no code implementations • 17 Aug 2021 • Olivier Bousquet, Mark Braverman, Klim Efremenko, Gillat Kol, Shay Moran
We derive an optimal $2$-approximation learning strategy for the Hypothesis Selection problem, outputting $q$ such that $\mathsf{TV}(p, q) \leq2 \cdot opt + \eps$, with a (nearly) optimal sample complexity of~$\tilde O(\log n/\epsilon^2)$.
no code implementations • 19 Oct 2020 • Ariel Avital, Klim Efremenko, Aryeh Kontorovich, David Toplin, Bo Waggoner
We propose a non-parametric variant of binary regression, where the hypothesis is regularized to be a Lipschitz function taking a metric space to [0, 1] and the loss is logarithmic.
no code implementations • 7 Oct 2019 • Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt
Research on nearest-neighbor methods tends to focus somewhat dichotomously either on the statistical or the computational aspects -- either on, say, Bayes consistency and rates of convergence or on techniques for speeding up the proximity search.