Withdraw
Loading…
On sparse mirror descent
Guha, Shovik
Loading…
Permalink
https://hdl.handle.net/2142/115797
Description
- Title
- On sparse mirror descent
- Author(s)
- Guha, Shovik
- Issue Date
- 2022-04-29
- Director of Research (if dissertation) or Advisor (if thesis)
- Koyejo, Oluwasanmi
- Department of Study
- Computer Science
- Discipline
- Computer Science
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Keyword(s)
- Machine Learning
- Optimization
- Algorithms
- Mirror Descent, Sparse Optimization
- Abstract
- Parsimony is a general guiding principle in science and philosophy which suggests that if one has multiple theories fitting the data equally well, one should choose the ``simplest" theory. In the field of machine learning and artificial intelligence, the sparsity of a model is used as a measure of parsimony. Algorithms which produce an optimal set of sparse parameters for a given model have been notoriously difficult to construct due to the non-convex and combinatorial nature of sparsity constraints. In this thesis we begin by giving an overview of popular algorithms for sparse and convex optimization. We then show how they can be combined with classical tools from the theory of approximation algorithms to compute approximate projections onto the sparsity constraints, which ultimately leads to a novel algorithm for sparse optimization.
- Graduation Semester
- 2022-05
- Type of Resource
- Thesis
- Copyright and License Information
- Copyright 2022 Shovik Guha
Owning Collections
Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisManage Files
Loading…
Edit Collection Membership
Loading…
Edit Metadata
Loading…
Edit Properties
Loading…
Embargoes
Loading…