Jun 14, 2023 · This paper proposes a novel uncertainty-aware graph learning framework motivated by distributionally robust optimization.
noisy graphs, especially for the problem of semi-supervised node classification. 43. In this paper, we propose a novel uncertainty-aware graph learning ...
✔️ We propose a simple yet effective robust learning method leveraging a mixture of experts model on various noise settings. ✔️ The proposed method can not only ...
Such an uncertainty-aware learning process leads to improved node representations and a more robust graph predictive model that effectively mitigates the impact ...
Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a ...
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Oct 13, 2022 · This work proposes UA-HRL, an uncertainty-aware hierarchical reinforcement learning framework for mitigating the problems caused by noisy sensor data.
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Jun 3, 2023 · This paper bridges the gap by proposing a pairwise framework for noisy node classification on graphs, which relies on the PI as a primary ...
Elucidating Robust Learning with Uncertainty-Aware Corruption ...
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Our proposed method can not only successfully learn the clean target distribution from a dirty dataset but also can estimate the underlying noise pattern. To ...
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Oct 9, 2024 · The model features are more robust to label noise. •. Reweighting based on uncertainty can alleviate the cumulative bias of the model. Abstract.
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Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed ...
Missing: Graphs. | Show results with:Graphs.