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Filtering by: Creator Casstevens, Terry M. Committee Member Dietterich, Thomas G. Degree Field Computer Science

Individual Fairness Optimization in Machine Learning with Minimax Loss and an Abstain Option

Creator:

Behnoudfar, Ali

Abstract:

In this thesis, a new learning algorithm is introduced that is targeted towards individual fairness. In order to be individually fair, mispredictions need to be avoided as each such prediction means the learning algorithm was unfair towards some individual. Therefore, achieving individual fairness implies having a perfect classifier, which is...

Resource Type:

Masters Thesis

Full Text:

becomes, arg min m∈M (max s∈S φm(f(Xs), ys)) (1.1) where (X, y) are the training samples, f() is the
Learning and Improving Policies for Probabilistic Planning Problems

Creator:

Issakkimuthu, Murugeswari

Abstract:

In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...

Resource Type:

Dissertation

Full Text:

. The output layer ZL = {zL0 , zL1 , zL2 , . . . , zLm} contains m + 1 nodes, where zL1 , . . . , zLm
Learning ranking functions for efficient search

Creator:

Xu, Yuehua

Abstract:

This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for...

Resource Type:

Dissertation

Full Text:

nodes to m-dimensional real-valued vectors, and < is a total preference ordering on search nodes. We