• Publications
  • Influence
Multitask Learning
  • R. Caruana
  • Computer Science
  • Encyclopedia of Machine Learning and Data Mining
  • 1 May 1998
TLDR
Multitask Learning is an approach to inductive transfer that improves learning for one task by using the information contained in the training signals of other related tasks. Expand
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Multitask Learning
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An empirical comparison of supervised learning algorithms
TLDR
We present a large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. Expand
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Do Deep Nets Really Need to be Deep?
TLDR
In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Expand
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Removing the Genetics from the Standard Genetic Algorithm
TLDR
We present an abstraction of the genetic algorithm (GA), termed population-based incremental learning (PBIL), that explicitly maintains the statistics contained in a GA''s population, but which abstracts away the crossover operator and redefines the role of the population. Expand
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Multitask Learning: A Knowledge-Based Source of Inductive Bias
TLDR
This paper suggests that it may be easier to learn several hard tasks at one time than to learn them separately. Expand
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Predicting good probabilities with supervised learning
TLDR
We examine the relationship between the predictions made by different learning algorithms and true posterior probabilities. Expand
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Model compression
TLDR
We present a method for "compressing" large, complex ensembles into smaller, faster models, usually without significant loss in performance. Expand
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Ensemble selection from libraries of models
TLDR
We present a method for constructing ensembles from libraries of thousands of models by using many different learning algorithms and parameter settings. Expand
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Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
TLDR
We present two case studies where high-performance generalized additive models with pairwise interactions (GA2Ms) are applied to real healthcare problems yielding intelligible models with state-of-the-art accuracy. Expand
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