2 citations · 2 across the 7 of their papers we have counts for
7 papers
Hierarchical Causal Bandit
Ruiyang Song, Stefano Rini, Kuang Xu
Causal bandit is a nascent learning model where an agent sequentially experiments in a causal network of variables, in order to identify the reward-maximizing intervention. Despite…
Temporal Concatenation for Markov Decision Processes
Ruiyang Song, Kuang Xu
We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems al…
Compress-and-Estimate Source Coding for a Vector Gaussian Source
Ruiyang Song, Stefano Rini, Alon Kipnis +1
We consider the remote vector source coding problem in which a vector Gaussian source is to be estimated from noisy linear measurements. For this problem, we derive the performance…
Optimal Rate Allocation in Mismatched Multiterminal Source Coding
Ruiyang Song, Stefano Rini, Alon Kipnis +1
We consider a multiterminal source coding problem in which a source is estimated at a central processing unit from lossy-compressed remote observations. Each lossy-encoded observat…
Online Supervised Subspace Tracking
Yao Xie, Ruiyang Song, Hanjun Dai +2
We present a framework for supervised subspace tracking, when there are two time series and , one being the high-dimensional predictors and the other being the response…
Sequential Information Guided Sensing
Ruiyang Song, Yao Xie, Sebastian Pokutta
We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when informatio…
Sequential Sensing with Model Mismatch
Ruiyang Song, Yao Xie, Sebastian Pokutta
We characterize the performance of sequential information guided sensing, Info-Greedy Sensing, when there is a mismatch between the true signal model and the assumed model, which m…