SFO: A Toolbox for Submodular Function Optimization

In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns property, stating that adding an element to a smaller set helps more than adding it to a larger set. Similarly to convexity, submodularity allows one to efficiently find provably (near-) optimal solutions for large problems. We present SFO, a toolbox for use in MATLAB or Octave that implements algorithms for minimization and maximization of submodular functions. A tutorial script illustrates the application of submodularity to machine learning and AI problems such as feature selection, clustering, inference and optimized information gathering

Validation of agent-based models

The automatic collection of customer transaction data, through either online shops or reward cards, is producing very large databases which contain much information about consumer behaviour. What kind of information and how exploitable it is are very relevant questions. Two approaches are being used. Either one concentrates on individual behaviour and tries to apply various theoretical frameworks and results of the literature on discrete choice, or one uses clustering algorithms in order to determine several classes of customers. The very existence of such categories is likely to be the result of social interactions and influences. The literature on discrete choice cannot easily be generalised to networked interactions, which are known to be widely present in various contexts. Another approach is to use toy models of individual behaviour and concentrate on global, aggregate quantities such as market share or demand fluctuations. This raises the question of how to validate such kind of model, hence the request of Unilever. The latter should also be understood with respect to the contribution of ESGI 2004, where a very sophisticated agent-based model of consumer behaviour was proposed (but not much studied)

Efficient Minimization of Decomposable Submodular Functions

Many combinatorial problems arising in machine learning can be reduced to the problem of minimizing a submodular function. Submodular functions are a natural discrete analog of convex functions, and can be minimized in strongly polynomial time. Unfortunately, state-of-the-art algorithms for general submodular minimization are intractable for larger problems. In this paper, we introduce a novel subclass of submodular minimization problems that we call decomposable. Decomposable submodular functions are those that can be represented as sums of concave functions applied to modular functions. We develop an algorithm, SLG, that can efficiently minimize decomposable submodular functions with tens of thousands of variables. Our algorithm exploits recent results in smoothed convex minimization. We apply SLG to synthetic benchmarks and a joint classification-and-segmentation task, and show that it outperforms the state-of-the-art general purpose submodular minimization algorithms by several orders of magnitude.Comment: Expanded version of paper for Neural Information Processing Systems 201

A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users

Optimizing Sensing: From Water to the Web

Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the Web? Such problems are typically NP-hard in theory and extremely challenging in practice. The authors present algorithms that exploit submodularity to efficiently find provably near-optimal solutions to large, complex real-world sensing problems