Ioannis Panageas

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Georgios PiliourasSingapore University of Technology and DesignVerified email at sutd.edu.sg
Constantinos DaskalakisProfessor of Computer Science, MITVerified email at csail.mit.edu
Ruta MehtaDept. of Computer Science, University of Illinois at Urbana-ChampaignVerified email at cs.illinois.edu
Sai Ganesh NagarajanPostdoctoral Researcher, EPFLVerified email at epfl.ch
Xiao WangShanghai University of Finance and EconomicsVerified email at sufe.edu.cn
Vijay VaziraniDistinguished Professor, Dept. of Computer Science, University of California, Irvine.Verified email at ics.uci.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Vaggos ChatziafratisUniversity of California, Santa CruzVerified email at cs.stanford.edu
Ioannis AnagnostidesPhD student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Stefanos LeonardosLecturer in machine learning, King's College LondonVerified email at kcl.ac.uk
Prasad TetaliHead, Department of Mathematical Sciences, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Stephen McAleerPostdoc, CMUVerified email at uci.edu
Tuomas SandholmAngel Jordan University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Gabriele FarinaCarnegie Mellon UniversityVerified email at cs.cmu.edu
Fivos KalogiannisPhD student, University of California, IrvineVerified email at uci.edu
Roy FoxAssistant Professor, UC IrvineVerified email at uci.edu
Yong-Dian JianVerified email at gatech.edu
Arnab BhattacharyyaNational University of SingaporeVerified email at nus.edu.sg

Ioannis Panageas

Assistant Professor, University of California, Irvine

Verified email at ics.uci.edu - Homepage

Algorithms Optimization Learning in Games Multi-agent RL Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
First-order methods almost always avoid strict saddle points JD Lee, I Panageas, G Piliouras, M Simchowitz, MI Jordan, B Recht Mathematical programming 176 (1-2), 311-337, 2019	291	2019
The limit points of (optimistic) gradient descent in min-max optimization C Daskalakis, I Panageas Advances in Neural Information Processing Systems 31, 9236-9246, 2018	243	2018
Gradient descent only converges to minimizers: Non-isolated critical points and invariant regions I Panageas, G Piliouras Proceedings of the Conference on Innovations in Theoretical Computer Science, 2017	166*	2017
Last-iterate convergence: Zero-sum games and constrained min-max optimization C Daskalakis, I Panageas Proceedings of the Conference on Innovations in Theoretical Computer Science, 2019	126	2019
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos G Palaiopanos, I Panageas, G Piliouras 31st Annual Conference on Neural Information Processing Systems, 2017	109	2017
Global convergence of multi-agent policy gradient in markov potential games S Leonardos, W Overman, I Panageas, G Piliouras arXiv preprint arXiv:2106.01969, 2021	61	2021
Natural selection as an inhibitor of genetic diversity: Multiplicative weights updates algorithm and a conjecture of haploid genetics [working paper abstract] R Mehta, I Panageas, G Piliouras Proceedings of the 2015 Conference on Innovations in Theoretical Computer …, 2015	58	2015
First-order methods almost always avoid saddle points: The case of vanishing step-sizes I Panageas, G Piliouras, X Wang Advances in Neural Information Processing Systems 32, 2019	44	2019
Average case performance of replicator dynamics in potential games via computing regions of attraction I Panageas, G Piliouras Proceedings of the 2016 ACM Conference on Economics and Computation, 703-720, 2016	38*	2016
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes Q Lei, SG Nagarajan, I Panageas, X Wang International Conference on Artificial Intelligence and Statistics, 1441-1449, 2021	31	2021
Independent natural policy gradient always converges in Markov potential games R Fox, SM Mcaleer, W Overman, I Panageas International Conference on Artificial Intelligence and Statistics, 4414-4425, 2022	28	2022
Depth-width trade-offs for relu networks via sharkovsky's theorem V Chatziafratis, SG Nagarajan, I Panageas, X Wang arXiv preprint arXiv:1912.04378, 2019	28	2019
Regression from dependent observations C Daskalakis, N Dikkala, I Panageas Proceedings of the 51st Annual ACM symposium on Theory of Computing, 2019	26	2019
Cycles in zero-sum differential games and biological diversity T Mai, M Mihail, I Panageas, W Ratcliff, V Vazirani, P Yunker Proceedings of the 2018 ACM Conference on Economics and Computation, 339-350, 2018	18*	2018
Mutation, sexual reproduction and survival in dynamic environments R Mehta, I Panageas, G Piliouras, P Tetali, VV Vazirani Proceedings of the 2017 Conference on Innovations in Theoretical Computer …, 2015	17*	2015
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always I Panageas, G Piliouras, X Wang Proceedings of the 36th International Conference on Machine Learning, 4961-4969, 2019	16	2019
On last-iterate convergence beyond zero-sum games I Anagnostides, I Panageas, G Farina, T Sandholm International Conference on Machine Learning, 536-581, 2022	13	2022
Fast convergence of langevin dynamics on manifold: Geodesics meet log-sobolev X Wang, Q Lei, I Panageas Advances in Neural Information Processing Systems 33, 18894-18904, 2020	13	2020
The computational complexity of genetic diversity R Mehta, I Panageas, G Piliouras, S Yazdanbod 24th Annual European Symposium on Algorithms (ESA 2016), 2016	13*	2016
Better depth-width trade-offs for neural networks through the lens of dynamical systems V Chatziafratis, SG Nagarajan, I Panageas International Conference on Machine Learning, 1469-1478, 2020	11	2020

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