Search Constraints

Start Over
Filtering by: Creator Spaeth, Andrew D. Remove constraint Creator: Spaeth, Andrew D. Contributor advisor sim Tadepalli, Prasad Remove constraint Contributor advisor sim: Tadepalli, Prasad

Search Results

  • Learning classification rules by randomized iterative local search

    Creator:
    Chisholm, Michael (Michael Andrew), 1974-
    Abstract:
    Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning algorithms for this problem employ some variation of a greedy separate-and-conquer algorithm. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules from examples using a...
    Resource Type:
    Masters Thesis
    Full Text:
    , D., editor, Expert Systems in the Microelectronic Age. Edinburgh University Press, Edinburgh

  • High-Dimensional Reinforcement Learning with Human Feedback

    Creator:
    Curran, William
    Abstract:
    State-of-the-art personal robots need to perform complex manipulation tasks to be viable in complex scenarios. However, many of these robots, like the PR2, use manipulators with high degrees of freedom. High degrees of freedom are desirable from a functionality standpoint, but make the learning task more difficult by adding a...
    Resource Type:
    Dissertation
    Full Text:
    -Dimensional Reinforcement Learning with Human Feedback Abstract approved: William D. Smart Prasad Tadepalli

  • 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
    Full Text:
    . Let the next state be s' and the reward is (d) (s,a) = (s,a)(1 _3) +/3(Timm +maxa'[O.l(s',a')]) (e) If

  • New Directions in Search-based Structured Prediction: Multi-Task Learning and Integration of Deep Models

    Creator:
    Ma, Chao
    Abstract:
    This thesis studies the problem of structured prediction (SP), where the agent needs to predict a structured output for a given structured input (e.g., Part-of-Speech tagging sequence for an input sentence). Many important applications including machine translation in natural language processing (NLP) and image interpretation in computer vision can be...
    Resource Type:
    Dissertation
    Full Text:
    ) where d is the depth of the search tree and b is the branching factor, but also allow the search to have

  • Bayesian methods for knowledge transfer and policy search in reinforcement learning

    Creator:
    Wilson, Aaron
    Abstract:
    How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...
    Resource Type:
    Dissertation
    Full Text:
    by D. . . . . . . . . . . . . . . . . . . . . 17 3.1 (a) Single task Bayesian RL vs. (b

  • 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
    Full Text:
    , Drew Bagnell, Roni Khardon, Andrew McCallum, Lise Getoor, Jason Eisner, Hal Daumé III, John Langford

  • Structured gradient boosting

    Creator:
    Parker, Charles (Charles Lincoln)
    Abstract:
    The goal of many machine learning problems can be formalized as the creation of a function that can properly classify an input vector, given a set of examples of that function. While this formalism has produced a number of success stories, there are notable situations in which it fails. One...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . 102 LIST OF FIGURES (Continued) Figure Page 6.1 A plot of the function dg(x, y) = d(x, y) 1 1+w

  • Adversarial Attacks in Natural Language Question-Answering

    Creator:
    Rahurkar, Prachi S.
    Abstract:
    Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based...
    Resource Type:
    Masters Thesis
    Full Text:
    that it is possible to even 3-D print some real-world objects that can fool deep neural network

  • Local and Global Explanations for Deep Image Classification via Structured Attention Graphs

    Creator:
    Shitole, Vivswan
    Abstract:
    Attention maps are popular tools of explaining the decisions of convolutional neural net-works (CNNs) for image classification. Typically, for each image of interest, a single attention map is produced, which assigns weights to pixels based on their importance to the classification. We argue that a single attention map provides an...
    Resource Type:
    Masters Thesis
    Full Text:
    Explanations for Wrong Predictions . . . . . . . . . . . . . . . . . . . 63 D Examples of Global Explanations

  • Data Classification and Extraction of Weather Reports

    Creator:
    Qin, Rui
    Abstract:
    Severe weather in the United States causes huge insured losses to crop and property frequently.It creates major impact and elicit diverse response in the weather insurance industry. Events like hail, storm, hurricane etc. are more likely to cause catastrophe losses. So it becomes crucial to collect and analyze these extreme...
    Resource Type:
    Capstone Project
    Full Text:
    the a regular expression below: \d{3}\n\w{4}\d{1, 2} \w{4} \d{6}. ∗ \n\w{6} This regular expression