144 citations · 292 across the 30 of their papers we have counts for
11 papers · 1 filter
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.…
Shared Learning : Enhancing Reinforcement in -Ensembles
Rakesh R Menon, Balaraman Ravindran
Deep Reinforcement Learning has been able to achieve amazing successes in a variety of domains from video games to continuous control by trying to maximize the cumulative reward. H…
RAIL: Risk-Averse Imitation Learning
Anirban Santara, Abhishek Naik, Balaraman Ravindran +4
Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a s…
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning
Sahil Sharma, Aravind Suresh, Rahul Ramesh +1
Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains. Among all such visual decision making p…
Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning
Sahil Sharma, Girish Raguvir J, Srivatsan Ramesh +1
Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning…