Alec Koppel

A Saddle Point Algorithm for Networked Online Convex Optimization

Alec Koppel, Felicia Y. Jakubiec, Alejandro R. Ribeiro
IEEE Transactions on Signal Processing
2014

An algorithm to learn optimal actions in convex distributed online problems is developed. Learning is online because cost functions are revealed sequentially and distributed because they are revealed… (More)

Proximity Without Consensus in Online Multiagent Optimization

Alec Koppel, Brian M. Sadler, Alejandro R. Ribeiro
IEEE Transactions on Signal Processing
2016

We consider stochastic optimization problems in multiagent settings, where a network of agents aims to learn parameters that are optimal in terms of a global convex objective, while giving preference… (More)

D4L: Decentralized Dynamic Discriminative Dictionary Learning

Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro R. Ribeiro
IEEE Transactions on Signal and Information…
2015

We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn, from sequential observations, statistical model parameters jointly with… (More)

Doubly random parallel stochastic methods for large scale learning

Aryan Mokhtari, Alec Koppel, Alejandro R. Ribeiro
2016 American Control Conference (ACC)
2016

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 propose the random… (More)

Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation

Aryan Mokhtari, Alec Koppel, Gesualdo Scutari, Alejandro R. Ribeiro
2017 IEEE International Conference on Acoustics…
2017

We consider supervised learning problems over training sets in which both the number of training examples and the dimension of the feature vectors are large. We focus on the case where the loss… (More)

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

Andrea Simonetto, Aryan Mokhtari, Alec Koppel, Geert Leus, Alejandro R. Ribeiro
IEEE Transactions on Signal Processing
2016

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution… (More)

Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization

Andrea Simonetto, Alec Koppel, Aryan Mokhtari, G. Leus, Alejandro R. Ribeiro
IEEE Transactions on Automatic Control
2017

We develop algorithms that find and track the optimal solution trajectory of time-varying convex optimization problems that consist of local and network-related objectives. The algorithms are derived… (More)

Prediction-correction methods for time-varying convex optimization

Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro R. Ribeiro
2015 49th Asilomar Conference on Signals, Systems…
2015

We consider unconstrained convex optimization problems with objective functions that vary continuously in time. We propose algorithms with a discrete time-sampling scheme to find and track the… (More)

Online learning for characterizing unknown environments in ground robotic vehicle models

Alec Koppel, Jonathan Fink, Garrett Warnell, Ethan Stump, Alejandro R. Ribeiro
2016 IEEE/RSJ International Conference on…
2016

In pursuit of increasing the operational tempo of a ground robotics platform in unknown domains, we consider the problem of predicting the distribution of structural state-estimation error due to… (More)

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning

Aryan Mokhtari, Alec Koppel, Alejandro R. Ribeiro
ArXiv
2016

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 propose the random… (More)

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