Shubhada Agrawal

According to our database, Shubhada Agrawal authored at least 9 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Optimal Top-Two Method for Best Arm Identification and Fluid Analysis.

Agniv Bandyopadhyay, Sandeep Juneja, Shubhada Agrawal

CoRR, 2024

2023

Shift, scale and restart smaller models to estimate larger ones: Agent based simulators in epidemiology.

Daksh Mittal, Sandeep Juneja, Shubhada Agrawal

SIGMETRICS Perform. Evaluation Rev., April, 2023

CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption.

Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard

CoRR, 2023

Optimal Best-Arm Identification in Bandits with Access to Offline Data.

Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam, Arun Sai Suggala

CoRR, 2023

2021

Optimal Best-Arm Identification Methods for Tail-Risk Measures.

Shubhada Agrawal, Wouter M. Koolen, Sandeep Juneja

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Regret Minimization in Heavy-Tailed Bandits.

Shubhada Agrawal, Sandeep Juneja, Wouter M. Koolen

Proceedings of the Conference on Learning Theory, 2021

2020

City-Scale Agent-Based Simulators for the Study of Non-Pharmaceutical Interventions in the Context of the COVID-19 Epidemic.

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

CoRR, 2020

Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions.

Shubhada Agrawal, Sandeep Juneja, Peter W. Glynn

Proceedings of the Algorithmic Learning Theory, 2020

2019

Optimal best arm selection for general distributions.

Shubhada Agrawal, Sandeep Juneja, Peter W. Glynn

CoRR, 2019