34 citations · 99 across the 14 of their papers we have counts for
10 papers · 1 filter
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
Guojun Xiong, Gang Yan, Rahul Singh +1
With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the…
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
Agus Sudjianto, William Knauth, Rahul Singh +2
The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box" models without a…
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning
Akshay Mete, Rahul Singh, Xi Liu +1
The Reward-Biased Maximum Likelihood Estimate (RBMLE) for adaptive control of Markov chains was proposed to overcome the central obstacle of what is variously called the fundamenta…
Multi-Armed Bandits with Dependent Arms
Rahul Singh, Fang Liu, Yin Sun +1
We study a variant of the classical multi-armed bandit problem (MABP) which we call as Multi-Armed Bandits with dependent arms. More specifically, multiple arms are grouped togethe…
Contextual Bandits with Side-Observations
Rahul Singh, Fang Liu, Xin Liu +1
We investigate contextual bandits in the presence of side-observations across arms in order to design recommendation algorithms for users connected via social networks. Users in so…
Improving Robustness via Risk Averse Distributional Reinforcement Learning
Rahul Singh, Qinsheng Zhang, Yongxin Chen
One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances…