Collaborating Authors

Column Selection via Adaptive Sampling

Neural Information Processing Systems

Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm that can be used to improve any relative-error column selection algorithm. Our algorithm delivers a tighter theoretical bound on the approximation error which we also demonstrate empirically using two well known relative-error column subset selection algorithms. Our experimental results on synthetic and real-world data show that our algorithm outperforms non-adaptive sampling as well as prior adaptive sampling approaches. Papers published at the Neural Information Processing Systems Conference.

Algorithm Selection for Combinatorial Search Problems: A Survey

AI Magazine

The algorithm selection problem is concerned with selecting the best algorithm to solve a given problem instance on a case-by-case basis. It has become especially relevant in the last decade, with researchers increasingly investigating how to identify the most suitable existing algorithm for solving a problem instance instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where algorithm selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine algorithm selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which algorithm selection has been approached. This article contrasts and compares different methods for solving the problem as well as ways of using these solutions.

A probabilistic and multi-objective analysis of lexicase selection and ε-lexicase selection. - PubMed - NCBI


Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selection to solve difficult problems in program synthesis and symbolic regression, the central goal of this paper is to develop the theoretical underpinnings that explain its performance. To this end, we derive an analytical formula that gives the expected probabilities of selection under lexicase selection, given a population and its behavior. In addition, we expand upon the relation of lexicase selection to many-objective optimization methods to describe the behavior of lexicase selection, which is to select individuals on the boundaries of Pareto fronts in high-dimensional space. We show analytically why lexicase selection performs more poorly for certain sizes of population and training cases, and show why it has been shown to perform more poorly in continuous error spaces.

Latent Features for Algorithm Selection

AAAI Conferences

The success and power of algorithm selection techniques has been empirically demonstrated on numerous occasions, most noticeably in the competition settings like those for SAT, CSP, MaxSAT, QBF, etc. Yet while there is now a plethora of competing approaches, all of them are dependent on the quality of a set of structural features they use to distinguish amongst the instances. Over the years, each domain has defined and refined its own set of features, yet at their core they are mostly a collection of everything that was considered useful in the past. As an alternative to this shotgun generation of features, this paper instead proposes a more systematic approach. Specifically, the paper shows how latent features gathered from matrix decomposition are enough for a linear model to achieve a level of performance comparable to a perfect Oracle portfolio. This information can, in turn, help guide researchers to the kinds of structural features they should be looking for, or even just identifying when such features are missing.

Algorithm Selection in Bilateral Negotiation

AAAI Conferences

Despite the abundance of strategies in the literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible set- tings. Thus, agent designers face an “algorithm selection” problem— which negotiation strategy to choose when facing a new negotiation. Our approach to this problem is to pre- dict the performance of different strategies based on structural features of the domain and to select the negotiation strategy that is predicted to be most successful using a “meta-agent”. This agent was able to outperform all of the finalists to the recent Automated Negotiation Agent Competition (ANAC). Our results have insights for agent-designers, demonstrating that “a little learning goes a long way”, despite the inherent uncertainty associated with negotiation under incomplete in- formation.