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Finite sample deviation and variance bounds for first order autoregressive processes
In this paper, we study finite-sample properties of the least squares es...
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Finite Sample-Size Regime of Testing Against Independence with Communication Constraints
The central problem of Hypothesis Testing (HT) consists in determining t...
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Resolution Limits of Non-Adaptive Querying for Noisy 20 Questions Estimation
We study fundamental limits of estimation accuracy for the noisy 20 ques...
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Universal Joint Image Clustering and Registration using Partition Information
We consider the problem of universal joint clustering and registration o...
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Gaussian Multiple and Random Access in the Finite Blocklength Regime
This paper presents finite-blocklength achievability bounds for the Gaus...
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Resolution Limits of Noisy 20 Questions Estimation
We establish fundamental limits on estimation accuracy for the noisy 20 ...
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Stochastic blockmodels with growing number of classes
We present asymptotic and finite-sample results on the use of stochastic...
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Finite-Sample Analysis of Image Registration
We study the problem of image registration in the finite-resolution regime and characterize the error probability of algorithms as a function of properties of the transformation and the image capture noise. Specifically, we define a channel-aware Feinstein decoder to obtain upper bounds on the minimum achievable error probability under finite resolution. We specifically focus on the higher-order terms and use Berry-Esseen type CLTs to obtain a stronger characterization of the achievability condition for the problem. Then, we derive a strong type-counting result to characterize the performance of the MMI decoder in terms of the maximum likelihood decoder, in a simplified setting of the problem. We then describe how this analysis, when related to the results from the channel-aware context provide stronger characterization of the finite-sample performance of universal image registration.
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