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Filtering by: Committee Member Dietterich, Thomas G. Remove constraint Committee Member: Dietterich, Thomas G.

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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
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
Machine Learning Techniques to Model Species Interactions and Distributions Under Imperfect Detection

Creator:

Seo, Eugene

Abstract:

Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...

Resource Type:

Dissertation
Anomaly Detection: Theory, Explanation and User Feedback

Creator:

Siddiqui, Md Amran

Abstract:

Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...

Resource Type:

Dissertation
Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming

Creator:

Lauer, Christopher Joseph

Abstract:

Forest management in the face of fire risk is a challenging problem because fire spreads across a landscape and because its occurrence is unpredictable. Additionally, management must be adjusted over time as unpredictable fire events are realized. Land managers have some control over the vegetation conditions that facilitate fire spread,...

Resource Type:

Dissertation
Insights into the Geochemical Evolution of the Youngest Toba Tuff (Sumatra, Indonesia) Magma Chamber Through the Lens of Zircon-hosted Melt Inclusions

Creator:

Lerner, Allan

Abstract:

Supervolcanic eruptions are among the most catastrophic phenomena on Earth, erupting 100s-1000s of cubic kilometers of magma, and producing devastating local effects and global climate perturbations. The largest supervolcanic eruption of the last 28 Ma was the 74 ka Youngest Toba Tuff (YTT) eruption from Sumatra, Indonesia, which erupted 2,800...

Resource Type:

Masters Thesis
Integrating learning and search for structured prediction

Creator:

Doppa, Janardhan Rao

Abstract:

We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...

Resource Type:

Dissertation
New learning modes for sequential decision making

Creator:

Judah, Kshitij

Abstract:

This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...

Resource Type:

Dissertation
Learning greedy policies for the easy-first framework

Creator:

Xie, Jun

Abstract:

Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...

Resource Type:

Masters Thesis
Multi-criteria average reward reinforcement learning

Creator:

Natarajan, Sriraam

Abstract:

Reinforcement learning (RL) is the study of systems that learn from interaction with their environment. The current framework of Reinforcement Learning is based on receiving scalar rewards, which the agent aims to maximize. But in many real world situations, tradeoffs must be made among multiple objectives. This necessitates the use...

Resource Type:

Masters Thesis

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Academic Affiliation

Electrical Engineering and Computer Science11
Applied Economics1
College of Earth, Ocean, and Atmospheric Sciences1

Advisor

Fern, Alan3
Fern, Alan P.2
Tadepalli, Prasad2
Fern, Xiaoli Z.1
Forbes, Leonard1
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Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2004 to 2023

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Committee Member

Dietterich, Thomas G.[remove]13
Tadepalli, Prasad5
Raich, Raviv2
Bell, Chris1
Bolte, John1
more Committee Members »

Creator

Behnoudfar, Ali1
Doppa, Janardhan Rao1
Han, Jifeng1
Issakkimuthu, Murugeswari1
Judah, Kshitij1
more Creators »

Contributor

Clarke, Justin1
Fu, Xiao1
Hallman, Tyler A.1
Hutchinson, Rebecca A.1
Kilbride, John1
more Contributors »

Date

Date range begin – Date range end

Current results range from 2003 to 2022

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Decade

2020-20293
2010-20197
2000-20093

Degree Field

Computer Science9
Electrical and Computer Engineering2
Applied Economics1
Geology1

Degree Level

Doctoral9
Master's4

Degree Name

Doctor of Philosophy (Ph.D.)9
Master of Science (M.S.)4

Language

English [eng]13

License

All rights reserved12

Non-Academic Affiliation

Oregon State University. Graduate School9

Peer Reviewed

No11

Resource Type

Dissertation9
Masters Thesis4

Rights Statement

In Copyright12
Copyright Not Evaluated1

Subject

Machine learning5
Heuristic algorithms2
Anomaly detection (Computer security)1
Electric generators1
Electric noise1
more Subjects »

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