9 citations · 17 across the 5 of their papers we have counts for
4 papers · 1 filter
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions
Adam Block, Max Simchowitz
Due to the drastic gap in complexity between sequential and batch statistical learning, recent work has studied a smoothed sequential learning setting, where Nature is constrained…
Smoothed Online Learning is as Easy as Statistical Learning
Adam Block, Yuval Dagan, Noah Golowich +1
Much of modern learning theory has been split between two regimes: the classical offline setting, where data arrive independently, and the online setting, where data arrive adversa…
Majorizing Measures, Sequential Complexities, and Online Learning
Adam Block, Yuval Dagan, Sasha Rakhlin
We introduce the technique of generic chaining and majorizing measures for controlling sequential Rademacher complexity. We relate majorizing measures to the notion of fractional c…
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis
Adam Block, Youssef Mroueh, Alexander Rakhlin +1
Recently, the task of image generation has attracted much attention. In particular, the recent empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin Dyna…