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20172026
most citedImplicit Graph Neural Networks

57 citations · 200 across the 66 of their papers we have counts for

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Showing 2022Show all

8 papers · 1 filter

eess.SY2022

Distributed Optimal Control of Graph Symmetric Systems via Graph Filters

Fengjun Yang, Fernando Gama, Somayeh Sojoudi +1

Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and infor…

cs.LG2022★ 1 cited

An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models

Brendon G. Anderson, Tanmay Gautam, Somayeh Sojoudi

In this discussion paper, we survey recent research surrounding robustness of machine learning models. As learning algorithms become increasingly more popular in data-driven contro…

eess.SY2022

LQR Control with Sparse Adversarial Disturbances

Samuel Pfrommer, Somayeh Sojoudi

Recent developments in cyber-physical systems and event-triggered control have led to an increased interest in the impact of sparse disturbances on dynamical processes. We study Li…

math.OC2022

Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing

Baturalp Yalcin, Ziye Ma, Javad Lavaei +1

Many fundamental low-rank optimization problems, such as matrix completion, phase synchronization/retrieval, power system state estimation, and robust PCA, can be formulated as the…

math.OC2022

A New Complexity Metric for Nonconvex Rank-one Generalized Matrix Completion

Haixiang Zhang, Baturalp Yalcin, Javad Lavaei +1

In this work, we develop a new complexity metric for an important class of low-rank matrix optimization problems in both symmetric and asymmetric cases, where the metric aims to qu…

eess.SY2022★ 5 cited

Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning

Jingqi Li, Donggun Lee, Somayeh Sojoudi +1

In this paper, we consider the infinite-horizon reach-avoid zero-sum game problem, where the goal is to find a set in the state space, referred to as the reach-avoid set, such that…