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Artificial Intelligence and Robotics Commons™
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- Machine learning (2)
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Articles 1 - 6 of 6
Full-Text Articles in Artificial Intelligence and Robotics
Detecting Anomalously Similar Entities In Unlabeled Data, Lisa D. Friedland
Detecting Anomalously Similar Entities In Unlabeled Data, Lisa D. Friedland
Doctoral Dissertations
In this work, the goal is to detect closely-linked entities within a data set. The entities of interest have a tie causing them to be similar, such as a shared origin or a channel of influence. Given a collection of people or other entities with their attributes or behavior, we identify unusually similar pairs, and we pose the question: Are these two people linked, or can their similarity be explained by chance? Computing similarities is a core operation in many domains, but two constraints differentiate our version of the problem. First, the score assigned to a pair should account for …
Stochastic Network Design: Models And Scalable Algorithms, Xiaojian Wu
Stochastic Network Design: Models And Scalable Algorithms, Xiaojian Wu
Doctoral Dissertations
Many natural and social phenomena occur in networks. Examples include the spread of information, ideas, and opinions through a social network, the propagation of an infectious disease among people, and the spread of species within an interconnected habitat network. The ability to modify a phenomenon towards some desired outcomes has widely recognized benefits to our society and the economy. The outcome of a phenomenon is largely determined by the topology or properties of its underlying network. A decision maker can take management actions to modify a network and, therefore, change the outcome of the phenomenon. A management action is an …
Learning From Pairwise Proximity Data, Hamid Dadkhahi
Learning From Pairwise Proximity Data, Hamid Dadkhahi
Doctoral Dissertations
In many areas of machine learning, the characterization of the input data is given by a form of proximity measure between data points. Examples of such representations are pairwise differences, pairwise distances, and pairwise comparisons. In this work, we investigate different learning problems on data represented in terms of such pairwise proximities. More specifically, we consider three problems: masking (feature selection) for dimensionality reduction, extension of the dimensionality reduction for time series, and online collaborative filtering. For each of these problems, we start with a form of pairwise proximity which is relevant in the problem at hand. We evaluate the …
Efficient Inference, Search And Evaluation For Latent Variable Models Of Text With Applications To Information Retrieval And Machine Translation, Kriste Krstovski
Efficient Inference, Search And Evaluation For Latent Variable Models Of Text With Applications To Information Retrieval And Machine Translation, Kriste Krstovski
Doctoral Dissertations
Latent variable models of text, such as topic models, have been explored in many areas of natural language processing, information retrieval and machine translation to aid tasks such as exploratory data analysis, automated topic clustering and finding similar documents in mono- and multilingual collections. Many additional applications of these models, however, could be enabled by more efficient techniques for processing large datasets. In this thesis, we introduce novel methods that offer efficient inference, search and evaluation for latent variable models of text. We present efficient, online inference for representing documents in several languages in a common topic space and fast …
Extending Faceted Search To The Open-Domain Web, Weize Kong
Extending Faceted Search To The Open-Domain Web, Weize Kong
Doctoral Dissertations
Faceted search enables users to navigate a multi-dimensional information space by combining keyword search with drill-down options in each facets. For example, when searching “computer monitor”' in an e-commerce site, users can select brands and monitor types from the the provided facets {“Samsung”, “Dell”, “Acer”, ...} and {“LET-Lit”, “LCD”, “OLED”, ...}. It has been used successfully for many vertical applications, including e-commerce and digital libraries. However, this idea is not well explored for general web search in an open-domain setting, even though it holds great potential for assisting multi-faceted queries and exploratory search. The goal of this work is to …
Algorithms For First-Order Sparse Reinforcement Learning, Bo Liu
Algorithms For First-Order Sparse Reinforcement Learning, Bo Liu
Doctoral Dissertations
This thesis presents a general framework for first-order temporal difference learning algorithms with an in-depth theoretical analysis. The main contribution of the thesis is the development and design of a family of first-order regularized temporal-difference (TD) algorithms using stochastic approximation and stochastic optimization. To scale up TD algorithms to large-scale problems, we use first-order optimization to explore regularized TD methods using linear value function approximation. Previous regularized TD methods often use matrix inversion, which requires cubic time and quadratic memory complexity. We propose two algorithms, sparse-Q and RO-TD, for on-policy and off-policy learning, respectively. These two algorithms exhibit linear computational …