CS 7301: Spring 2021 Course on Advanced Topics in Optimization for Machine Learning

Video Lectures are on this youtube playlist: https://www.youtube.com/playlist?list=PLGod0_zT9w92_evaYrf3-rE67AmgPJoUU

https://github.com/rishabhk108/OptimizationDemos

https://docs.google.com/spreadsheets/d/1UHHFlo_8QAvmXjWqoU02Calq86S-ewYl7Jczjhgr0wY/edit?usp=sharing

Deadline for finalizing on the papers to cover: February 26th

Deadine for finalizing on the project topic: March 5th

• Week 1
  • Logistics, Outline of this Course
  • Continuous Optimization in ML
  • Convex Sets and Basics of Convexity
• Week 2: Gradient Descent and Family
  • Convex Functions, Properties, Minima, Subgradients
  • Gradient Descent and Line Search
• Week 3: Gradient Descent Cont.
  • Accelerated Gradient Descent
  • Projected and Proximal Gradient Descent
• Week 4
  • Projected GD and Conditional GD (Constrained Case)
  • Second Order Methods (Newton, Quasi-Newton, BFGS, LBFGS)
• Week 5
  • Second Order Methods Completed
  • Barzelia Borwein and Conjugate GD
  • Coordinate Descent Family
• Week 6
  • Stochastic Gradient and Family (SGD, SVRG)
  • SGD for Non-Convex Optimization. Modern variants of SGD particularly for deep learning (e.g. Adagrad, Adam, AdaDelta, RMSProp, Momentum etc.)
• Week 7
  • Submodular Optimization: Basics, Definitions, Properties, and Examples.
• Week 8
  • Submodular Information Measures: Conditional Gain, Submodular Mutual Information, Submodular Span, Submodular Multi-Set Mutual Information
• Week 9
  • Submodular Minimization and Continuous Extensions of Submodular Functions. Submodular Minimization under constraints
• Week 10
  • Submodular Maximization Variants, Submodular Set Cover, Approximate submodularity. Algorithms under different constraints and monotone/non-monotone settings. Also, distributed and streaming algorithms, DS Optimization, Submodular Optimization under Submodular Constraints
• Week 11
  • Applications of Discrete Optimization: Data Subset Selection, Data Summarization, Feature Selection, Active Learning etc.
• Rest of the Weeks
  • Paper Presentations/Project Presentations by the Students

• 10% for Class Participation (Interaction, asking questions, answering questions)
• 30% Assignments (2 Assignments, one on continuous optimization and one on discrete optimization)
• 30% Paper Presentations (1-2 papers per student)
• 30% for the Final Project
  • Take a new dataset/problem and study how existing optimization algorithms work on them
  • Take an existing problem and compare all optimization algorithms with your implementation from scratch
  • Design a ML optimization toolkit with algorithms implemented from scratch -- if one of you would like to extend my current python demos for optimization, that will be an awesome contribution and I might pick it up for my future classes and acknowledge you :)

• Convex Optimization: https://blogs.princeton.edu/imabandit/orf523-the-complexities-of-optimization/
• Convex Optimization: http://www.cs.cmu.edu/~pradeepr/convexopt/
• Convex Optimization: https://ee227c.github.io/
• Convex Optimization: https://github.com/epfml/OptML_course
• Convex Optimization: https://www.cs.ubc.ca/~schmidtm/Courses/LecturesOnML/
• Combinatorial Optimization: http://www-math.mit.edu/~goemans/18433S15/18433.html
• Subodular Optimization: https://people.ece.uw.edu/bilmes/classes/ee563_spring_2018/

• Martin Jaggi's EPFL Course notes on Convex Optimization: https://github.com/epfml/OptML_course/blob/master/lecture_notes/lecture-notes.pdf
• Sebastian Bubeck's Monograph on Convex Optimization: https://arxiv.org/pdf/1405.4980.pdf
• Submodular Optimization Survey: http://www.bioinfo.org.cn/~dbu/AlgorithmCourses/Lectures/Lec7-SubModular-Golovin.pdf
• Francis Bach's Monograph on Submodular Optimizationn and Convexity: https://arxiv.org/pdf/1111.6453.pdf
• Online convex optimization by Elad Hazan: https://sites.google.com/view/intro-oco/