Index Catalog // Hyrax

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Schöpfer:: Dodge, Jonathan E.
Abstract:: Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
Resource Type:: Dissertation

Machine learning methods for public policy : simulation, optimization, and visualization

Schöpfer:: McGregor, Sean
Abstract:: Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for...
Resource Type:: Dissertation

Simulator-Defined MDP Planning with Applications in Natural Resource Management

Schöpfer:: Alkaee Taleghan, Majid
Abstract:: This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...
Resource Type:: Dissertation

Monte Carlo Tree Search with Fixed and Adaptive Abstractions

Schöpfer:: Hostetler, Jesse A.
Abstract:: Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
Resource Type:: Dissertation

Improving automated email tagging with implicit feedback

Schöpfer:: Sorower, Mohammad Shahed
Abstract:: Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
Resource Type:: Dissertation

Machine learning in engineering : techniques to speed up numerical optimization

Schöpfer:: Cerbone, G. (Giuseppe)
Abstract:: Many important application problems in engineering can be formalized as nonlinear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. For example, these methods depend critically on choosing a good starting point from which to perform the optimization search. In high-dimensional spaces, numerical...
Resource Type:: Dissertation

Correct abstraction in counter-planning : a knowledge compilation approach

Schöpfer:: Flann, Nicholas S.
Abstract:: Knowledge compilation improves search-intensive problem-solvers that are easily specified but inefficient. One promising approach improves efficiency by constructing a database of problem-instance/best-action pairs that replace problem-solving search with efficient lookup. The database is constructed by reverse enumeration- expanding the complete search space backwards, from the terminal problem instances. This approach...
Resource Type:: Dissertation

Evaluation functions in aerial image segmentation

Schöpfer:: Langford, William T.
Abstract:: Remote sensing is the most practical way to acquire large amounts of land cover data for monitoring and understanding environmental change, so it is important to be able to map land cover from imagery. Maps defining land cover patches as polygons rather than pixels greatly improve processing efficiency in models...
Resource Type:: Dissertation

Applying machine learning for prediction, recommendation, and integration

Schöpfer:: Bao, Xinlong
Abstract:: This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning. FolderPredictor is an intelligent desktop software tool that helps...
Resource Type:: Dissertation

Reinforcement learning for job-shop scheduling

Schöpfer:: Zhang, Wei, 1960-
Resource Type:: Dissertation

Hyrax

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Machine learning methods for public policy : simulation, optimization, and visualization

Simulator-Defined MDP Planning with Applications in Natural Resource Management

Monte Carlo Tree Search with Fixed and Adaptive Abstractions

Improving automated email tagging with implicit feedback

Machine learning in engineering : techniques to speed up numerical optimization

Correct abstraction in counter-planning : a knowledge compilation approach

Evaluation functions in aerial image segmentation

Applying machine learning for prediction, recommendation, and integration

Reinforcement learning for job-shop scheduling

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