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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 74Machine Learning
- 70Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 22Natural Language Processing
- 22Reinforcement learning
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Spring 2010
As the volume of data on the Web or in databases increases, data integration is becoming more expensive and challenging than ever before. One of the challenges is entity resolution when integrating data from different sources. References with different representations but referring to the same...
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Fall 2023
Evaluating and ranking the difficulty and enjoyment of puzzles is important in game design. Typically, such rankings are constructed manually for each specific game, which can be time consuming, subject to designer bias, and requires extensive play testing. An approach to ranking that generalizes...
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Spring 2017
Edge-labeled graphs are widely used to describe relationships between entities in a database. We study a class of queries on edge-labeled graphs, referred to as exemplar queries, where each query gives an example of what the user is searching for. Given an exemplar query, we study the problem of...
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Estimating Fine-Grained Mobile Application Energy Use based on Run-Time Software Measured Features
DownloadFall 2020
Inefficient mobile software kills battery life. Yet, developers lack the tools necessary to detect and solve energy bugs in software. In addition, developers are usually tasked with the creation of software features and triaging existing bugs. This means that most developers do not have the time...
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Fall 2019
This thesis proposes a method to estimate robot localization error without having a ground-truth measurement of robot position. Robot localization refers to estimating a robot position and orientation (pose) within a known map, where the error is the difference between the robot’s ground-truth...
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Estimating the Overlap of Top Instances in Lists Ranked by Correlation to Label
Spring 2012
Recent advances in high-throughput technologies, such as genome-wide SNP analysis and microar- ray gene expression profiling, have led to a multitude of ranked lists, where the features (SNPs, genes) are sorted based on their individual correlation with a phenotype. Multiple reviews have shown...
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Fall 2023
Modelling agent preferences has applications in a range of fields including economics and increasingly, artificial intelligence. These preferences are not always known and thus may need to be estimated from observed behavior, in which case a model is required to map agent preferences to...
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Spring 2021
Temporal difference (TD) methods provide a powerful means of learning to make predictions in an online, model-free, and highly scalable manner. In the reinforcement learning (RL) framework, we formalize these prediction targets in terms of a (possibly discounted) sum of rewards, called the...