%0 Journal Article %1 hanneke2018lower %A Hanneke, Steve %A Kontorovich, Aryeh %D 2018 %K compression learning theory %T A New Lower Bound for Agnostic Learning with Sample Compression Schemes %U http://arxiv.org/abs/1805.08140 %X 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 sample compression schemes. In particular, we find that the optimal rates of convergence for size-$k$ agnostic sample compression schemes are of the form $\frack łog(n/k)n$, which contrasts with agnostic learning with classes of VC dimension $k$, where the optimal rates are of the form $\frackn$.

@article{hanneke2018lower, abstract = {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 sample compression schemes. In particular, we find that the optimal rates of convergence for size-$k$ agnostic sample compression schemes are of the form $\sqrt{\frac{k \log(n/k)}{n}}$, which contrasts with agnostic learning with classes of VC dimension $k$, where the optimal rates are of the form $\sqrt{\frac{k}{n}}$.}, added-at = {2019-06-17T01:20:32.000+0200}, author = {Hanneke, Steve and Kontorovich, Aryeh}, biburl = {https://www.bibsonomy.org/bibtex/26289a0bc47465a36a791b0ea96d12d3a/kirk86}, description = {[1805.08140] A New Lower Bound for Agnostic Learning with Sample Compression Schemes}, interhash = {b7f579bdb33f5a6b2230de4ebd3aae60}, intrahash = {6289a0bc47465a36a791b0ea96d12d3a}, keywords = {compression learning theory}, note = {cite arxiv:1805.08140}, timestamp = {2019-06-17T01:20:32.000+0200}, title = {A New Lower Bound for Agnostic Learning with Sample Compression Schemes}, url = {http://arxiv.org/abs/1805.08140}, year = 2018 }