Discriminative Batch Mode Active Learning

By Yuhong Guo, Dale Schuurmans. 2007

320 citations recorded by the source corpus.

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Citations in the corpus

Fine-Tuning Language Models from Human Preferences (2019, methodology): “Examples of such techniques include selecting batches based on entropy considerations (Guo and Schuurmans, 2008), gradient-based metrics (Huang et al.”
Active Learning Literature Survey (2009, background): “Guo and Schuurmans (2008) demonstrate that general active learning strategies, when employed in a batch-mode setting (Section 5.2) are also often much worse than random i.i.d. sampling.”
Are All Training Examples Created Equal? An Empirical Study (2018, methodology): “This idea has been successfully utilized in the active learning setting [5, 24].”
Group-Based Active Learning of Classification Models (2017, background): “The second direction is batch-mode active learning, BMAL (Hoi et al. 2006; Chakraborty, Balasubramanian, and Panchanathan 2015; Guo and Schuurmans 2008).”
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning (2019, methodology): “Maintaining diversity when acquiring a batch of data has also been attempted using constrained optimisation [15] and in Gaussian Mixture Models [3].”
Discriminative Active Learning (2019, methodology): “Several methods were developed with this problem in mind (Chen & Krause, 2013), (Guo & Schuurmans, 2008), (Azimi et al., 2012), (Wei et al., 2015).”
Active Learning for Convolutional Neural Networks: A Core-Set Approach (2017, background): “…also many pool based active learning algorithms designed for the specific class of machine learning algorithms like k-nearest neighbors and naive Bayes (Wei et al., 2015), logistic regression Hoi et al. (2006); Guo & Schuurmans (2008), and linear regression with Gaussian noise (Yu et al., 2006).”
Active and Semisupervised Learning for the Classification of Remote Sensing Images (2014, unknown): “In [45], batch AL is formulated as an optimization problem that maximizes the discriminative classiﬁcation performance while taking into consideration the unlabeled examples.”
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds (2019, background): “There is also a large body of literature on batch mode active learning, where the learner is asked to select a batch of samples within each round (Guo and Schuurmans, 2008; Wang and Ye, 2015; Chen and Krause; Wei et al., 2015; Kirsch et al., 2019).”
From Theories to Queries (2011, methodology): “A third approach (Guo and Schuurmans, 2008) attempts to construct a batch by treating the pool of candidate instances U as a bit vector (with 1’s corresponding to the elements included in Q), and use gradient methods to approximate the best query-set vector by numerically optimizing a discriminative”