Communication-Efficient Algorithms For Distributed Optimization

This thesis is concerned with the design of distributed algorithms for solving optimization problems. We consider networks where each node has exclusive access to a cost function, and design algorithms that make all nodes cooperate to find the minimum of the sum of all the cost functions. Several problems in signal processing, control, and machine learning can be posed as such optimization problems. Given that communication is often the most energy-consuming operation in networks, it is important to design communication-efficient algorithms. The main contributions of this thesis are a classification scheme for distributed optimization and a set of corresponding communication-efficient algorithms. The class of optimization problems we consider is quite general, since each function may depend on arbitrary components of the optimization variable, and not necessarily on all of them. In doing so, we go beyond the common assumption in distributed optimization and create additional structure that can be used to reduce the number of communications. This structure is captured by our classification scheme, which identifies easier instances of the problem, for example the standard distributed optimization problem, where all functions depend on all the components of the variable. In our algorithms, no central node coordinates the network, all the communications occur between neighboring nodes, and the data associated with each node is processed locally. We show several applications including average consensus, support vector machines, network flows, and several distributed scenarios for compressed sensing. We also propose a new framework for distributed model predictive control. Through extensive numerical experiments, we show that our algorithms outperform prior distributed algorithms in terms of communication-efficiency, even some that were specifically designed for a particular application.Comment: Thesis defended on October 10, 2013. Dual PhD degree from Carnegie Mellon University, PA, and Instituto Superior T\'ecnico, Lisbon, Portuga

Multi-task CNN Model for Attribute Prediction

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape