Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 12014 for “"Robust"”.
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Robust optimization
We propose new methodologies in robust optimization that promise greater tractability, both theoretically and practically than the classical robust framework. We cover a broad range of mathematical optimization problems, including linear optimization (LP), quadratic constrained quadratic …
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Efficiently Learning Robust, Adaptive Controllers from Robust Tube MPC
… requires adaptation capabilities and robustness to uncertainties. Existing robust and adaptive controllers, such as those based on model predictive control (MPC), can achieve impressive performance at the cost of heavy online onboard computations. Strategies that efficiently learn …
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Robust graph transduction
… in this thesis we aim to develop a series of robust graph transduction methodologies via iterative or non-iterative way, so that they can perfectly handle the difficult noisy data points. Our works are summarized as follows: In Chapter 2, we propose a robust non-iterative algorithm named …
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Robust portfolio optimization
… and Nemirovski [3] therefore introduced the robust counterpart approach of the optimization problem to provide more conservative results. And on the ground of their work, Schottle [26] furthermore proposed the local robust counterpart approach with the smaller uncertainty set. This paper …
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Robust question answering
… de pergunta resposta completo, eficiente e robusto, capaz de competir com os sistemas mais avançados existentes actualmente. Uma opção importante tomada foi a utilização da língua Portuguesa, uma língua falada por um vasto número de pessoas, o que constitui um requisito importante para um …
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Adaptive Robust Control
… a novel approach to the problem of adaptive robust control. The essential idea involved is to fragment the modeling uncertainty in a physical plant into "small" families of plant models for which robust linear time-invariant controllers can be readily designed. We then implement a convex …
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Robust synthetic control
In this thesis, we present a robust generalization of the synthetic control method. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of …
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Robust network computation
In this thesis, we present various models of distributed computation and algorithms for these models. The underlying theme is to come up with fast algorithms that can tolerate faults in the underlying network. We begin with the classical message-passing model of computation, surveying many known …
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Relatively robust grasping
… thesis presents an approach for grasping objects robustly under significant positional uncertainty. In the field of robot manipulation there has been a great deal of work on how to grasp objects stably, and in the field of robot motion planning there has been a great deal of work on how to find …
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Nonconvex robust optimization
We propose a novel robust optimization technique, which is applicable to nonconvex and simulation-based problems. Robust optimization finds decisions with the best worst-case performance under uncertainty. If constraints are present, decisions should also be feasible under perturbations. In the …
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Robust network design
… thesis is to establish a framework that enhances robustness in investment decision-making within the 2-6 year capacity planning horizon. This entails the ability to balance risk and return for the company and account for the cyclical nature of business and other strategic considerations. The …
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Robust Updated MPC Schemes
… approach comes with the disadvantage of reduced robustness of the closed-loop solution against perturbations. We introduce the updated multistep MPC where an update is performed to the multistep MPC based on re-optimizations on shrinking horizons giving a straightforward approach to provide a …
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Robust motion estimation techniques
… perspectives. In this thesis, we study different robust motion estimation techniques, such as block matching, motion intensity profile technique, and global motion estimation. Block matching has been widely used for motion estimation in video compression. This approach performs very well when …
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Robust Neural Machine Translation
<p>This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or …
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On Cluster Robust Models
<p>Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, …
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Robust censorship-resistant communication
A key challenge in censorship circumvention is being able to direct legitimate users to redirection proxies while preventing censors, posing as insiders, from discovering their addresses and blocking them. In this thesis, we study how to protect and/or design censorship circumvention systems to …
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Risk and robust optimization
… explores the connections between risk theory and robust optimization. Specifically, we show that there is a one-to-one correspondence between a class of risk measures known as coherent risk measures and uncertainty sets in robust optimization. An important consequence of this is that one may …
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Robust autonomous vehicle navigation
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2002.
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Towards robust malware detection
… training of hardened models towards the goal of robustness against adversarial variants. Additionally, we provide visualization tools for analysis of hardened models. Our tools illustrate the difference in loss behavior between models trained with different methods, the effect of adversarial …
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