5 citations · 19 across the 7 of their papers we have counts for
14 papers
Distributionally Robust Semi-Supervised Learning Over Graphs
Alireza Sadeghi, Meng Ma, Bingcong Li +1
Semi-supervised learning (SSL) over graph-structured data emerges in many network science applications. To efficiently manage learning over graphs, variants of graph neural network…
Heavy Ball Momentum for Conditional Gradient
Bingcong Li, Alireza Sadeghi, Georgios B. Giannakis
Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented merits in machine learning and signal processing applications. Unlike projection-based methods, momentum…
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex
Yelin He, Xianbiao Qi, Jiaquan Ye +5
This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX. This competition has two sub-tasks: Table Structure Reconstruction (…
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem
Bingcong Li, Lingda Wang, Georgios B. Giannakis +1
Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of Ex…
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations
Lingda Wang, Bingcong Li, Huozhi Zhou +3
This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: \emp…
How Does Momentum Help Frank Wolfe?
Bingcong Li, Mario Coutino, Georgios B. Giannakis +1
We unveil the connections between Frank Wolfe (FW) type algorithms and the momentum in Accelerated Gradient Methods (AGM). On the negative side, these connections illustrate why mo…