Search Results for author: Yunchao Zhang

Found 5 papers, 2 papers with code

When to Pre-Train Graph Neural Networks? From Data Generation Perspective!

1 code implementation29 Mar 2023 Yuxuan Cao, Jiarong Xu, Carl Yang, Jiaan Wang, Yunchao Zhang, Chunping Wang, Lei Chen, Yang Yang

All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training.

Navigation as Attackers Wish? Towards Building Byzantine-Robust Embodied Agents under Federated Learning

no code implementations27 Nov 2022 Yunchao Zhang, Zonglin Di, Kaiwen Zhou, Cihang Xie, Xin Eric Wang

However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the local client to build a backdoor in the agent without notice.

Federated Learning Navigate +1

Universal Prompt Tuning for Graph Neural Networks

no code implementations30 Sep 2022 Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, Lei Chen

In this paper, we introduce a universal prompt-based tuning method called Graph Prompt Feature (GPF) for pre-trained GNN models under any pre-training strategy.

SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization

no code implementations ICLR 2019 Haoyi Xiong, Wenqing Hu, Zhanxing Zhu, Xinjian Li, Yunchao Zhang, Jun Huan

Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known.

BIG-bench Machine Learning Text-to-Image Generation

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