Yew-Soon Ong
7 papers in the PaperMetrix corpus
Papers by this author
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When Gaussian Process Meets Big Data: A Review of Scalable GPs
2020 · IEEE Transactions on Neural Networks and Learning Systems
The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process regression …
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Adaptive Knowledge Transfer based on Transfer Neural Kernel Network
2020
Transfer agents are widely used in the challenging problems where knowledge is cross-used among different tasks. One popular research approach is to design a transfer kernel that controls the strength of knowledge transfer based on …
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Unsupervised Object-Level Representation Learning from Scene Images
2021 · arXiv (Cornell University)
Contrastive self-supervised learning has largely narrowed the gap to supervised pre-training on ImageNet. However, its success highly relies on the object-centric priors of ImageNet, i.e., different augmented views of the same image correspond to the …
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Learning Conjoint Attentions for Graph Neural Nets
2021 · arXiv (Cornell University)
In this paper, we present Conjoint Attentions (CAs), a class of novel learning-to-attend strategies for graph neural networks (GNNs). Besides considering the layer-wise node features propagated within the GNN, CAs can additionally incorporate various structural …
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Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
2024 · arXiv (Cornell University)
There is growing research interest in measuring the statistical heterogeneity of clients' local datasets. Such measurements are used to estimate the suitability for collaborative training of personalized federated learning (PFL) models. Currently, these research endeavors …
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Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation
2025 · arXiv (Cornell University)
Guided diffusion-model generation is a promising direction for customizing the generation process of a pre-trained diffusion model to address specific downstream tasks. Existing guided diffusion models either rely on training the guidance model with pre-collected …
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Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs
2025 · arXiv (Cornell University)
The rise of reinforcement learning (RL) in critical real-world applications demands a fundamental rethinking of privacy in AI systems. Traditional privacy frameworks, designed to protect isolated data points, fall short for sequential decision-making systems where …