Alon, N., & Spencer, J. H. (2016). The probabilistic method (Fourth edition. ed.). Wiley.
Vazirani, V. V. (2001). Approximation algorithms. Berlin ; New York, Springer.
Lovász, L. s. and M. D. Plummer (1986). Matching theory. Amsterdam ; New York New York, N.Y., North-Holland.
Nisan, N. (2007). Algorithmic game theory. Cambridge University Press.
Echenique, F., N. Immorlica and V. V. Vazirani (2023). Online and matching-based market design. Cambridge, Cambridge University Press.
Blum, A., J. E. Hopcroft and R. Kannan (2020). Foundations of data science. New York, NY, Cambridge University Press.
TTIC 31260 - Algorithmic Game Theory (Spring 2024)
TTIC 31150/CMSC 31150 - Mathematical Toolkit (Spring 2023)
CMU - Foundations of Machine Learning and Data Science
COURSE: CS 15-892 Foundations of Electronic Marketplaces
Artificial Intelligence: Representation and Problem Solving
ACM Conference - Economics and Computation
ACM Transactions on Economics and Computation
International Symposium on Algorithmic Game Theory
ACM-SIAM Symposium on Discrete Algorithms (SODA)
Vijay V. Vazirani’s publication
Robert Schapire’s publication list
Games and Equilibria in System Design and Analysis, Simons Institute workshop
Advanced Combinatorial Optimization course by Michel Goemans
Satoru Iwata’s lectures and publications
Yasushi Kawase’s publications and teaching
Ben Zhao’s Webpage (adversarial machine learning) , Sand Lab(Security, Algorithms, Networking and Data)