May 22, 2024Open Access

Online Classification with Predictions

View Full Paper

Ask AI

Bookmark

Authors

VRVinod RamanBoston University ATAmbuj TewariUniversity of Michigan

Discussion

Member takes

Implication

Theoretical analysis demonstrates reduced expected regret in online learners, indicating that accurate forecasts allow online classification to match transductive online learning.

Key Points

Expected regret in online classification never exceeds the worst-case regret bound, gracefully decreasing as the accuracy of predictions regarding future examples increases.
Theoretical analysis develops an online learner that integrates machine-learned predictions about future examples to surpass traditional worst-case regret guarantees.
Guaranteed predictability of incoming examples allows online classification to match transductive online learning efficiency, complementing smoothed online classification.

Cite This Study

Raman et al. (2024) studied this question.

synapsesocial.com/papers/68e68fa6b6db643587616683 https://doi.org/10.48550/arxiv.2405.14066

View Full Paper

Ask AI

Bookmark

Implication

Theoretical analysis demonstrates reduced expected regret in online learners, indicating that accurate forecasts allow online classification to match transductive online learning.

Online Classification with Predictions

Discussion

Member takes

Implication

Key Points

Cite This Study

Online Classification with Predictions

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider