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Full-Text Articles in Physical Sciences and Mathematics
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Doctoral Dissertations
Text generation is an important emerging AI technology that has seen significant research advances in recent years. Due to its closeness to how humans communicate, mastering text generation technology can unlock several important applications such as intelligent chat-bots, creative writing assistance, or newer applications like task-agnostic few-shot learning. Most recently, the rapid scaling of large language models (LLMs) has resulted in systems like ChatGPT, capable of generating fluent, coherent and human-like text. However, despite their remarkable capabilities, LLMs still suffer from several limitations, particularly when generating long-form text. In particular, (1) long-form generated text is filled with factual inconsistencies to …
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Doctoral Dissertations
Federated learning is an emerging distributed learning paradigm that allows multiple users to collaboratively train a joint machine learning model without having to share their private data with any third party. Due to many of its attractive properties, federated learning has received significant attention from academia as well as industry and now powers major applications, e.g., Google's Gboard and Assistant, Apple's Siri, Owkin's health diagnostics, etc. However, federated learning is yet to see widespread adoption due to a number of challenges. One such challenge is its susceptibility to poisoning by malicious users who aim to manipulate the joint machine learning …
The Limits Of Location Privacy In Mobile Devices, Keen Yuun Sung
The Limits Of Location Privacy In Mobile Devices, Keen Yuun Sung
Doctoral Dissertations
Mobile phones are widely adopted by users across the world today. However, the privacy implications of persistent connectivity are not well understood. This dissertation focuses on one important concern of mobile phone users: location privacy. I approach this problem from the perspective of three adversaries that users are exposed to via smartphone apps: the mobile advertiser, the app developer, and the cellular service provider. First, I quantify the proportion of mobile users who use location permissive apps and are able to be tracked through their advertising identifier, and demonstrate a mark and recapture attack that allows continued tracking of users …
Problems In Graph-Structured Modeling And Learning, James Atwood
Problems In Graph-Structured Modeling And Learning, James Atwood
Doctoral Dissertations
This thesis investigates three problems in graph-structured modeling and learning. We first present a method for efficiently generating large instances from nonlinear preferential attachment models of network structure. This is followed by a description of diffusion-convolutional neural networks, a new model for graph-structured data which is able to outperform probabilistic relational models and kernel-on-graph methods at node classification tasks. We conclude with an optimal privacy-protection method for users of online services that remains effective when users have poor knowledge of an adversary's behavior.