Frugal Algorithm Selection

• URL: http://arxiv.org/abs/2405.11059v1
• Date: Fri, 17 May 2024 19:23:30 GMT
• Title: Frugal Algorithm Selection
• Authors: Erdem Kuş, Özgür Akgün, Nguyen Dang, Ian Miguel,
• Abstract summary: There is no single algorithm that works well for all instances of a problem. In this work, we explore reducing this cost by choosing a subset of the training instances on which to train. We approach this problem in three ways: using active learning to decide based on prediction uncertainty, augmenting the algorithm predictors with a timeout predictor, and collecting training data using a progressively increasing timeout.
• Score: 1.079960007119637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When solving decision and optimisation problems, many competing algorithms (model and solver choices) have complementary strengths. Typically, there is no single algorithm that works well for all instances of a problem. Automated algorithm selection has been shown to work very well for choosing a suitable algorithm for a given instance. However, the cost of training can be prohibitively large due to running candidate algorithms on a representative set of training instances. In this work, we explore reducing this cost by choosing a subset of the training instances on which to train. We approach this problem in three ways: using active learning to decide based on prediction uncertainty, augmenting the algorithm predictors with a timeout predictor, and collecting training data using a progressively increasing timeout. We evaluate combinations of these approaches on six datasets from ASLib and present the reduction in labelling cost achieved by each option.

Related papers

• Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection [11.409496019407067]
  In a budget-limited scenario, it is crucial to carefully select an algorithm candidate and allocate a budget for training it. We propose a novel framework in which an agent must select in the process of learning the most promising algorithm without waiting until it is fully trained.
  arXiv  Detail & Related papers  (2024-10-10T08:09:58Z)
• Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut [1.4624458429745086]
  We build upon recent work in this line of research by considering the setup where, instead of selecting a single algorithm that has the best performance, we allow the possibility of selecting an algorithm based on the instance to be solved. In particular, given a representative sample of instances, we learn a neural network that maps an instance of the problem to the most appropriate algorithm for that instance. In other words, the neural network will take as input a mixed-integer optimization instance and output a decision that will result in a small branch-and-cut tree for that instance.
  arXiv  Detail & Related papers  (2024-02-04T03:03:27Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Combining K-means type algorithms with Hill Climbing for Joint Stratification and Sample Allocation Designs [0.0]
  This is a sample problem in which we search for the optimal stratification from the set of all possible stratifications of basic strata. evaluating the cost of each solution is expensive. We compare the above multi-stage combination of algorithms with three recent algorithms and report the solution costs, evaluation times and training times.
  arXiv  Detail & Related papers  (2021-08-18T08:41:58Z)
• Algorithm Selection on a Meta Level [58.720142291102135]
  We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
  arXiv  Detail & Related papers  (2021-07-20T11:23:21Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Towards Meta-Algorithm Selection [78.13985819417974]
  Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidates. We show that meta-algorithm-selection can indeed prove beneficial in some cases.
  arXiv  Detail & Related papers  (2020-11-17T17:27:33Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)
• Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
  We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
  arXiv  Detail & Related papers  (2020-01-29T09:40:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.