Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Shen, Yanning"'
Autor:
Liu, Yezi, Shen, Yanning
Training graph neural networks (GNNs) on large-scale graphs can be challenging due to the high computational expense caused by the massive number of nodes and high-dimensional nodal features. Existing graph condensation studies tackle this problem on
Externí odkaz:
http://arxiv.org/abs/2407.08064
Autor:
Kose, O. Deniz, Shen, Yanning
Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structu
Externí odkaz:
http://arxiv.org/abs/2402.04383
Autor:
Ghari, Pouya M., Shen, Yanning
Online model selection involves selecting a model from a set of candidate models 'on the fly' to perform prediction on a stream of data. The choice of candidate models henceforth has a crucial impact on the performance. Although employing a larger se
Externí odkaz:
http://arxiv.org/abs/2401.10478
Machine learning (ML) has demonstrated remarkable capabilities across many real-world systems, from predictive modeling to intelligent automation. However, the widespread integration of machine learning also makes it necessary to ensure machine learn
Externí odkaz:
http://arxiv.org/abs/2401.02552
Autor:
Ghari, Pouya M., Shen, Yanning
Publikováno v:
in Advances in Neural Information Processing Systems, volume 35, pages 33316--33329, 2022
Multi-kernel learning (MKL) exhibits well-documented performance in online non-linear function approximation. Federated learning enables a group of learners (called clients) to train an MKL model on the data distributed among clients to perform onlin
Externí odkaz:
http://arxiv.org/abs/2311.05108
Graphs are mathematical tools that can be used to represent complex real-world interconnected systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it h
Externí odkaz:
http://arxiv.org/abs/2310.14432
We introduce the problem of model-extraction attacks in cyber-physical systems in which an attacker attempts to estimate (or extract) the feedback controller of the system. Extracting (or estimating) the controller provides an unmatched edge to attac
Externí odkaz:
http://arxiv.org/abs/2304.13090
Autor:
Kose, O. Deniz, Shen, Yanning
Graphs can facilitate modeling various complex systems such as gene networks and power grids, as well as analyzing the underlying relations within them. Learning over graphs has recently attracted increasing attention, particularly graph neural netwo
Externí odkaz:
http://arxiv.org/abs/2303.14591
Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it has been demonst
Externí odkaz:
http://arxiv.org/abs/2303.11459
We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynamical systems. We show that our algorithm can achieve a sub-linear regr
Externí odkaz:
http://arxiv.org/abs/2210.11684