5 citations · 5 across the 1 of their papers we have counts for
3 papers
cs.LG2020★ 5 cited
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
Steve Hanneke, Aryeh Kontorovich
We analyze a family of supervised learning algorithms based on sample compression schemes that are stable, in the sense that removing points from the training set which were not se…
cs.LG2018
Sample Compression for Real-Valued Learners
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
We give an algorithmically efficient version of the learner-to-compression scheme conversion in Moran and Yehudayoff (2016). In extending this technique to real-valued hypotheses,…
cs.LG2018
A New Lower Bound for Agnostic Learning with Sample Compression Schemes
Steve Hanneke, Aryeh Kontorovich
We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic samp…