22 citations · 29 across the 5 of their papers we have counts for
6 papers · 1 filter
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning
Nathan Beck, Adithya Iyer, Rishabh Iyer
As supervised fine-tuning of pre-trained models within NLP applications increases in popularity, larger corpora of annotated data are required, especially with increasing parameter…
Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection
Nathan Beck, Truong Pham, Rishabh Iyer
With increasing volume of data being used across machine learning tasks, the capability to target specific subsets of data becomes more important. To aid in this capability, the re…
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Nathan Beck, Krishnateja Killamsetty, Suraj Kothawade +1
Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to rand…
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings
Nathan Beck, Suraj Kothawade, Pradeep Shenoy +1
Deep neural networks have consistently shown great performance in several real-world use cases like autonomous vehicles, satellite imaging, etc., effectively leveraging large corpo…
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
Nathan Beck, Abhiramon Rajasekharan, Hieu Tran
Transfer learning approaches in reinforcement learning aim to assist agents in learning their target domains by leveraging the knowledge learned from other agents that have been tr…
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty +1
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples. However, existing active learning methods do not work well in reali…