Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Agrawal, Shubhada"'
We consider the problem of estimating the asymptotic variance of a function defined on a Markov chain, an important step for statistical inference of the stationary mean. We design a novel recursive estimator that requires $O(1)$ computation at each
Externí odkaz:
http://arxiv.org/abs/2409.05733
Top-$2$ methods have become popular in solving the best arm identification (BAI) problem. The best arm, or the arm with the largest mean amongst finitely many, is identified through an algorithm that at any sequential step independently pulls the emp
Externí odkaz:
http://arxiv.org/abs/2403.09123
We investigate the regret-minimisation problem in a multi-armed bandit setting with arbitrary corruptions. Similar to the classical setup, the agent receives rewards generated independently from the distribution of the arm chosen at each time. Howeve
Externí odkaz:
http://arxiv.org/abs/2309.16563
Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature. In this paper, we consider combining offline data with online learning, an area less studied but of
Externí odkaz:
http://arxiv.org/abs/2306.09048
Agent-based simulators (ABS) are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an epidemic in a city (or a region). They provide the flexibility to accurately model a heterogeneou
Externí odkaz:
http://arxiv.org/abs/2209.02887
We revisit the classic regret-minimization problem in the stochastic multi-armed bandit setting when the arm-distributions are allowed to be heavy-tailed. Regret minimization has been well studied in simpler settings of either bounded support reward
Externí odkaz:
http://arxiv.org/abs/2102.03734
Conditional value-at-risk (CVaR) and value-at-risk (VaR) are popular tail-risk measures in finance and insurance industries as well as in highly reliable, safety-critical uncertain environments where often the underlying probability distributions are
Externí odkaz:
http://arxiv.org/abs/2008.07606
Autor:
Agrawal, Shubhada, Bhandari, Siddharth, Bhattacharjee, Anirban, Deo, Anand, Dixit, Narendra M., Harsha, Prahladh, Juneja, Sandeep, Kesarwani, Poonam, Swamy, Aditya Krishna, Patil, Preetam, Rathod, Nihesh, Saptharishi, Ramprasad, Shriram, Sharad, Srivastava, Piyush, Sundaresan, Rajesh, Vaidhiyan, Nidhin Koshy, Yasodharan, Sarath
Publikováno v:
Journal of the Indian Institute of Science, volume 100, pages 809-847, 2020
We highlight the usefulness of city-scale agent-based simulators in studying various non-pharmaceutical interventions to manage an evolving pandemic. We ground our studies in the context of the COVID-19 pandemic and demonstrate the power of the simul
Externí odkaz:
http://arxiv.org/abs/2008.04849
Given a finite set of unknown distributions or arms that can be sampled, we consider the problem of identifying the one with the maximum mean using a $\delta$-correct algorithm (an adaptive, sequential algorithm that restricts the probability of erro
Externí odkaz:
http://arxiv.org/abs/1908.09094
Publikováno v:
2021 Seventh Indian Control Conference (ICC).
Classical regret minimization in a bandit frame-work involves a number of probability distributions or arms that are not known to the learner but that can be sampled from or pulled. The learner's aim is to sequentially pull these arms so as to maximi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::192dd32cc804a770a6c8b8ffc2cf8976
https://doi.org/10.1109/icc54714.2021.9703134
https://doi.org/10.1109/icc54714.2021.9703134
