparameters, and based on an initial part of the learning curve.
The method assumes that it is possible to first execute 100 evaluations up to the total number of epochs.
From these 100 evaluations (with different hyperparameters / architectures), the final performance y_T is collected. Then, based on an arbitrary prefix of epochs y_{1:t}, a model can be learned to predict y_T.
There are T different models, one for each prefix y_{1:t} of length t. The type of model used is counterintuitive for me; why use a SVR model? Especially since uncertainty estimates are required, a Gaussian process would be the obvious choice. 
The predictions in Section 3 appear to be very good, and it is nice to see the ablation study.
Section 4 fails to mention that its use of performance prediction for early stopping follows exactly that of Domhan et al (2015) and that this is not a contribution of this paper; this feels a bit disingenious and should be fixed.
The section should also emphasize that the models discussed in this paper are only applicable for early stopping in cases where the function evaluation budget N is much larger than 100.
The emphasis on the computational demand of 1-3 minutes for LCE seems like a red herring: MetaQNN trained 2700 networks in 100 GPU days, i.e., about 1 network per GPU hour. It trained 20 epochs for the studied case of CIFAR, so 1-3 minutes per epoch on the CPU can be implemented with zero overhead while the network is training on the GPU. Therefore, the following sentence seems sensational without substance: "Therefore, on a full meta-modeling experiment involving thousands of neural network configurations, our method could be faster by several orders of magnitude as compared to LCE based on current implementations."
The experiment on fast Hyperband is very nice at first glance, but the longer I think about it the more questions I have. During the rebuttal I would ask the authors to extend f-Hyperband all the way to the right in Figure 6 (left) and particularly in Figure 6 (right). Especially in Figure 6 (right), the original Hyperband algorithm ends up higher than f-Hyperband. The question this leaves open is whether f-Hyperband would reach the same performance when continued or not. 
I would also request the paper not to casually mention the 7x speedup that can be found in the appendix, without quantifying this. This is only possible for a large number of 40 Hyperband iterations, and in the interesting cases of the first few iterations speedups are very small. Also, do the simulated speedup results in the appendix account for potentially stopping a new best configuration, or do they simply count how much computational time is saved, without looking at performance? The latter would of course be extremely misleading and should be fixed. I am looking forward to a clarification in the rebuttal period. 
For relating properly to the literatue, the experiment for speeding up Hyperband should also mention previous methods for speeding up Hyperband by a model (I only know one by the authors' reference Klein et al (2017)).
Overall, this paper appears very interesting. The proposed technique has some limitations, but in some settings it seems very useful. I am looking forward to the reply to my questions above; my final score will depend on these.
Typos / Details: 
- The range of the coefficient of determination is from 0 to 1. Table 1 probably reports 100 * R^2? Please fix the description.
- I did not see Table 1 referenced in the text.
- Page 3: "more computationally and" -> "more computationally efficient and"
- Page 3: "for performing final" -> "for predicting final"
Points in favor of the paper:
- Simple method
- Good prediction results
- Useful possible applications identified
Points against the paper:
- Methodological advances are limited / unmotivated choice of model
- Limited applicability to settings where >> 100 configurations can be run fully
- Possibly inflated results reported for Hyperband experiment