73,334 research outputs found
A General Framework for Fair Regression
Fairness, through its many forms and definitions, has become an important
issue facing the machine learning community. In this work, we consider how to
incorporate group fairness constraints in kernel regression methods, applicable
to Gaussian processes, support vector machines, neural network regression and
decision tree regression. Further, we focus on examining the effect of
incorporating these constraints in decision tree regression, with direct
applications to random forests and boosted trees amongst other widespread
popular inference techniques. We show that the order of complexity of memory
and computation is preserved for such models and tightly bound the expected
perturbations to the model in terms of the number of leaves of the trees.
Importantly, the approach works on trained models and hence can be easily
applied to models in current use and group labels are only required on training
data.Comment: 8 pages, 4 figures, 2 pages reference
Implementing and reasoning about hash-consed data structures in Coq
We report on four different approaches to implementing hash-consing in Coq
programs. The use cases include execution inside Coq, or execution of the
extracted OCaml code. We explore the different trade-offs between faithful use
of pristine extracted code, and code that is fine-tuned to make use of OCaml
programming constructs not available in Coq. We discuss the possible
consequences in terms of performances and guarantees. We use the running
example of binary decision diagrams and then demonstrate the generality of our
solutions by applying them to other examples of hash-consed data structures
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A survey of induction algorithms for machine learning
Central to all systems for machine learning from examples is an induction algorithm. The purpose of the algorithm is to generalize from a finite set of training examples a description consistent with the examples seen, and, hopefully, with the potentially infinite set of examples not seen. This paper surveys four machine learning induction algorithms. The knowledge representation schemes and a PDL description of algorithm control are emphasized. System characteristics that are peculiar to a domain of application are de-emphasized. Finally, a comparative summary of the learning algorithms is presented
Relational Representations in Reinforcement Learning: Review and Open Problems
This paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.\u
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