However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change.

This field has experienced a surge in activity as researchers have produced numerous computational tools to analyze such data sets.

Neural Information Processing Systems http://nips.cc/

Although optimization is the longstanding algorithmic backbone of machine learning, new models still require the time-consuming implementation of new solvers. As a result, there are thousands of implementations of optimization algorithms for machine learning problems.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper develops a new method of performing blind source separation, by formulating the problem as an additive factorial HMM (AFHMM), and then applying signal aggregate constraints (SACs). The motivation behind this is that additional domain knowledge can be incorporated to improve the separation of the time series into components. The example used throughout the paper is energy disaggregation, where the components of domestic energy use (relating to individual appliances) can be better separated, when information relating to total (expected) usage of each appliance in a time period is incorporated. The objective function that is maximized to perform the separation (which is the log of the posterior distribution of the hidden chains given the observed data) is then transformed into a convex optimization problem.

For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive computational simulations.

We study the problem of zero-order optimization of a strongly convex function.

Partial monitoring (PM) is a general sequential decision-making problem with limited feedback ( Rus-tichini, 1999; Piccolboni and Schindelhauer, 2001).