Stopping criterion for active learning based on deterministic generalization bounds

• URL: http://arxiv.org/abs/2005.07402v1
• Date: Fri, 15 May 2020 08:15:47 GMT
• Title: Stopping criterion for active learning based on deterministic generalization bounds
• Authors: Hideaki Ishibashi and Hideitsu Hino
• Abstract summary: We propose a criterion for automatically stopping active learning. The proposed stopping criterion is based on the difference in the expected generalization errors and hypothesis testing. We demonstrate the effectiveness of the proposed method via experiments with both artificial and real datasets.
• Score: 4.518012967046983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active learning is a framework in which the learning machine can select the samples to be used for training. This technique is promising, particularly when the cost of data acquisition and labeling is high. In active learning, determining the timing at which learning should be stopped is a critical issue. In this study, we propose a criterion for automatically stopping active learning. The proposed stopping criterion is based on the difference in the expected generalization errors and hypothesis testing. We derive a novel upper bound for the difference in expected generalization errors before and after obtaining a new training datum based on PAC-Bayesian theory. Unlike ordinary PAC-Bayesian bounds, though, the proposed bound is deterministic; hence, there is no uncontrollable trade-off between the confidence and tightness of the inequality. We combine the upper bound with a statistical test to derive a stopping criterion for active learning. We demonstrate the effectiveness of the proposed method via experiments with both artificial and real datasets.

Related papers

• Large Class Separation is not what you need for Relational Reasoning-based OOD Detection [12.578844450586]
  Out-Of-Distribution (OOD) detection methods provide a solution by identifying semantic novelty. Most of these methods leverage a learning stage on the known data, which means training (or fine-tuning) a model to capture the concept of normality. A viable alternative is that of evaluating similarities in the embedding space produced by large pre-trained models without any further learning effort.
  arXiv  Detail & Related papers  (2023-07-12T14:10:15Z)
• Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach [5.371816551086118]
  We develop a learning method that sequentially decides whether to request the "labels" of feature samples from an unlabeled data stream. Our active learning method is the first to be directly informed by the decision error induced by the predicted parameters.
  arXiv  Detail & Related papers  (2023-05-11T05:44:36Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Learnability, Sample Complexity, and Hypothesis Class Complexity for Regression Models [10.66048003460524]
  This work is inspired by the foundation of PAC and is motivated by the existing regression learning issues. The proposed approach, denoted by epsilon-Confidence Approximately Correct (epsilon CoAC), utilizes Kullback Leibler divergence (relative entropy) It enables the learner to compare hypothesis classes of different complexity orders and choose among them the optimum with the minimum epsilon.
  arXiv  Detail & Related papers  (2023-03-28T15:59:12Z)
• Learning versus Refutation in Noninteractive Local Differential Privacy [133.80204506727526]
  We study two basic statistical tasks in non-interactive local differential privacy (LDP): learning and refutation. Our main result is a complete characterization of the sample complexity of PAC learning for non-interactive LDP protocols.
  arXiv  Detail & Related papers  (2022-10-26T03:19:24Z)
• Knowledge-driven Active Learning [70.37119719069499]
  Active learning strategies aim at minimizing the amount of labelled data required to train a Deep Learning model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. Here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples.
  arXiv  Detail & Related papers  (2021-10-15T06:11:53Z)
• Hitting the Target: Stopping Active Learning at the Cost-Based Optimum [1.1756822700775666]
  Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional fully supervised learning. This paper is the first to give actionable advice to practitioners on what stopping criteria they should use in a given real-world scenario. We contribute the first large-scale comparison of stopping criteria, using a cost measure to quantify the accuracy/label trade-off, public implementations of all stopping criteria we evaluate, and an open-source framework for evaluating stopping criteria.
  arXiv  Detail & Related papers  (2021-10-07T21:33:34Z)
• A Low Rank Promoting Prior for Unsupervised Contrastive Learning [108.91406719395417]
  We construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning. Our hypothesis explicitly requires that all the samples belonging to the same instance class lie on the same subspace with small dimension. Empirical evidences show that the proposed algorithm clearly surpasses the state-of-the-art approaches on multiple benchmarks.
  arXiv  Detail & Related papers  (2021-08-05T15:58:25Z)
• Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation [85.75352990739154]
  We propose a novel PU classification method based on density ratio estimation. A notable advantage of our proposed method is that it does not require the class-priors in the training phase.
  arXiv  Detail & Related papers  (2021-07-11T13:36:53Z)
• Stopping Criterion for Active Learning Based on Error Stability [3.2996723916635267]
  We propose a stopping criterion based on error stability, which guarantees that the change in generalization error upon adding a new sample is bounded by the annotation cost. We demonstrate that the proposed criterion stops active learning at the appropriate timing for various learning models and real datasets.
  arXiv  Detail & Related papers  (2021-04-05T10:15:50Z)
• Progressive Identification of True Labels for Partial-Label Learning [112.94467491335611]
  Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. This paper proposes a novel framework of classifier with flexibility on the model and optimization algorithm.
  arXiv  Detail & Related papers  (2020-02-19T08:35:15Z)

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.