536 citations · 4.4k across the 97 of their papers we have counts for
6 papers · 1 filter
Communication Efficient Distributed Optimization using an Approximate Newton-type Method
Ohad Shamir, Nathan Srebro, Tong Zhang
We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, t…
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
Ohad Shamir
Many machine learning approaches are characterized by information constraints on how they interact with the training data. These include memory and sequential access constraints (e…
Accurate Profiling of Microbial Communities from Massively Parallel Sequencing using Convex Optimization
Or Zuk, Amnon Amir, Amit Zeisel +2
We describe the Microbial Community Reconstruction ({\bf MCR}) Problem, which is fundamental for microbiome analysis. In this problem, the goal is to reconstruct the identity and f…
An Algorithm for Training Polynomial Networks
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represen…
Online Learning for Time Series Prediction
Oren Anava, Elad Hazan, Shie Mannor +1
In this paper we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret mi…
Online Learning with Switching Costs and Other Adaptive Adversaries
Nicolo Cesa-Bianchi, Ofer Dekel, Ohad Shamir
We study the power of different types of adaptive (nonoblivious) adversaries in the setting of prediction with expert advice, under both full-information and bandit feedback. We me…