An anytime algorithm for learning loss functions that is asymptotically optimal in the worst case and experimentally faster than prior methods for hyperparameter tuning.
A survey of algorithms and analysis for adaptive online learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Learning Effective Loss Functions Efficiently
An anytime algorithm for learning loss functions that is asymptotically optimal in the worst case and experimentally faster than prior methods for hyperparameter tuning.