Minimisation of discrete energies defined over factors is an important problem in computer vision, and a vast number of MAP inference algorithms have been proposed. Different inference algorithms perform better on factor graph models (GMs) from different underlying problem classes, and in general it is difficult to know which algorithm will yield the lowest energy for a given GM. To mitigate this difficulty, survey papers advise the practitioner on what algorithms perform well on what classes of models. We take the next step forward, and present a technique to automatically select the best inference algorithm for an input GM. We validate our method experimentally on an extended version of the OpenGM2 benchmark, containing a diverse set of vision problems. On average, our method selects an inference algorithm yielding labellings with 96 of variables the same as the best available algorithm.
Automatic algorithm selection was pioneered by @cite_5 , which considered algorithms for quadrature and process scheduling. More recently, machine learning techniques have been used to select algorithms for constraint-satisfaction @cite_49 , and other combinatorial search problems @cite_42 . However, none of these works consider selecting MAP inference algorithms.
@cite_5 @cite_42 @cite_49
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It is well established that in many scenarios there is no single solver that will provide optimal performance across a wide range of problem instances. Taking advantage of this observation, research into algorithm selection is designed to help identify the best approach for each problem at hand. This segregation is usually based on carefully constructed features, designed to quickly present the overall structure of the instance as a constant size numeric vector. Based on these features, a plethora of machine learning techniques can be utilized to predict the appropriate solver to execute, leading to significant improvements over relying solely on any one solver. However, being manually constructed, the creation of good features is an arduous task requiring a great deal of knowledge of the problem domain of interest. To alleviate this costly yet crucial step, this paper presents an automated methodology for producing an informative set of features utilizing a deep neural network. We show that the presented approach completely automates the algorithm selection pipeline and is able to achieve significantly better performance than a single best solver across multiple problem domains. We present an instance-specific test-time dynamic feature selection algorithm. Our algorithm sequentially chooses features given previously selected features and their values. It stops the selection process to make a prediction according to a user-specified accuracy-cost trade-off. We cast the sequential decision-making problem as a Markov Decision Process and apply imitation learning techniques. We address the problem of learning and inference jointly in a simple multiclass classification setting. Experimental results on UCI datasets show that our approach achieves the same or higher accuracy using only a small fraction of features than static feature selection methods. Algorithm selection can be performed using a model of runtime distribution, learned during a preliminary training phase. There is a trade-off between the performance of model-based algorithm selection, and the cost of learning the model. In this paper, we treat this trade-off in the context of bandit problems. We propose a fully dynamic and online algorithm selection technique, with no separate training phase: all candidate algorithms are run in parallel, while a model incrementally learns their runtime distributions. A redundant set of time allocators uses the partially trained model to propose machine time shares for the algorithms. A bandit problem solver mixes the model-based shares with a uniform share, gradually increasing the impact of the best time allocators as the model improves. We present experiments with a set of SAT solvers on a mixed SAT-UNSAT benchmark; and with a set of solvers for the Auction Winner Determination problem. In this article, we address the problem of automatic constraint selection to improve the performance of constraint-based clustering algorithms. To this aim we propose a novel active learning algorithm that relies on a k-nearest neighbors graph and a new constraint utility function to generate queries to the human expert. This mechanism is paired with propagation and refinement processes that limit the number of constraint candidates and introduce a minimal diversity in the proposed constraints. Existing constraint selection heuristics are based on a random selection or on a min-max criterion and thus are either inefficient or more adapted to spherical clusters. Contrary to these approaches, our method is designed to be beneficial for all constraint-based clustering algorithms. Comparative experiments conducted on real datasets and with two distinct representative constraint-based clustering algorithms show that our approach significantly improves clustering quality while minimizing the number of human expert solicitations.