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Filtering by: Creator Helfand, Stuart C. Remove constraint Creator: Helfand, Stuart C. Degree Field Computer Science Remove constraint Degree Field: Computer Science Degree Name Doctor of Philosophy (Ph.D.) Remove constraint Degree Name: Doctor of Philosophy (Ph.D.)

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  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
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    Dissertation
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    triplets respectively; c) active learning of pairwise constraints for clustering, where the goal is to

  • An empirical study into learning through experimentation

    Creator:
    Ruff, Ritchey Alvin
    Abstract:
    A key aspect of how we understand the world revolves around an ability to manipulate our surroundings to experiment. From the scientific method through theories of child development, the ability to experiment is deemed critical; however, few studies have been performed to understand the strengths and weaknesses of different experimental...
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    Dissertation
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    number of hypotheses were possible, (b) each experiment had a large number of outcomes, and (c

  • On Avoiding Power-Seeking by Artificial Intelligence

    Creator:
    Turner, Alexander M.
    Abstract:
    We do not know how to align a very intelligent AI agent's behavior with human interests. I investigate whether—absent a full solution to this AI alignment problem—we can build smart {\ai} agents which have limited impact on the world, and which do not autonomously seek power. In this thesis, I...
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    Dissertation
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    Additional data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 C Formalizing The

  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
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    Dissertation
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    that learn to perform well. c©Copyright by Jervis Pinto June 12, 2015 All Rights Reserved

  • Interpretable Machine Learning: Applications in Biology and Genomics

    Creator:
    Danaee, Padideh
    Abstract:
    Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
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    Dissertation
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    applicable more broadly to other areas of data science. c�Copyright by Padideh Danaee December 6, 2019

  • Electra : integrating constraints, condition-based dispatching, and features exclusion into the multiparadigm language Leda

    Creator:
    Zamel, Nabil M.
    Abstract:
    Multiparadigm languages are languages that are designed to support more than one style of programming. Leda is a strongly-typed multiparadigm programming language that supports imperative, functional, object-oriented, and logic programming. The constraint programming paradigm is a declarative style of programming where the programmer is able to state relationships among some...
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    Dissertation
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    research, Masami Takikawa who taught me a lot about constraints, and Dr. Leine Stuart who has been a

  • Variational Satisfiability Solving

    Creator:
    Young, Jeffrey M.
    Abstract:
    Over the last two decades, satisfiability and satisfiability-modulo theory (SAT/SMT) solvers have grown powerful enough to be general purpose reasoning engines throughout software engineering and computer science. However, most practical use cases of SAT/SMT solvers require not just solving a single SAT/SMT problem, but solving sets of related SAT/SMT problems....
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    Dissertation
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    incremental SAT program which is suitable for execution on an incremental SAT solver. (c) A formal

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
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    inhibitor); and (c) our method “Learn and Vote” analysed on the same experiments as in the middle panel (b

  • Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

    Creator:
    Zemicheal, Tadesse H.
    Abstract:
    Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...
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    Dissertation
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    mixture model gives the most effective anomaly detections for this task. c©Copyright by Tadesse H

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
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    range dependencies in sequences. c©Copyright by Guohua Hao July 21, 2009 All Rights Reserved