Zobrazeno 1 - 10
of 198
pro vyhledávání: '"Vidyasagar, Mathukumalli"'
Autor:
Vidyasagar, Mathukumalli
In this paper, we present a brief survey of Reinforcement Learning (RL), with particular emphasis on Stochastic Approximation (SA) as a unifying theme. The scope of the paper includes Markov Reward Processes, Markov Decision Processes, Stochastic App
Externí odkaz:
http://arxiv.org/abs/2304.00803
In this paper, we study the well-known "Heavy Ball" method for convex and nonconvex optimization introduced by Polyak in 1964, and establish its convergence under a variety of situations. Traditionally, most algorithms use "full-coordinate update," t
Externí odkaz:
http://arxiv.org/abs/2303.16241
We consider the problem of estimating a large causal polytree from a relatively small i.i.d. sample. This is motivated by the problem of determining causal structure when the number of variables is very large compared to the sample size, such as in g
Externí odkaz:
http://arxiv.org/abs/2209.07028
Autor:
Agrawal, Manindra, Kanitkar, Madhuri, Phillip, Deepu, Hajra, Tanima, Singh, Arti, Singh, Avaneesh, Singh, Prabal Pratap, Vidyasagar, Mathukumalli
The Covid-19 pandemic has two key properties: (i) asymptomatic cases (both detected and undetected) that can result in new infections, and (ii) time-varying characteristics due to new variants, Non-Pharmaceutical Interventions etc. We develop a model
Externí odkaz:
http://arxiv.org/abs/2101.09158
The objectives of this article are three-fold. Firstly, we present for the first time explicit constructions of an infinite family of \textit{unbalanced} Ramanujan bigraphs. Secondly, we revisit some of the known methods for constructing Ramanujan gr
Externí odkaz:
http://arxiv.org/abs/1910.03937
In this paper we study the matrix completion problem: Suppose $X \in {\mathbb R}^{n_r \times n_c}$ is unknown except for a known upper bound $r$ on its rank. By measuring a small number $m \ll n_r n_c$ of elements of $X$, is it possible to recover $X
Externí odkaz:
http://arxiv.org/abs/1908.00963
Autor:
Lotfi, Mahsa, Vidyasagar, Mathukumalli
In this paper, we study the problem of compressed sensing using binary measurement matrices and $\ell_1$-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust
Externí odkaz:
http://arxiv.org/abs/1808.03001
In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning. It is shown that the Vapnik-Chervonenkis (VC-) dimension of the set of half-spaces in $\mathbb{R}^n$ generate
Externí odkaz:
http://arxiv.org/abs/1710.07973