We thank all 3 reviewers for their thoughtful comments. " nearest neighbor theory papers have largely not worried too much about constants......This analysis is " In the evolution of the study of nearest neighbor, early work focused on consistency, and later Y ou are absolutely correct that very few work studies the constant. We argue that this is "a feature, not " The scope of the analysis is very limited to distributed nearest neighbor classification (along with some distributional The latter is a fairly interesting direction, due to its connection with deep learning. " Currently the paper has lots of small typos. Please proofread carefully and revise.. " Thanks for pointing out, and we " Also, I find T able 1 ... How is the risk percentage defined in comparison to the oracle KNN/OWNN? " I'd suggest adding error bars to T able 1 (for example, to denote standard deviations across experimental repeats).

We thank the reviewers for their thoughtful comments. An expander graph code allows simple, neurally plausible decoding to perform at par with BP . These expander codes can also be decoded by belief propagation (BP), but it's harder the other way around. We plan to follow this paper with another paper describing neuroscience applications. For space and coherence, this paper focuses on the conceptual theory without elaborating on applications.

Thanks all the reviewers for the detailed and thoughtful comments. HMM-based works [1, 2, 3], all of which proposed methods to estimate alignments from unsegmented data. We've not thoroughly explored to improve the duration predictor and simply follow the same We design the grouped 1x1 convolutions to be able to mix channels. For example, to generate a speech of 5.8 Therefore, adopting parallel TTS models significantly improves the sampling speed of end-to-end systems. In Section 5.3, we showed that varying temperature can change We will add a reference about Viterbi training.

We thank all 3 reviewers for their thoughtful comments. " nearest neighbor theory papers have largely not worried too much about constants......This analysis is " In the evolution of the study of nearest neighbor, early work focused on consistency, and later Y ou are absolutely correct that very few work studies the constant. We argue that this is "a feature, not " The scope of the analysis is very limited to distributed nearest neighbor classification (along with some distributional The latter is a fairly interesting direction, due to its connection with deep learning. " Currently the paper has lots of small typos. Please proofread carefully and revise.. " Thanks for pointing out, and we " Also, I find T able 1 ... How is the risk percentage defined in comparison to the oracle KNN/OWNN? " I'd suggest adding error bars to T able 1 (for example, to denote standard deviations across experimental repeats).

We thank all the reviewers for their time in reading our paper and providing thoughtful comments. Thank you for pointing out the typo. We note that we obtain a guarantee in expectation. We will add the details in the revised version. There are also "second-order" regret bounds which look at the "variance" We will add the detailed comparison in the revised version.

We first thank all reviewers for their thoughtful comments, and we wish everyone health during these hard times. We acknowledge the simplicity in our linear demand and reference price update models. These references are also discussed in Section 2 of the paper. The gradient of revenue can be calculated using estimated elasticity, observed sales (i.e. Assumption 1 is invoked in all theorems and lemmas of Section 5, and we will clearly state this in the revised paper. In the proof of Lemma 3.2, we show that This means if firms are willing to consider both prices near zero and those sufficiently large, Assumption 1 holds.