2026-09-23T01:24:50Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/930702026-06-16T18:54:27Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Cynthia Rudin.
Tulabandhula, Theja
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-01-20T17:59:54Z
2015-01-20T17:59:54Z
2014
2014
http://hdl.handle.net/1721.1/93070
900005872
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 253-263).
We quantify the effects of learning and decision making on each other in three parts. In the first part, we look at how knowledge about decision making can influence learning. Let the decision cost be the amount spent by the practitioner in executing a policy. If we have prior knowledge about this cost, for instance that it should be low, then this knowledge can help restrict the hypothesis space for learning, which can help with its generalization. We derive a suite of theoretical generalization bounds and an algorithm for this setting. In the second part, we look at how knowledge about learning can influence decision making. We study this in the context of robust optimization. Taking the uncertainty of learning the right model into account, we derive multiple probabilistic guarantees on the robustness of the resulting policy. In the last part, we explore the interactions between learning and decision making in depth for two applications. The first application is in the area of power grid maintenance and the second is in the area of professional racing. We provide tailored solutions for modeling, predicting and making decisions in each context.
by Theja Tulabandhula.
Ph.D.
263 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Interactions between learning and decision making
Thesis
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Publication
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<Abstract>We quantify the effects of learning and decision making on each other in three parts. In the first part, we look at how knowledge about decision making can influence learning. Let the decision cost be the amount spent by the practitioner in executing a policy. If we have prior knowledge about this cost, for instance that it should be low, then this knowledge can help restrict the hypothesis space for learning, which can help with its generalization. We derive a suite of theoretical generalization bounds and an algorithm for this setting. In the second part, we look at how knowledge about learning can influence decision making. We study this in the context of robust optimization. Taking the uncertainty of learning the right model into account, we derive multiple probabilistic guarantees on the robustness of the resulting policy. In the last part, we explore the interactions between learning and decision making in depth for two applications. The first application is in the area of power grid maintenance and the second is in the area of professional racing. We provide tailored solutions for modeling, predicting and making decisions in each context.</Abstract>
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