128 citations · 148 across the 7 of their papers we have counts for
7 papers
Signed Graph Neural Networks: A Frequency Perspective
Rahul Singh, Yongxin Chen
Graph convolutional networks (GCNs) and its variants are designed for unsigned graphs containing only positive links. Many existing GCNs have been derived from the spectral domain…
Inference of collective Gaussian hidden Markov models
Rahul Singh, Yongxin Chen
We consider inference problems for a class of continuous state collective hidden Markov models, where the data is recorded in aggregate (collective) form generated by a large popul…
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…
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…
Sample-based Distributional Policy Gradient
Rahul Singh, Keuntaek Lee, Yongxin Chen
Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the key idea of…
Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications
Songtao Lu, Ioannis Tsaknakis, Mingyi Hong +1
The min-max problem, also known as the saddle point problem, is a class of optimization problems which minimizes and maximizes two subsets of variables simultaneously. This class o…