Emmanouil-Vasileios Vlatakis-Gkaragkounis

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Georgios PiliourasGoogle DeepMind, Singapore University of Technology and DesignVerified email at google.com
Panayotis MertikopoulosCNRSVerified email at imag.fr
Lampros FlokasPhD Student, Columbia UniversityVerified email at cs.columbia.edu
Angeliki GiannouUniversity of Wisconsin–MadisonVerified email at wisc.edu
Fivos KalogiannisPhD student, UCSDVerified email at ucsd.edu
Ioannis PanageasAssistant Professor, University of California, IrvineVerified email at ics.uci.edu
Thanasis LianeasVerified email at corelab.ntua.gr
Rocco Anthony ServedioDepartment of Computer Science, Columbia UniversityVerified email at columbia.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Clayton SanfordGoogle ResearchVerified email at google.com
Ioannis AnagnostidesPhD student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Chenghao GuoMassachusetts Institute of TechnologyVerified email at mit.edu
Mihalis YannakakisPercy K. and Vida L. W. Hudson Professor of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Xi ChenColumbia UniversityVerified email at cs.columbia.edu
Xinzhi ZhangUniversity of WashingtonVerified email at uw.edu
Alexandros PotamianosNational Technical University of AthensVerified email at central.ntua.gr
Georgios ParaskevopoulosAssociate Researcher, Institute for Speech and Language Processing, Athena RCVerified email at central.ntua.gr
Efthymios TzinisSr. Research Scientist at Google | Ex. UIUC, MERL, MetaVerified email at google.com
Anindya DeUniversity of PennsylvaniaVerified email at seas.upenn.edu

Emmanouil-Vasileios Vlatakis-Gkaragkounis

Assistant Professor, University of Wisconsin-Madison

Verified email at wisc.edu - Homepage

Theoretical Computer Science Game Theory Optimization Learning Theory


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No-regret learning and mixed nash equilibria: They do not mix EV Vlatakis-Gkaragkounis, L Flokas, T Lianeas, P Mertikopoulos, ... Advances in Neural Information Processing Systems 33, 1380-1391, 2020	91*	2020
Poincaré recurrence, cycles and spurious equilibria in gradient-descent-ascent for non-convex non-concave zero-sum games EV Vlatakis-Gkaragkounis, L Flokas, G Piliouras Advances in Neural Information Processing Systems 32, 2019	66	2019
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information A Giannou, EV Vlatakis-Gkaragkounis, P Mertikopoulos Conference on Learning Theory, 2147-2148, 2021	39	2021
Efficiently avoiding saddle points with zero order methods: No gradients required EV Vlatakis-Gkaragkounis, L Flokas, G Piliouras Advances in neural information processing systems 32, 2019	36*	2019
On the approximation power of two-layer networks of random relus D Hsu, CH Sanford, R Servedio, EV Vlatakis-Gkaragkounis Conference on Learning Theory, 2423-2461, 2021	32	2021
Contracting with a learning agent G Guruganesh, Y Kolumbus, J Schneider, I Talgam-Cohen, ... Advances in Neural Information Processing Systems 37, 77366-77408, 2024	31	2024
On the rate of convergence of regularized learning in games: From bandits and uncertainty to optimism and beyond A Giannou, EV Vlatakis-Gkaragkounis, P Mertikopoulos Advances in Neural Information Processing Systems 34, 22655-22666, 2021	30	2021
Smoothed complexity of local Max-Cut and binary Max-CSP X Chen, C Guo, EV Vlatakis-Gkaragkounis, M Yannakakis, X Zhang Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020	25	2020
Optimal private median estimation under minimal distributional assumptions C Tzamos, EV Vlatakis-Gkaragkounis, I Zadik Advances in Neural Information Processing Systems 33, 3301-3311, 2020	23	2020
On the convergence of policy gradient methods to Nash equilibria in general stochastic games A Giannou, K Lotidis, P Mertikopoulos, EV Vlatakis-Gkaragkounis Advances in Neural Information Processing Systems 35, 7128-7141, 2022	22	2022
Efficiently computing nash equilibria in adversarial team markov games F Kalogiannis, I Anagnostides, I Panageas, EV Vlatakis-Gkaragkounis, ... arXiv preprint arXiv:2208.02204, 2022	22	2022
Solving min-max optimization with hidden structure via gradient descent ascent EV Vlatakis-Gkaragkounis, L Flokas, G Piliouras Advances in Neural Information Processing Systems 34, 2373-2386, 2021	22	2021
First-order algorithms for min-max optimization in geodesic metric spaces M Jordan, T Lin, EV Vlatakis-Gkaragkounis Advances in Neural Information Processing Systems 35, 6557-6574, 2022	20	2022
Algorithms and complexity for computing nash equilibria in adversarial team games I Anagnostides, F Kalogiannis, I Panageas, EV Vlatakis-Gkaragkounis, ... arXiv preprint arXiv:2301.02129, 2023	14	2023
The computational complexity of multi-player concave games and Kakutani fixed points CH Papadimitriou, EV Vlatakis-Gkaragkounis, M Zampetakis arXiv preprint arXiv:2207.07557, 2022	11	2022
Near-optimal statistical query lower bounds for agnostically learning intersections of halfspaces with gaussian marginals DJ Hsu, CH Sanford, R Servedio, EV Vlatakis-Gkaragkounis Conference on Learning Theory, 283-312, 2022	10	2022
Towards convergence to Nash equilibria in two-team zero-sum games F Kalogiannis, I Panageas, EV Vlatakis-Gkaragkounis arXiv preprint arXiv:2111.04178, 2021	7	2021
Chaos persists in large-scale multi-agent learning despite adaptive learning rates EV Vlatakis-Gkaragkounis, L Flokas, G Piliouras arXiv preprint arXiv:2306.01032, 2023	6	2023
Teamwork makes von neumann work: Min-max optimization in two-team zero-sum games F Kalogiannis, I Panageas, EV Vlatakis-Gkaragkounis	6	2021
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium I Sakos, EV Vlatakis-Gkaragkounis, P Mertikopoulos, G Piliouras Advances in Neural Information Processing Systems 36, 66979-67006, 2023	5	2023

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