Predicting with Proxies: Transfer Learning in High Dimension

Learning Boxes in High Dimension

Learning Boxes in High Dimension 1

We present exact learning algorithms that learn several classes of (discrete) boxes in f0; : : : ; ` 1gn. In particular we learn: (1) The class of unions of O(log n) boxes in time poly(n; log `) (solving an open problem of [16, 12]; in [3] this class is shown to be learnable in time poly(n; `)). (2) The class of unions of disjoint boxes in time poly(n; t; log `), where t is the number of boxes....

Towards Predicting Future Transfer of Learning

We present an automated detector that can predict a student’s future performance on a transfer post-test, a post-test involving related but different skills than the skills studied in the tutoring system, within an Intelligent Tutoring System for College Genetics. We show that this detector predicts transfer better than Bayesian Knowledge Tracing, a measure of student learning in intelligent tu...

cognitive failure and alexithymia in predicting high–risk behaviors of students with learning disabilities

conclusions: these findings demonstrated that cognitive failure and alexithymia had important roles in strengthening and appearance of high-risk behaviors in students with learning disabilities. therefore, considering those problems, precautionary actions might be necessary. results: the results showed that high-risk behaviors had significant positive correlations with difficulty identifying fe...

High Dimension Action Spaces in Robot Skill Learning

Table lookup with interpolation is used for many learning and adaptation tasks. Redundant mappings capture the important concept of “motor skill,” which is important in real, behaving systems. Few robot skill implementations have dealt with redundant mappings, in which the space to be searched to create the table has much higher dimensionality than the table. A practical method for inverting re...