- Remote Access
- Virtual Presentation - ET
- BEN MOSELEY
- Carnegie Bosch Associate Professor of Operations Research
- Tepper School of Business
- Carnegie Mellon University
Machine Learning for Scheduling
This talk will discuss a model for augmenting algorithms with useful predictions that go beyond worst-case bounds on the algorithm performance. The model ensures predictions are formally learnable and instance robust. Learnability guarantees that predictions can be efficiently constructed from past data. Instance robustness formally ensures a prediction is robust to modest changes in the problem input. This talk will discuss predictions that satisfy these properties for scheduling and resource augmentation. Algorithms developed breakthrough worst-case barriers with accurate predictions and have a graceful degradation in performance when the error in the predictions grows.
Ben Moseley is the Carnegie Bosch Associate Professor of Operations Research in the Tepper School of Business at Carnegie Mellon University (CMU). He is an Assistant Professor of Machine Learning in the School of Computer Science (by courtesy) and member of the Ph.D. program Algorithms, Combinatorics and Optimization (ACO). He received a Ph.D. from the University of Illinois in Computer Science and was advised by Chandra Chekuri.
Professor Moseley's research interests are broadly in operations research, theoretical computer science and machine learning. He works on the design, analysis and evaluation of algorithms. He is currently working on the algorithmic foundations of machine learning, big data analysis (e.g. relational in-database algorithms, distributed algorithm design, and streaming), and algorithms for scheduling and logistics. Learn more...
The objective of a virtual seminar on scheduling research and applications is to discuss both the field's newest advancements and survey traditional areas. Seminars take place typically on every second Wednesday through three different time zones (Europe, the Middle East & Africa, North America & South America, and Asia, Australia & Oceania).