Algorithms with predictions, also known as learning-augmented algorithms, is a growing field of research at the intersection of theoretical computer science and machine-learning. It looks to address the following question: How to use imperfect predictions in a robust way – retaining worst-case guarantees of classic algorithms – yet achieve optimal performance when the predictions are accurate? This one-day tutorial aims to serve as a gentle introduction to the area for theory researchers with any background willing to get into the area.
PhD School will take place on Monday, February 9th. This is a tentative schedule:
| 09:00–10:30 | Lecture: Online learning-augmented algorithms |
| 10:30–11:00 | Coffee break |
| 11:00–12:30 | Lecture: Warm-starting offline algorithms |
| 12:30–14:00 | Lunch break |
| 14:00–15:30 | Exercise session |
| 15:30–16:00 | Coffee break |
| 16:00–17:00 | Discussion of the exercises |
| 17:00–18:00 | Lecture: Open problems in algorithms with predictions |