S
Shalabh Bhatnagar
Researcher at Indian Institute of Science
Publications - 308
Citations - 5153
Shalabh Bhatnagar is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Stochastic approximation & Markov decision process. The author has an hindex of 30, co-authored 294 publications receiving 4300 citations. Previous affiliations of Shalabh Bhatnagar include University of Marne-la-Vallée & Indian Institutes of Technology.
Papers
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Journal ArticleDOI
An efficient ad recommendation system for TV programs
TL;DR: This paper presents a single end-to-end ad recommender system that considers all of these factors and recommends a set of well scheduled and sequenced ads that are the best suited for a given TV ad break and demonstrates that this strategy does indeed help sponsors to attract viewers’ attention while playing their ads during ad breaks of TV programs.
Proceedings ArticleDOI
A Markov decision process model for capacity expansion and allocation
TL;DR: In this article, a finite-horizon Markov decision process (MDP) model is presented for providing decision support in semiconductor manufacturing on such critical operational issues as when to add additional capacity and when to convert from one type of production to another.
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Nonextensive triangle equality and other properties of Tsallis relative-entropy minimization
TL;DR: This paper presents the properties of Tsallis relative-entropy minimization and highlights the use of the q-product, an operator that has been recently introduced to derive the mathematical structure behind theTsallis statistics.
Proceedings Article
Universal Option Models
TL;DR: It is proved that the UOM of an option can construct a traditional option model given a reward function, and also supports efficient computation of the option-conditional return, and extended to linear function approximation.
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Adaptive mean queue size and its rate of change: queue management with random dropping
TL;DR: In this article, an adaptive queue management with random dropping algorithm is proposed which incorporates information not just about the average queue size but also the rate of change of the same, introducing an adaptively changing threshold level that falls in between lower and upper thresholds.