Machine Learning for Scheduling
Dear scheduling researcher,
We are delighted to announce the talk given by Benjamin Moseley (Carnegie Mellon).
The title is “Machine Learning for Scheduling”.
The seminar will take place on Zoom on Wednesday, October 27 at 13:00 UTC.
Meeting ID: 965 5357 0741
You can follow the seminar online or offline on our Youtube channel as well:
The abstract follows.
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 break through worst-case barriers with accurate predictions and have a graceful degradation in performance when the error in the predictions grows.
The next talk in our series will be given by
Carlo Mannino (SINTEF & Oslo Uni.) | November 10 | Train Scheduling: Models, decomposition methods and practice.
For more details, please visit https://schedulingseminar.com/
With kind regards
Zdenek, Mike and Guohua