no code implementations • 10 May 2023 • Kota Srinivas Reddy, P. N. Karthik, Nikhil Karamchandani, Jayakrishnan Nair
The pulled arm and its instantaneous reward are revealed to the learner, whose goal is to find the best arm by minimising the expected stopping time, subject to an upper bound on the error probability.
no code implementations • 19 Aug 2022 • Kota Srinivas Reddy, P. N. Karthik, Vincent Y. F. Tan
The local best arm at a client is the arm with the largest mean among the arms local to the client, whereas the global best arm is the arm with the largest average mean across all the clients.
no code implementations • 29 Mar 2022 • P. N. Karthik, Kota Srinivas Reddy, Vincent Y. F. Tan
For this problem, we derive the first-known problem instance-dependent asymptotic lower bound on the growth rate of the expected time required to find the index of the best arm, where the asymptotics is as the error probability vanishes.
no code implementations • 29 May 2020 • Sahasrajit Sarmasarkar, Kota Srinivas Reddy, Nikhil Karamchandani
We consider the problem of identifying the subset $\mathcal{S}^{\gamma}_{\mathcal{P}}$ of elements in the support of an underlying distribution $\mathcal{P}$ whose probability value is larger than a given threshold $\gamma$, by actively querying an oracle to gain information about a sequence $X_1, X_2, \ldots$ of $i. i. d.$ samples drawn from $\mathcal{P}$.
no code implementations • 4 Sep 2019 • Kota Srinivas Reddy, Nikhil Karamchandani
We study a multi-access variant of the popular coded caching framework, which consists of a central server with a catalog of $N$ files, $K$ caches with limited memory $M$, and $K$ users such that each user has access to $L$ consecutive caches with a cyclic wrap-around and requests one file from the central server's catalog.