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Found 5 papers, 0 papers with code
An Active Learning Framework for Efficient Robust Policy Search
Robust Policy Search is the problem of learning policies that do not degrade in performance when subject to unseen environment model parameters.
Rate of Change Analysis for Interestingness Measures
We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties.
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates
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
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 whose quality is above a threshold.
Linear Bandit algorithms using the Bootstrap
One of the proposed methods, X-Random bootstrap, performs better than the baselines in-terms of cumulative regret across various degrees of noise and different number of trials.
