no code implementations • 14 Mar 2024 • Agniv Bandyopadhyay, Sandeep Juneja, Shubhada Agrawal
We show that the proposed algorithm is optimal as $\delta \rightarrow 0$.
no code implementations • 28 Sep 2023 • Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard
In this setting, accommodating potentially unbounded corruptions, we establish a problem-dependent lower bound on regret for a given family of arm distributions.
no code implementations • 15 Jun 2023 • Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam, Arun Sai Suggala
Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature.
no code implementations • 7 Sep 2022 • Daksh Mittal, Sandeep Juneja, Shubhada Agrawal
They provide the flexibility to accurately model a heterogeneous population with time and location varying, person-specific interactions as well as detailed governmental mobility restrictions.
no code implementations • 7 Feb 2021 • Shubhada Agrawal, Sandeep Juneja, Wouter M. Koolen
We show that our index concentrates faster than the well known truncated or trimmed empirical mean estimators for the mean of heavy-tailed distributions.
no code implementations • NeurIPS 2021 • Shubhada Agrawal, Wouter M. Koolen, Sandeep Juneja
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 heavy-tailed.
1 code implementation • 11 Aug 2020 • Shubhada Agrawal, Siddharth Bhandari, Anirban Bhattacharjee, Anand Deo, Narendra M. Dixit, Prahladh Harsha, Sandeep Juneja, Poonam Kesarwani, Aditya Krishna Swamy, Preetam Patil, Nihesh Rathod, Ramprasad Saptharishi, Sharad Shriram, Piyush Srivastava, Rajesh Sundaresan, Nidhin Koshy Vaidhiyan, Sarath Yasodharan
We highlight the usefulness of city-scale agent-based simulators in studying various non-pharmaceutical interventions to manage an evolving pandemic.
Populations and Evolution Other Computer Science Physics and Society Quantitative Methods
no code implementations • 24 Aug 2019 • Shubhada Agrawal, Sandeep Juneja, Peter Glynn
We then propose a $\delta$-correct algorithm that matches the lower bound as $\delta$ reduces to zero under the mild restriction that a known bound on the expectation of $(1+\epsilon)^{th}$ moment of the underlying random variables exists, for $\epsilon > 0$.