4 accepted papers
In this paper, we investigate the adversarial robustness of nonparametric regression, a fundamental problem in machine learning, under the setting where an adversary can arbitrarily corrupt a subset of the input data. While the robustness of parametric regression has been extensively studied, its no…
Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker node processes a combination of the data, rather than the raw data itself. The…
Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of $m$ samples from a high--dimensional random vector, and they can only communicate a limited number of bits to a central server, w…
We consider a problem of localizing the destination of an activated path signal supported on a graph. An “activated path signal” is a graph signal that evolves over time that can be viewed as the trajectory of a moving agent. We show that by combining dynamic programming and graph partitioning, the…