Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex black boxes, meaning their internal logic and inner workings are hidden to the user and even experts cannot fully understand the rationale behind their predictions. Moreover, new regulations and highly regulated domains have made the audit and verifiability of decisions mandatory, increasing the demand for the ability to question, understand, and trust machine learning systems, for which interpretability is indispensable. The research community has recognized this interpretability problem and focused on developing both interpretable models and explanation methods over the past few years. However, the emergence of these methods shows there is no consensus on how to assess the explanation quality. Which are the most suitable metrics to assess the quality of an explanation? The aim of this article is to provide a review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics. Furthermore, a complete literature review is presented in order to identify future directions of work on this field.

Machine Learning Interpretability: A Survey on Methods and Metrics

We propose a general modeling and inference framework that combines the complementary strengths of probabilistic graphical models and deep learning methods. Our model family composes latent graphical models with neural network observation likelihoods. For inference, we use recognition networks to produce local evidence potentials, then combine them with the model distribution using efficient message-passing algorithms. All components are trained simultaneously with a single stochastic variational inference objective. We illustrate this framework by automatically segmenting and categorizing mouse behavior from raw depth video, and demonstrate several other example models.

/pdf/composing-graphical-models-with-neural-networks-for-3l1dun0l1o.pdf

Composing graphical models with neural networks for structured representations and fast inference

This paper is about distributed derivative-based algorithms for solving optimization problems with a separable (potentially nonconvex) objective function and coupled affine constraints. A parallelizable method is proposed that combines ideas from the fields of sequential quadratic programming and augmented Lagrangian algorithms. The method negotiates shared dual variables that may be interpreted as prices, a concept employed in dual decomposition methods and the alternating direction method of multipliers (ADMM). Here, each agent solves its own small-scale nonlinear programming problem and communicates with other agents by solving coupled quadratic programming problems. These coupled quadratic programming problems have equality constraints for which parallelizable methods are available. The use of techniques associated with standard sequential quadratic programming methods gives a method with superlinear or quadratic convergence rate under suitable conditions. This is in contrast to existing decomposition...

An Augmented Lagrangian Based Algorithm for Distributed NonConvex Optimization

In this paper we study distributionally robust stochastic programming in a setting where there is a specified reference probability measure and the uncertainty set of probability measures consists of measures in some sense close to the reference measure. We discuss law invariance of the associated worst case functional and consider two basic constructions of such uncertainty sets. Finally we illustrate some implications of the property of law invariance.

http://www.optimization-online.org/DB_FILE/2015/12/5238.pdf

Distributionally Robust Stochastic Programming

University of Minnesota Ph.D. dissertation. May 2014. Major: Electrical Engineering. Advisor: Zhi-Quan Luo. 1 computer file (PDF); x, 193 pages.

https://conservancy.umn.edu/bitstream/handle/11299/163884/Razaviyayn_umn_0130E_14988.pdf?sequence=1&isAllowed=y

Successive Convex Approximation: Analysis and Applications

Thank you very much for reading perturbation analysis of optimization problems. Maybe you have knowledge that, people have look hundreds times for their favorite books like this perturbation analysis of optimization problems, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. perturbation analysis of optimization problems is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the perturbation analysis of optimization problems is universally compatible with any devices to read.

Benjamin Pfaff

Papers

Perturbation Analysis Of Optimization Problems