Schedule
1) (Mar 15) An introductory lecture
2) (Mar 22) C. 2 - Parameterized
Algorithms, Fedor V. Fomin,
Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi, Roni Zehavi
3) (Mar 29) C. 3 - From
Adaptive Analysis to Instance Optimality, Jérémy Barbay, Moria Nachmany
4) (April 19) C. 4 - Resource
Augmentation, Tim Roughgarden, Amit Sandler
5) (May 3) C. 8 - Distributional
Analysis, Tim Roughgarden, Yuval Shem-Tov
6) (May 10ŕFriday May 5) C. 11 - Random-Order
Models, Anupam Gupta, Sahil
Singla, Alon Alexander
7) (May 17) C. 12 - Self-Improving
Algorithms, C. Seshadhri, Inbal Hadad
8) (May 24) C. 15 -
Smoothed
Analysis of Pareto Curves in Multiobjective
Optimization, Heiko Röglin,
Tommy
Winewtraub
9)
(May 31
and June 7) Correlation
clustering and robust
online correlation clustering, Omer Azoulay
10) (June 14) Online Steiner tree and generalized
steiner tree (classical worst case model), Dean Oren
11) (June 21) Online scheduling via learned weights, Nir Shalmon
12) (June 28) Learning
from a sample in online algorithms (note the supplementary material), Dana Cohen