Bandit-Driven Batch Selection for Robust Learning under Label Noise

• URL: http://arxiv.org/abs/2311.00096v1
• Date: Tue, 31 Oct 2023 19:19:01 GMT
• Title: Bandit-Driven Batch Selection for Robust Learning under Label Noise
• Authors: Michal Lisicki, Mihai Nica, Graham W. Taylor
• Abstract summary: We introduce a novel approach for batch selection in Gradient Descent (SGD) training, leveraging bandit algorithms. Our methodology focuses on optimizing the learning process in the presence of label noise, a prevalent issue in real-world datasets.
• Score: 20.202806541218944
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce a novel approach for batch selection in Stochastic Gradient Descent (SGD) training, leveraging combinatorial bandit algorithms. Our methodology focuses on optimizing the learning process in the presence of label noise, a prevalent issue in real-world datasets. Experimental evaluations on the CIFAR-10 dataset reveal that our approach consistently outperforms existing methods across various levels of label corruption. Importantly, we achieve this superior performance without incurring the computational overhead commonly associated with auxiliary neural network models. This work presents a balanced trade-off between computational efficiency and model efficacy, offering a scalable solution for complex machine learning applications.

Related papers

• Enhancing PAC Learning of Half spaces Through Robust Optimization Techniques [0.0]
  PAC learning half spaces under constant malicious noise, where a fraction of the training data is adversarially corrupted. My study presents a novel, efficient algorithm that extends the existing theoretical frameworks to account for noise resilience in half space learning. We provide a comprehensive analysis of the algorithm's performance, demonstrating its superior robustness to malicious noise when compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-21T23:08:17Z)
• Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution [62.71425232332837]
  We show that training amortized models with noisy labels is inexpensive and surprisingly effective. This approach significantly accelerates several feature attribution and data valuation methods, often yielding an order of magnitude speedup over existing approaches.
  arXiv  Detail & Related papers  (2024-01-29T03:42:37Z)
• Fine tuning Pre trained Models for Robustness Under Noisy Labels [34.68018860186995]
  The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. We introduce a novel algorithm called TURN, which robustly and efficiently transfers the prior knowledge of pre-trained models.
  arXiv  Detail & Related papers  (2023-10-24T20:28:59Z)
• Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning [22.410220040736235]
  We present a theoretically optimal solution for addressing both coreset selection and active learning. Our proposed method, COPS, is designed to minimize the expected loss of a model trained on subsampled data.
  arXiv  Detail & Related papers  (2023-09-05T14:06:33Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Active Learning Guided by Efficient Surrogate Learners [25.52920030051264]
  Re-training a deep learning model each time a single data point receives a new label is impractical. We introduce a new active learning algorithm that harnesses the power of a Gaussian process surrogate in conjunction with the neural network principal learner. Our proposed model adeptly updates the surrogate learner for every new data instance, enabling it to emulate and capitalize on the continuous learning dynamics of the neural network.
  arXiv  Detail & Related papers  (2023-01-07T01:35:25Z)
• Batch Active Learning from the Perspective of Sparse Approximation [12.51958241746014]
  Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart.
  arXiv  Detail & Related papers  (2022-11-01T03:20:28Z)
• UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning [89.56465237941013]
  We propose UNICON, a simple yet effective sample selection method which is robust to high label noise. We obtain an 11.4% improvement over the current state-of-the-art on CIFAR100 dataset with a 90% noise rate.
  arXiv  Detail & Related papers  (2022-03-28T07:36:36Z)
• Learning with Neighbor Consistency for Noisy Labels [69.83857578836769]
  We present a method for learning from noisy labels that leverages similarities between training examples in feature space. We evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, Clothing1M, mini-ImageNet-Red) noise.
  arXiv  Detail & Related papers  (2022-02-04T15:46:27Z)
• Semi-supervised Batch Active Learning via Bilevel Optimization [89.37476066973336]
  We formulate our approach as a data summarization problem via bilevel optimization. We show that our method is highly effective in keyword detection tasks in the regime when only few labeled samples are available.
  arXiv  Detail & Related papers  (2020-10-19T16:53:24Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.