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Filtering by: Alternative title ssim No. 15 Remove constraint Alternative title ssim: No. 15 Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Committee Member Raich, Raviv Remove constraint Committee Member: Raich, Raviv

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  • Adiabatic Markov Decision Process : convergence of value iteration algorithm

    Creator:
    Duong, Thai
    Abstract:
    Markov Decision Process (MDP) is a well-known framework for devising the optimal decision making strategies under uncertainty. Typically, the decision maker assumes a stationary environment which is characterized by a time-invariant transition probability matrix. However, in many real-world scenarios, this assumption is not justified, thus the optimal strategy might not...
    Resource Type:
    Masters Thesis
    Full Text:
    Markov Decision Process . . . . . . . . . . . . . . . . . . . . . . 15 3.1 Preliminaries on Adiabatic

  • Anomaly Detection: Theory, Explanation and User Feedback

    Creator:
    Siddiqui, Md Amran
    Abstract:
    Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
    Resource Type:
    Dissertation
    Full Text:
    . Unfortunately, most anomaly detectors provide no explanations about why an instance was considered anomalous

  • 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...
    Resource Type:
    Dissertation
    Full Text:
    an unsupervised method, where no supervision about the data is available for learning

  • Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism

    Creator:
    Moissinac, Béatrice
    Abstract:
    Auctions are used to solve resource allocation problem between many agents and many items in real-world settings. Unfortunately, in most cases, it is possible for selfish agents to manipulate the system for their own interest at the expense of the social welfare. Such manipulation can be prevented using the Vickrey-Clarke-Groves...
    Resource Type:
    Masters Thesis
    Full Text:
    Difference Utilities using Reinforcement Learning . . . . . . . 15 5.1 Variation of El Farol Bar Problem

  • Learning ranking functions for efficient search

    Creator:
    Xu, Yuehua
    Abstract:
    This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for...
    Resource Type:
    Dissertation
    Full Text:
    small number of planning domains and/or small number of test problems. There is no empirical evidence

  • Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework

    Creator:
    Duong, Thai
    Abstract:
    We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the...
    Resource Type:
    Masters Thesis
    Full Text:
    on Graph and Probabilistic MDP Policy . . . . . . . . 15 4.3 Fast Markov Decision Processes

  • Leveraging Structures of the Data in Deep Learning

    Creator:
    Li, Xingyi
    Abstract:
    The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.6.1

  • Visual object discovery and understanding

    Creator:
    Yuan, Jialin
    Abstract:
    Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
    Resource Type:
    Dissertation
    Full Text:
    DVIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.3.1 The