4 citations · 13 across the 8 of their papers we have counts for
8 papers
Structured Tensor Approximation from Lateral Slice Sampling via Basis and Manifold Priors
Jeongmin Chae, Usama Saleem, Selin Bac +2
In this work, we consider a structured tensor approximation problem, where only a limited number of lateral slices are observed. The proposed algorithm , called Basis and Manifold…
Matrix Approximation with Side Information: When Column Sampling is Enough
Jeongmin Chae, Praneeth Narayanamurthy, Selin Bac +2
A novel matrix approximation problem is considered herein: observations based on a few fully sampled columns and quasi-polynomial structural side information are exploited. The fra…
Distributed Online Learning with Multiple Kernels
Jeongmin Chae, Songnam Hong
We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion. Online learning is additionally assumed, where every learner r…
Multiple Kernel-Based Online Federated Learning
Jeongmin Chae, Songnam Hong
Online federated learning (OFL) becomes an emerging learning framework, in which edge nodes perform online learning with continuous streaming local data and a server constructs a g…
Distributed Online Learning with Multiple Kernels
Jeongmin Chae, Songnam Hong
In the Internet-of-Things (IoT) systems, there are plenty of informative data provided by a massive number of IoT devices (e.g., sensors). Learning a function from such data is of…
Pool-based sequential active learning with multi kernels
Jeongmin Chae, Songnam Hong
We study a pool-based sequential active learning (AL), in which one sample is queried at each time from a large pool of unlabeled data according to a selection criterion. For this…
Active Learning with Multiple Kernels
Songnam Hong, Jeongmin Chae
Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature approximation, the major drawback of…
Greedy Sparse Signal Recovery Algorithm Based on Bit-wise MAP detection
J. Chae, S. -N. Hong
We propose a novel greedy algorithm for the support recovery of a sparse signal from a small number of noisy measurements. In the proposed method, a new support index is identified…