Zobrazeno 1 - 10
of 555
pro vyhledávání: '"Mukhopadhyay, Samrat"'
We address the problem of sparse recovery using greedy compressed sensing recovery algorithms, without explicit knowledge of the sparsity. Estimating the sparsity order is a crucial problem in many practical scenarios, e.g., wireless communications,
Externí odkaz:
http://arxiv.org/abs/2210.07800
We revisit the classic problem of optimal subset selection in the online learning set-up. Assume that the set $[N]$ consists of $N$ distinct elements. On the $t$th round, an adversary chooses a monotone reward function $f_t: 2^{[N]} \to \mathbb{R}_+$
Externí odkaz:
http://arxiv.org/abs/2209.14222
Publikováno v:
In International Journal of Biological Macromolecules October 2024 278 Part 1
Publikováno v:
In Signal Processing September 2024 222
We introduce the $\texttt{$k$-experts}$ problem - a generalization of the classic Prediction with Expert's Advice framework. Unlike the classic version, where the learner selects exactly one expert from a pool of $N$ experts at each round, in this pr
Externí odkaz:
http://arxiv.org/abs/2110.07881
Autor:
Sharma, Ankita, Verma, Chetna, Singh, Pratibha, Mukhopadhyay, Samrat, Gupta, Amlan, Gupta, Bhuvanesh
Publikováno v:
In International Journal of Biological Macromolecules April 2024 264 Part 2
Autor:
Mukhopadhyay, Samrat
In this paper we consider the problem of exact recovery of a fixed sparse vector with the measurement matrices sequentially arriving along with corresponding measurements. We propose an extension of the iterative hard thresholding (IHT) algorithm, te
Externí odkaz:
http://arxiv.org/abs/2103.00449
Autor:
Mukhopadhyay, Samrat, Sinha, Abhishek
We consider the classical uncoded caching problem from an online learning point-of-view. A cache of limited storage capacity can hold $C$ files at a time from a large catalog. A user requests an arbitrary file from the catalog at each time slot. Befo
Externí odkaz:
http://arxiv.org/abs/2101.07043
Autor:
Mukhopadhyay, Samrat
In this paper we theoretically study exact recovery of sparse vectors from compressed measurements by minimizing a general nonconvex function that can be decomposed into the sum of single variable functions belonging to a class of smooth nonconvex sp
Externí odkaz:
http://arxiv.org/abs/2010.09755