Submodularity in Data Subset Selection and Active Learning

By Kai Wei, Rishabh K. Iyer, J. Bilmes. 2015

494 citations recorded by the source corpus.

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Citations in the corpus

Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “…learning strategies (Cohn, Atlas, & Ladner, 1994; Lin & Parikh, 2017; Wang, Zhang, Li, Zhang, & Lin, 2016; Settles, 2009; Yoo & So Kweon, 2019; Wei, Iyer, & Bilmes, 2015), with the most common selection methods using uncertainty sampling (Lewis & Gale, 1994; Culotta & McCallum, 2005;…”
Coresets via Bilevel Optimization for Continual Learning and Streaming (2020, neutral): “Several approaches relax the uniform approximation guarantees: the Hilbert coreset [7] which poses the subset selection problem as a vector approximation in a normed vector space, and data selection techniques that were successfully employed in active learning with convolutional neural networks [56,”
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly (2017, neutral): “Additionally, submodular set functions have been used to prove guarantees for online and active learning problems [Hoi et al., 2006; Wei et al., 2015; Buchbinder et al., 2015].”
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).”
Imbalance Problems in Object Detection: A Review (2019, methodology): “Another related set of methods addresses image classification problem from the data redundancy perspective [149], [150], [151], [152], [153].”
Deep Active Learning for Named Entity Recognition (2017, methodology): “Following Wei et al. (2015), we express the problem of maximizing representativeness of a labeled set as a submodular optimization problem, and provide an efficient streaming algorithm adapted to use a constraint suitable to the NER task.”
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning (2020, methodology): “However, with the emergence of batch active learning (Wei, Iyer, and Bilmes 2015; Sener and Savarese 2018), simple US and QBC approaches do not capture diversity in the batch.”
Coresets for Data-efficient Training of Machine Learning Models (2019, background): “…et al., 2017) for a particular problem, such as k -means and k -median clustering (Har-Peled & Mazumdar, 2004), naïve Bayes and nearest-neighbors (Wei et al., 2015), mixture models (Lucic et al., 2017), low rank approximation (Cohen et al., 2017), spectral approximation (Agarwal et al., 2004; Li…”
Active Learning for Convolutional Neural Networks: A Core-Set Approach (2017, background): “Bayesian active learning methods typically use a non-parametric model like Gaussian process to estimate the expected improvement by each query (Kapoor et al., 2007) or the expected error after a set of queries (Roy & McCallum, 2001).”
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning (2021, methodology): “Several works [57, 39, 24, 23] have studied coresets for efficient training of deep learning models in the supervised learning scenarios.”
Data Distillation for Controlling Specificity in Dialogue Generation (2017, background): “The goal of controlling output specificity is closely related to recent attempts to address this issue.”
Deep Learning on a Data Diet: Finding Important Examples Early in Training (2021, background): “Here, the goal is to select a small set of examples to label at any given iteration of training (see, e.g., [26–30], and references therein).”
DataComp: In search of the next generation of multimodal datasets (2023, background): “[136] Kai Wei, Rishabh Iyer, and Jeff Bilmes.”
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).”
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten" (2017, background): “, 2012), recommender systems (El-Arini & Guestrin, 2011), search result diversification (Rakesh Agrawal, 2009), data subset selection (Wei et al., 2015), and social networks analysis (Kempe et al.”
Very Fast Streaming Submodular Function Maximization (2020, background): “This makes them ideal for solving data summarization (Mirzasoleiman et al., 2016), active learning (Wei et al., 2015), recommendation (Ashkan et al., 2015), and many other related tasks.”