SWAG: A Wrapper Method for Sparse Learning
The majority of machine learning methods and algorithms give high priority to prediction performance which may not always correspond to the priority of the users. In many cases, practitioners and researchers in different fields, going from engineering to genetics, require interpretability and replicability of the results especially in settings where, for example, not all attributes may be available to them. As a consequence, there is the need to make the outputs of machine learning algorithms more interpretable and to deliver a library of "equivalent" learners (in terms of prediction performance) that users can select based on attribute availability in order to test and/or make use of these learners for predictive/diagnostic purposes. To address these needs, we propose to study a procedure that combines screening and wrapper approaches which, based on a user-specified learning method, greedily explores the attribute space to find a library of sparse learners with consequent low data collection and storage costs. This new method (i) delivers a low-dimensional network of attributes that can be easily interpreted and (ii) increases the potential replicability of results based on the diversity of attribute combinations defining strong learners with equivalent predictive power. We call this algorithm "Sparse Wrapper AlGorithm" (SWAG).
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeDiagnosticDiversitySparse LearningSimilar Papers 제목 키워드 기반
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribu…
regressionState EstimationUncertainty QuantificationSWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Prediction
We describe the system submitted by the SWAGex team to the SemEval-2020 Commonsense Validation and Explanation Task. We use multiple methods on the pre-trained language model BERT (Devlin et al., 2018) for tasks that req…
Language ModelingLanguage ModellingSWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation
While existing recognition approaches excel at identifying current surgical phases, they provide limited foresight into future procedural steps, restricting their intraoperative utility. Similarly, current anticipation m…
ClassificationLong Term AnticipationPass ClassificationregressionSWAG: Storytelling With Action Guidance
Automated long-form story generation typically employs long-context large language models (LLMs) for one-shot creation, which can produce cohesive but not necessarily engaging content. We introduce Storytelling With Acti…
Story GenerationThe SWAG Algorithm; a Mathematical Approach that Outperforms Traditional Deep Learning. Theory and Implementation
The performance of artificial neural networks (ANNs) is influenced by weight initialization, the nature of activation functions, and their architecture. There is a wide range of activation functions that are traditionall…
Learning Theory