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Found 7 papers, 2 papers with code
Ranking-Constrained Learning with Rationales for Text Classification
We propose a novel approach that jointly utilizes the labels and elicited rationales for text classification to speed up the training of deep learning models with limited training data.
IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients
Integrated Gradients (IG) as well as its variants are well-known techniques for interpreting the decisions of deep neural networks.
Leaders or Followers? A Temporal Analysis of Tweets from IRA Trolls
The Internet Research Agency (IRA) influences online political conversations in the United States, exacerbating existing partisan divides and sowing discord.
Hindsight Analysis of the Chicago Food Inspection Forecasting Model
The Chicago Department of Public Health (CDPH) conducts routine food inspections of over 15, 000 food establishments to ensure the health and safety of their patrons.
Anytime Active Learning
Our simulated experiments show that anytime active learning outperforms several baselines on these two datasets.
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Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition
We address the cost-sensitive feature acquisition problem, where misclassifying an instance is costly but the expected misclassification cost can be reduced by acquiring the values of the missing features.
