Learning to represent and reason under limited supervision
Abstract/Contents
- Abstract
- Natural agents, such as humans, excel at building representations of the world and using them to effectively draw inferences and make decisions. Critically, the development of such advanced reasoning capabilities can occur even with limited supervision. In stark contrast, the major successes of machine learning (ML)-based artificial agents are primarily in tasks that have access to large labelled datasets or simulators, such as object recognition and game playing. This dissertation focuses on probabilistic modeling frameworks that shrink this gap between natural and artificial agents and thus enable effective reasoning in supervision constrained scenarios. This dissertation comprises of three parts. First, we formally lay the foundations for learning probabilistic generative models. The goal here is to simulate any available data, thus providing a natural learning objective even in settings with limited supervision. We discuss various trade-offs involved in learning and inference in high dimensions using these models, including the specific choice of learning objective, optimization procedure, and parametric model family. Building on these insights, we develop new algorithms to boost the performance of state-of-the-art models and mitigate their biases when trained on large uncurated and unlabelled datasets. Second, we extend these models to learn feature representations for relational data. Learning these representations is unsupervised, and we demonstrate their utility for classification and sequential decision making. Third and last, we present two real-world applications of these models for accelerating scientific discovery in: (a) learning data dependent priors for compressed sensing, and (b) design of experiments for optimizing charging in electric batteries. Together, these contributions enable ML systems to overcome the critical supervision bottleneck for high-dimensional inference and decision making problems in the real world.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Grover, Aditya |
|---|---|
| Degree supervisor | Ermon, Stefano |
| Thesis advisor | Ermon, Stefano |
| Thesis advisor | Charikar, Moses |
| Thesis advisor | Horvitz, Eric J. (Eric Joel) |
| Thesis advisor | Leskovec, Jurij |
| Degree committee member | Charikar, Moses |
| Degree committee member | Horvitz, Eric J. (Eric Joel) |
| Degree committee member | Leskovec, Jurij |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Aditya Grover. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Aditya Grover
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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