16 citations · 30 across the 4 of their papers we have counts for
5 papers
An Active Learning Framework for Efficient Robust Policy Search
Sai Kiran Narayanaswami, Nandan Sudarsanam, Balaraman Ravindran
Robust Policy Search is the problem of learning policies that do not degrade in performance when subject to unseen environment model parameters. It is particularly relevant for tra…
Rate of Change Analysis for Interestingness Measures
Nandan Sudarsanam, Nishanth Kumar, Abhishek Sharma +1
The use of Association Rule Mining techniques in diverse contexts and domains has resulted in the creation of numerous interestingness measures. This, in turn, has motivated resear…
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates
Subhojyoti Mukherjee, K. P. Naveen, Nandan Sudarsanam +1
We propose a novel variant of the UCB algorithm (referred to as Efficient-UCB-Variance (EUCBV)) for minimizing cumulative regret in the stochastic multi-armed bandit (MAB) setting.…
Thresholding Bandits with Augmented UCB
Subhojyoti Mukherjee, K. P. Naveen, Nandan Sudarsanam +1
In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms…
Linear Bandit algorithms using the Bootstrap
Nandan Sudarsanam, Balaraman Ravindran
This study presents two new algorithms for solving linear stochastic bandit problems. The proposed methods use an approach from non-parametric statistics called bootstrapping to cr…