196 citations · 319 across the 19 of their papers we have counts for
4 papers · 1 filter
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani, Abbas Mehrabian, Audrey Durand +1
We propose , a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampl…
Multiplayer bandits without observing collision information
Gabor Lugosi, Abbas Mehrabian
We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involve…
Sample-Efficient Learning of Mixtures
Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian
We consider PAC learning of probability distributions (a.k.a. density estimation), where we are given an i.i.d. sample generated from an unknown target distribution, and want to ou…
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
Peter L. Bartlett, Nick Harvey, Chris Liaw +1
We prove new upper and lower bounds on the VC-dimension of deep neural networks with the ReLU activation function. These bounds are tight for almost the entire range of parameters.…