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 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).

Weaknesses: Unfortunately, I strongly believe that this paper will have a very limited attraction from the research community since derivations are done for binary classification and the nearest neighbor classification is no longer popular as before as there are numerous good alternatives. To validate my claim, I looked at the recent Neurips 2019 paper cited as [45] which is quite similar to this paper. In one year, it is cited only once. This is quite natural in my opinion since deep neural networks dominated classification and there are good alternatives to the nearest neighbor classification for large-scale data as hashing, approximate nearest neighbor classification methods, etc. Especially, unsupervised and supervised hashing methods are quite popular for large-scale data with high-dimensional feature spaces. Therefore, I strongly believe that the impact of the paper is very limited and it will attract a very few attention from research community.

Ranking and selection (R&S) conventionally aims to select the unique best alternative with the largest mean performance from a finite set of alternatives. However, for better supporting decision making, it may be more informative to deliver a small menu of alternatives whose mean performances are among the top $m$. Such problem, called optimal subset selection (OSS), is generally more challenging to address than the conventional R&S. This challenge becomes even more significant when the number of alternatives is considerably large. Thus, the focus of this paper is on addressing the large-scale OSS problem. To achieve this goal, we design a top-$m$ greedy selection mechanism that keeps sampling the current top $m$ alternatives with top $m$ running sample means and propose the explore-first top-$m$ greedy (EFG-$m$) procedure. Through an extended boundary-crossing framework, we prove that the EFG-$m$ procedure is both sample optimal and consistent in terms of the probability of good selection, confirming its effectiveness in solving large-scale OSS problem. Surprisingly, we also demonstrate that the EFG-$m$ procedure enables to achieve an indifference-based ranking within the selected subset of alternatives at no extra cost. This is highly beneficial as it delivers deeper insights to decision-makers, enabling more informed decision-makings. Lastly, numerical experiments validate our results and demonstrate the efficiency of our procedures.

Coding Update: Challenges, My Latest Project, and Website Optimization. Coding Update: Challenges, My Latest Project, and Website Optimization.

DALLE-2 can produce photorealistic graphics from natural language descriptions. While such models are adaptable, they fail to comprehend some notions, such as object relationships. This article proposes a newest AI art generating method. A alternative diffusion model can generate images conditioned on your sentence descriptions.

Confession time: I really enjoy using AirPods. I got them as a Christmas gift, and have consistently worn them ever since. But Amazon has come pretty close to giving my choice of wireless earbuds a second thought with the release of its second-generation Echo Buds. They fit really well, sound great, and offer a lot of what AirPods give you at a lower price. My history with wireless earbud wearing hasn't been great.

Slinko and White, (2008) have recently introduced a new model of coalitional manipulation of voting rules under limited communication, which they call safe strategic voting. The computational aspects of this model were first studied by Hazon and Elkind, (2010), who provide polynomial-time algorithms for finding a safe strategic vote under k-approval and the Bucklin rule. In this paper, we answer an open question of Hazon and Elkind, (2010) by presenting a polynomial-time algorithm for finding a safe strategic vote under the Borda rule. Our results for Borda generalize to several interesting classes of scoring rules.