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ML-Guided Algorithms for Online Decision Making
ML-Guided Algorithms for Online Decision Making
Members of the Theory Group use mathematical techniques to study problems throughout computer science. Our work is extremely diverse -- it includes graph and network algorithms, randomized approximation algorithms, streaming algorithms, combinatorial optimization, computational geometry, dynamic algorithms and complexity, model checking and static analysis, database theory, descriptive complexity, parallel algorithms, and computational complexity theory. Members of the Theory Group wear other hats as well and collaborate throughout the department and the world beyond. Every week we all get together for a research seminar, featuring either one of our own professors or Ph.D. students, or a visitor from another institution. You can find more about the group and our weekly seminars at theory.cs.umass.edu/seminar
Project Description: TBD
Learning Objectives:
- Students should be able to read academic papers in the area of theoretical computer science and understand their main objectives and contributions.
- Students should understand basic techniques and ideas used in algorithms for online decision making. E.g., they should understand the idea of competitive ratio, and be able to analyze the classic 1/e competitive algorithm for the secretary problem. They should know about related problems like profit inequalities, online matching, etc.
- Students should be able to formally describe an online algorithm in pseudocode and attempt to analyze the competitive ratio of that algorithm.
Skills needed:
- Strong background in probability, and basic background in calculus.
- Ability to write well and describe their ideas clearly. Both in plain English and in pseudocode/mathematical notation.
- Basic coding ability to write simulations for algorithms.
- Ability to use AI tools (i.e., LLMs), to explore proof ideas, draft technical proofs, etc. And to verify and critically analyze the produced content.
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