[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118989-en":3,"doc-seo-118989-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118989,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","On robust estimation in causal machine learning - Dissertation abstract","This dissertation develops robust estimation methods for causal machine learning, with a strong focus on variational autoencoders (VAEs), energy-based models, and causal education simulations. It introduces techniques to impose substantial structure on VAE latent spaces, enabling out-of-distribution generation, structural hypothesis testing, and latent-space augmentations for improved robustness and explainability. It also proposes an energy-based unsupervised defense against poisoning attacks, reducing risk without heavy additional training or major accuracy loss. Finally, it builds a causal simulation to study human-machine interactions in educational settings, highlighting implementation challenges and comparing heuristic and deep learning effectiveness under unknown subgroup distributions and hidden student states.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nOn robust estimation in causal machine learning  \nPermalink  \n[https://escholarship.org/uc/item/7qj5v525](https://escholarship.org/uc/item/7qj5v525)  \nAuthor  \nJiang, Jeffrey  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nOn robust estimation in causal machine learning  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical and Computer Engineering  \nby  \nJeffrey Jiang  \n2024  \n© Copyright by Jeffrey Jiang 2024  \nABSTRACT OF THE DISSERTATION  \nOn robust estimation in causal machine learning  \nby  \nJeffrey Jiang  \nDoctor of Philosophy in Electrical and Computer Engineering University of California, Los Angeles, 2024  \nProfessor Gregory Pottie, Chair  \nThis thesis presents three significant contributions to the field of machine learning, with a focus on Variational Autoencoders (VAEs), energy-based models, and education simulations. Firstly, we demonstrate the ability to impose substantial structure on the latent space of VAEs, enabling out-of-distribution data generation, structural hypothesis testing, and the production of augmentations in the latent space. These findings give us new ways to structure and interpret the latent space, creating robustness and explainability. Secondly, we identify a state-of-the-art defense technique using the unsupervised learning approach of energy-based models. This technique effectively defends against several poisoning techniques without requiring excessive additional training time or significantly reducing test accuracy. Lastly, we have developed a simulation for educational purposes that aims to model and comprehend the interactions between humans and machines. This simulation, built on causal information, provides insights into the design of practical educational experiments and highlights the challenges associated with implementing a dynamic Intelligent Tutoring System (ITS) in an educational context. Interestingly, our simulation reveals that heuristic methods continue to perform on par with deep learning techniques in the presence of unknown subpopulation  \ndistributions and hidden student states. This suggests that despite the rapid advancementsin deep learning, heuristic methods retain their effectiveness in certain scenarios. These findings open new avenues for the application of machine learning techniques and provide a solid foundation for future research in these areas.  \nThe dissertation of Jeffrey Jiang is approved.  \nLieven Vandenberghe Suhas Diggavi Xiang ‘Anthony’ Chen Gregory Pottie, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTo my hardworking parents, Xueping and Weijun, my amazing sister, Jessica, and my loving wife, Ramya.  \nv  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Background ...................................... 3  \n2.1 Probabilistic Graphical Models ......................... 3  \n2.1.1 Conditional Probability and Bayes’ Rule ................ 3  \n2.1.2 Conditional Independence and Bayesian Networks ........... 4  \n2.1.3 Hidden Markov Models .......................... 6  \n2.2 Causality and Causal Modeling ......................... 7  \n2.3 Deep Learning ................................... 9  \n2.3.1 Supervised and Unsupervised Learning ................. 10  \n2.3.2 Reinforcement Learning ......................... 11  \n2.4 Generative Models ................................ 14  \n2.4.1 Variational Autoencoders (VAEs) .................... 14  \n2.4.2 Conditional VAE (CVAE) ........................ 15  \n3 Games ......................................... 17  \n3.1 Motivation ..................................... 17  \n3.2 Problem Formulation ............................... 18  \n3.3 Formal Problem Statement ............................ 19  \n3","cbCaiopiOehmo6YD","https://ap.wps.com/l/cbCaiopiOehmo6YD","pdf",12219127,1,228,"English","en",105,"# 1 Introduction\n# 2 Background\n## 2.1 Probabilistic Graphical Models\n## 2.2 Causality and Causal Modeling\n## 2.3 Deep Learning\n## 2.4 Generative Models\n# 3 Games\n## 3.1 Motivation\n## 3.2 Problem Formulation\n## 3.3 Formal Problem Statement\n## 3.4 Liar’s Poker\n## 3.5 Rock Paper Scissors\n## 3.6 Collaborative Game-Sets\n# 4 De-Biasing Generative Models using Counterfactual Methods\n## 4.1 Introduction\n## 4.2 Related Work\n## 4.3 Background\n## 4.4 Problem Setting\n## 4.5 Causal Counterfactual Generative Model\n## 4.6 Experiments\n## 4.7 Conclusion\n# 5 Causal Structural Hypothesis Testing and Data Generation Models","[{\"question\":\"What are the main contributions of the thesis on robust estimation in causal machine learning?\",\"answer\":\"The thesis presents three contributions: structured latent-space learning for VAEs (including out-of-distribution generation and hypothesis testing), an energy-based unsupervised defense against poisoning attacks, and a causal simulation for educational human-machine interactions.\"},{\"question\":\"How does the thesis improve VAEs for robust, explainable results?\",\"answer\":\"It demonstrates imposing substantial structure on the latent space of VAEs, enabling out-of-distribution data generation, structural hypothesis testing, and latent-space augmentations that support robustness and interpretability.\"},{\"question\":\"How does the proposed defense using energy-based models help against poisoning attacks?\",\"answer\":\"The defense uses an unsupervised energy-based learning approach to defend effectively against several poisoning techniques while avoiding excessive additional training time and without significantly reducing test accuracy.\"}]","On robust estimation in causal machine learning - 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