Publications and Notes
Publications
Denizalp Goktas, Arjun Prakash, Amy Greenwald. (2023). “Convex-Concave Zero-Sum Stochastic Stackelberg Games.” To appear in proceedings of the Conference on Neural Information Processing Systems (NeurIPS'23).
Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, and Andrea Tacchetti. (2023). “Generative Adversarial Equilibrium Solvers." Invited talk at the Equilibrium Computation Workshop at the 24th ACM Conference on Economics and Computation (EC@EC'23).
Denizalp Goktas, Sadie Zhao, and Amy Greenwald. (2023). “Tâtonnement in Homothetic Fisher Markets." Proceedings of the 24th ACM Conference on Economics and Computation (EC'23).
Sadie Zhao, Denizalp Goktas, Amy Greenwald. (2023). “Fisher Markets with Social Influence." Proceedings of the Conference of the Association for the Advancement of Artificial Intelligence (AAAI'23).
Denizalp Goktas, Amy Greenwald. (2022). “Exploitability Minimization in Games and Beyond." Proceedings of the Conference on Neural Information Processing Systems (NeurIPS'22).
Denizalp Goktas, Sadie Zhao and Amy Greenwald. (2022), “Zero-Sum Stochastic Stackelberg Games.", The 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM'22), The ICLR Gamification and Multiagent Solutions Workshop (ICLR'22), Proceedings of the Conference on Neural Information Processing Systems (NeurIPS'22).
Denizalp Goktas. (2022). “An Algorithmic Theory of Markets and their Application to Decentralized Markets.", Proceedings of the Conference of the Association for the Advancement of Artificial Intelligence (AAAI'22).
Denizalp Goktas, Sadie Zhao and Amy Greenwald. (2022). “Robust No-Regret Learning in Min-Max Stackelberg Games.", The AAAI-22 Workshop on Adversarial Machine Learning and Beyond, Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems 2022 (AAMAS'22).
Denizalp Goktas and Amy Greenwald. (2022). “Gradient Descent Ascent in Min-Max Stackelberg Games.". Games, Agents, and Incentives Workshop 2022 at the International Conference on Autonomous Agents and Multi-Agent Systems 2022 (AAMAS'22).
Denizalp Goktas and Amy Greenwald. (2021), “Convex-Concave Min-Max Stackelberg Games.". Proceedings of Conference on Neural Information Processing Systems (NeurIPS'21).
Denizalp Goktas, Enrique Areyan Viqueira, and Amy Greenwald. (2021). “A Consumer-Theoretic Characterization of Fisher Market Equilibria.”. Contributed Poster in Twenty-Second ACM Conference on Economics and Computation (EC’21), Proceedings of the Conference on Web and Internet Economics (WINE’21).
Seth Goldstein, Denizalp Goktas, Miles Conn, Shanmukha Phani Teja Pitchuka, Mohammed Sameer, Maya Shah, HefeiTu ColinSwett, Shrinath Viswanathan, and Jessica Xiao. “BoLT: Building on Local Trust to Solve Lending Market Failure.” (2020). “BoLT: Building on Local Trust to Solve Lending Market Failure.". Mechanism Design for Social Good (MD4SG'20) - Additional information on project here!.
Notes
Throughout my time doing research I often struggled finding resources that connected recent advances in microeconomics, optimization, mathematics and computer science that were relevant to my field. As a result, to make sense of everything I was reading, I decided to come up with a set of notes for some of the most relevant topics. I hope they can be of help for anyone looking to get introduced to the field!
Topic Surveys
Introducion to Markets for Computer Scientists
Convex Analysis and Optimization for Econ-CS
Online Learning and Online Convex Optimization (Notes compiled together with Sadie Zhao)
Miscelaneous Notes
Walrassian Equilibria in Indivisible and Divisible Settings: Linear and Convex Programming Duality
Fair Divsion, Wagering and their Equivalence
Parimutuel Betting and Fisher Markets
Disclaimer: Some of these notes are not entirely finished or have been edited more than others. I am sharing them even if not complete since they definitely could help those entering the field.
Request for feedback: I always appreciate feedback and corrections from readers.