Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]

• URL: http://arxiv.org/abs/2006.12328v2
• Date: Tue, 23 Jun 2020 09:27:59 GMT
• Title: Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
• Authors: Joeran Beel, Bryan Tyrell, Edward Bergman, Andrew Collins, Shahad Nagoor
• Abstract summary: Key to algorithm selection via meta-learning is often the (meta) features, which sometimes do not provide enough information to train a meta-learner effectively. We propose a Siamese Neural Network architecture for automated algorithm selection that focuses more on 'alike performing' instances than meta-features.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do not provide enough information to train a meta-learner effectively. We propose a Siamese Neural Network architecture for automated algorithm selection that focuses more on 'alike performing' instances than meta-features. Our work includes a novel performance metric and method for selecting training samples. We introduce further the concept of 'Algorithm Performance Personas' that describe instances for which the single algorithms perform alike. The concept of 'alike performing algorithms' as ground truth for selecting training samples is novel and provides a huge potential as we believe. In this proposal, we outline our ideas in detail and provide the first evidence that our proposed metric is better suitable for training sample selection that standard performance metrics such as absolute errors.

Related papers

• 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)
• Automatic learning algorithm selection for classification via convolutional neural networks [0.0]
  The goal of this study is to learn the inherent structure of the data without identifying meta-features. Experiments with simulated datasets show that the proposed approach achieves nearly perfect performance in identifying linear and nonlinear patterns.
  arXiv  Detail & Related papers  (2023-05-16T01:57:01Z)
• Explainable Model-specific Algorithm Selection for Multi-Label Classification [6.442438468509492]
  Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. Several MLC algorithms have been proposed in the literature, resulting in a meta-optimization problem. We investigate in this work the quality of an automated approach that uses characteristics of the datasets.
  arXiv  Detail & Related papers  (2022-11-21T07:42:11Z)
• The Information Geometry of Unsupervised Reinforcement Learning [133.20816939521941]
  Unsupervised skill discovery is a class of algorithms that learn a set of policies without access to a reward function. We show that unsupervised skill discovery algorithms do not learn skills that are optimal for every possible reward function.
  arXiv  Detail & Related papers  (2021-10-06T13:08:36Z)
• 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)
• 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)
• Meta Learning Black-Box Population-Based Optimizers [0.0]
  We propose the use of meta-learning to infer population-based blackbox generalizations. We show that the meta-loss function encourages a learned algorithm to alter its search behavior so that it can easily fit into a new context.
  arXiv  Detail & Related papers  (2021-03-05T08:13:25Z)
• 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)
• Fast Few-Shot Classification by Few-Iteration Meta-Learning [173.32497326674775]
  We introduce a fast optimization-based meta-learning method for few-shot classification. Our strategy enables important aspects of the base learner objective to be learned during meta-training. We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-10-01T15:59:31Z)
• 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)

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.