10 citations · 31 across the 12 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…
Rate of convergence of the smoothed empirical Wasserstein distance
Adam Block, Zeyu Jia, Yury Polyanskiy +1
Consider an empirical measure induced by iid samples from a -dimensional -subgaussian distribution and let be the isotropic…
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
Adam Block, Rahul Kidambi, Daniel N. Hill +2
Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know wh…
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…