123 citations · 534 across the 48 of their papers we have counts for
6 papers · 1 filter
Temporal Difference Learning as Gradient Splitting
Rui Liu, Alex Olshevsky
Temporal difference learning with linear function approximation is a popular method to obtain a low-dimensional approximation of the value function of a policy in a Markov Decision…
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Qianqian Ma, Alex Olshevsky
We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknow…
Optimal Lockdown for Pandemic Control
Qianqian Ma, Yang-Yu Liu, Alex Olshevsky
As a common strategy of contagious disease containment, lockdowns will inevitably weaken the economy. The ongoing COVID-19 pandemic underscores the trade-off arising from public he…
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion
Rui Liu, Alex Olshevsky
We study alternating minimization for matrix completion in the simplest possible setting: completing a rank-one matrix from a revealed subset of the entries. We bound the asymptoti…
Local SGD With a Communication Overhead Depending Only on the Number of Workers
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis
We consider speeding up stochastic gradient descent (SGD) by parallelizing it across multiple workers. We assume the same data set is shared among workers, who can take SGD ste…
Asymptotic Network Independence and Step-Size for A Distributed Subgradient Method
Alex Olshevsky
We consider whether distributed subgradient methods can achieve a linear speedup over a centralized subgradient method. While it might be hoped that distributed network of node…