34 citations · 237 across the 46 of their papers we have counts for
15 papers · 1 filter
Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments
Rahul Singh
Negative control is a strategy for learning the causal relationship between treatment and outcome in the presence of unmeasured confounding. The treatment effect can nonetheless be…
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…
Filtering for Aggregate Hidden Markov Models with Continuous Observations
Qinsheng Zhang, Rahul Singh, Yongxin Chen
We consider a class of filtering problems for large populations where each individual is modeled by the same hidden Markov model (HMM). In this paper, we focus on aggregate inferen…
Learning Hidden Markov Models from Aggregate Observations
Rahul Singh, Qinsheng Zhang, Yongxin Chen
In this paper, we propose an algorithm for estimating the parameters of a time-homogeneous hidden Markov model from aggregate observations. This problem arises when only the popula…
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…