Black-box optimization and quantum annealing for filtering out mislabeled training instances

• URL: http://arxiv.org/abs/2501.06916v1
• Date: Sun, 12 Jan 2025 19:59:33 GMT
• Title: Black-box optimization and quantum annealing for filtering out mislabeled training instances
• Authors: Makoto Otsuka, Kento Kodama, Keisuke Morita, Masayuki Ohzeki,
• Abstract summary: Mislabeled training instances, a common issue in real-world datasets, often degrade model generalization. The proposed method evaluates filtered training subsets based on validation loss, iteratively refines loss estimates through surrogate model-based BBO with postprocessing. Experiments on a noisy majority bit task demonstrate the method's ability to prioritize the removal of high-risk mislabeled instances.
• Score: 0.4499833362998489
• License:
• Abstract: This study proposes an approach for removing mislabeled instances from contaminated training datasets by combining surrogate model-based black-box optimization (BBO) with postprocessing and quantum annealing. Mislabeled training instances, a common issue in real-world datasets, often degrade model generalization, necessitating robust and efficient noise-removal strategies. The proposed method evaluates filtered training subsets based on validation loss, iteratively refines loss estimates through surrogate model-based BBO with postprocessing, and leverages quantum annealing to efficiently sample diverse training subsets with low validation error. Experiments on a noisy majority bit task demonstrate the method's ability to prioritize the removal of high-risk mislabeled instances. Integrating D-Wave's clique sampler running on a physical quantum annealer achieves faster optimization and higher-quality training subsets compared to OpenJij's simulated quantum annealing sampler or Neal's simulated annealing sampler, offering a scalable framework for enhancing dataset quality. This work highlights the effectiveness of the proposed method for supervised learning tasks, with future directions including its application to unsupervised learning, real-world datasets, and large-scale implementations.

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