자료유형  

 학위논문

Control Number  

0016934894

International Standard Book Number  

9798380482981

Dewey Decimal Classification Number  

600

Main Entry-Personal Name  

Izzo, Zachary Luigi Edward.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(210 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

General Note  

Advisor: Zou, James;Ying, Lexing.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Machine learning (ML) and AI have achieved remarkable, super-human performance in a wide variety of domains: computer vision, natural language processing, and protein folding, to name but a few. Until recently, most advancements have taken a model-centric approach, focusing primarily on improved neural network architectures (ConvNets, ResNets, transformers, etc.) and optimization procedures for training these models (batch norm, dropout, neural architecture search, etc.). Relatively less attention has been paid to the data used to train these models, in spite of the well-known fact that ML is critically dependent on high-quality data, captured succinctly with the phrase "garbage in, garbage out." As the returns on ever larger and more complicated models diminish (MT-NLG from Nvidia and Microsoft having 530B parameters), researchers have begun to realize the importance of taking a data-centric approach and developing principled methods for studying the fuel for these models: the data itself. Beyond improved task performance, a data-centric perspective also allows us to take socially critical considerations, such as data privacy, into account.In this thesis, we will take a critical look at several points in the ML data pipeline: before, during, and after model training. Before model training, we will explore the problem of data selection: which data should be used to train the model, and on what type of data should we expect our model to work? As we move forward into model training, we will turn our attention to two issues which can result from the interaction of our ML systems with the environment in which they are deployed. The first issue is that of data privacy: how can we prevent our models from leaking sensitive information about their training data? The second issue concerns the dynamic nature of some modeled populations. Especially when our model is used to make socially impactful decisions (e.g., automated loan approval or recommender systems), the model itself may impact the distribution of the data, leading to degraded performance. Lastly, despite following best practices before and during model training, it may be the case that we want to post-process a model to remove the effects of certain data after training. How can this be achieved in a computationally efficient manner?This thesis covers novel solutions for each of the preceding problems, with an emphasis on the provable guarantees for each of the proposed algorithms. By applying mathematical rigor to challenging real-world problems, we can develop algorithms which are both effective and trustworthy.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 85-04B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641416