Search Results for author: Yashesh Dhebar

Found 6 papers, 2 papers with code

Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems

no code implementations20 Sep 2020 Yashesh Dhebar, Kalyanmoy Deb, Subramanya Nageshrao, Ling Zhu, Dimitar Filev

In this paper, we use a recently proposed nonlinear decision-tree (NLDT) approach to find a hierarchical set of control rules in an attempt to maximize the open-loop performance for approximating and explaining the pre-trained black-box DRL (oracle) agent using the labelled state-action dataset.

Bilevel Optimization

Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems

no code implementations2 Aug 2020 Yashesh Dhebar, Kalyanmoy Deb

By restricting the structure of split-rule at each conditional node and depth of the decision tree, the interpretability of the classifier is assured.

Bilevel Optimization

Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

1 code implementation3 Dec 2019 Zhichao Lu, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, Vishnu Naresh Boddeti

While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches.

Classification Computational Efficiency +4

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