4 papers
Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning
Amogh Inamdar, Zhenwei Tang, Ashton Anderson +1
Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance. Static strat…
Re-Evaluating Continual Learning with Few-Shot Adaptation
Amogh Inamdar, Matthew So, Vici Milenia +1
Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standard measure of stability (i.e.,…
QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
Zhun Deng, Thomas P Zollo, Benjamin Eyre +3
As machine learning models grow increasingly competent, their predictions can supplement scarce or expensive data in various important domains. In support of this paradigm, algorit…
LogicLearner: A Tool for the Guided Practice of Propositional Logic Proofs
Amogh Inamdar, Uzay Macar, Michel Vazirani +6
The study of propositional logic -- fundamental to the theory of computing -- is a cornerstone of the undergraduate computer science curriculum. Learning to solve logical proofs re…