5 citations · 9 across the 8 of their papers we have counts for
8 papers · 1 filter
Realizable Bayes-Consistency for General Metric Losses
Dan Tsir Cohen, Steve Hanneke, Aryeh Kontorovich
We study strong universal Bayes-consistency in the realizable setting for learning with general metric losses, extending classical characterizations beyond - classification (…
Efficient Agnostic Learning with Average Smoothness
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
We study distribution-free nonparametric regression following a notion of average smoothness initiated by Ashlagi et al. (2021), which measures the "effective" smoothness of a func…
Near-optimal learning with average Hölder smoothness
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
We generalize the notion of average Lipschitz smoothness proposed by Ashlagi et al. (COLT 2021) by extending it to Hölder smoothness. This measure of the "effective smoothness" of…
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…
Universal Bayes consistency in metric spaces
Steve Hanneke, Aryeh Kontorovich, Sivan Sabato +1
We extend a recently proposed 1-nearest-neighbor based multiclass learning algorithm and prove that our modification is universally strongly Bayes-consistent in all metric spaces a…
Agnostic Sample Compression Schemes for Regression
Idan Attias, Steve Hanneke, Aryeh Kontorovich +1
We obtain the first positive results for bounded sample compression in the agnostic regression setting with the loss, where . We construct a generic appro…
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,…
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…