88 citations · 176 across the 7 of their papers we have counts for
7 papers · 1 filter
Blockbuster, Part 1: Block-level AI Operator Fusion
Ofer Dekel
Blockbuster is a framework for AI operator fusion in inference programs. The Blockbuster framework is compatible with any multiprocessor architecture that has a tiered memory hiera…
Learning SMaLL Predictors
Vikas K. Garg, Ofer Dekel, Lin Xiao
We present a new machine learning technique for training small resource-constrained predictors. Our algorithm, the Sparse Multiprototype Linear Learner (SMaLL), is inspired by the…
Linear Learning with Sparse Data
Ofer Dekel
Linear predictors are especially useful when the data is high-dimensional and sparse. One of the standard techniques used to train a linear predictor is the Averaged Stochastic Gra…
Online Learning with Feedback Graphs: Beyond Bandits
Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel +1
We study a general class of online learning problems where the feedback is specified by a graph. This class includes online prediction with expert advice and the multi-armed bandit…
Bandit Convex Optimization: sqrt{T} Regret in One Dimension
Sébastien Bubeck, Ofer Dekel, Tomer Koren +1
We analyze the minimax regret of the adversarial bandit convex optimization problem. Focusing on the one-dimensional case, we prove that the minimax regret is $\widetildeΘ(\sqrt{T}…
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret
Raman Arora, Ofer Dekel, Ambuj Tewari
Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm's ability to learn…