170 citations · 506 across the 51 of their papers we have counts for
6 papers · 1 filter
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space
Alec Koppel, Garrett Warnell, Ethan Stump +1
Despite their attractiveness, popular perception is that techniques for nonparametric function approximation do not scale to streaming data due to an intractable growth in the amou…
Proximity Without Consensus in Online Multi-Agent Optimization
Alec Koppel, Brian M. Sadler, Alejandro Ribeiro
We consider stochastic optimization problems in multi-agent settings, where a network of agents aims to learn parameters which are optimal in terms of a global objective, while giv…
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro
We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we prop…
Decentralized Dynamic Discriminative Dictionary Learning
Alec Koppel, Garrett Warnell, Ethan Stump +1
We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn a common set of dictionary elements of a feature space and m…
Doubly Random Parallel Stochastic Methods for Large Scale Learning
Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro
We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we prop…
Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization
Andrea Simonetto, Alec Koppel, Aryan Mokhtari +2
We develop algorithms that find and track the optimal solution trajectory of time-varying convex optimization problems which consist of local and network-related objectives. The al…