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Found 6 papers, 0 papers with code
On the Fragility of Active Learners
The impact of this study is in its insights for a practitioner: (a) the choice of text representation and classifier is as important as that of an AL technique, (b) choice of the right metric is critical in assessment of the latter, and, finally, (c) reported AL results must be holistically interpreted, accounting for variables other than just the query strategy.
Are Good Explainers Secretly Human-in-the-Loop Active Learners?
We argue that this is equivalent to Active Learning, where the query strategy involves a human-in-the-loop.
Data Selection: A Surprisingly Effective and General Principle for Building Small Interpretable Models
In the first two tasks, model size is identified by number of leaves in the tree and the number of prototypes respectively.
Rational Kernels: A survey
The framework of rational kernels partly addresses this problem by providing an elegant representation for sequences, for algorithms that use kernel functions.
Learning Interpretable Models Using an Oracle
Our work addresses this by: (a) showing that learning a training distribution (often different from the test distribution) can often increase accuracy of small models, and therefore may be used as a strategy to compensate for small sizes, and (b) providing a model-agnostic algorithm to learn such training distributions.
Interpretability with Accurate Small Models
Our technique identifies the training data distribution to learn from that leads to the highest accuracy for a model of a given size.
