Ioannis Panageas

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Georgios PiliourasGoogle DeepMind, Singapore University of Technology and DesignVerified email at google.com
Constantinos DaskalakisProfessor of Computer Science, MITVerified email at csail.mit.edu
Xiao WangShanghai University of Finance and EconomicsVerified email at sufe.edu.cn
Sai Ganesh NagarajanPostdoctoral Researcher, Zuse Institute BerlinVerified email at zib.de
Ioannis AnagnostidesPhD student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Ruta MehtaDept. of Computer Science, University of Illinois at Urbana-ChampaignVerified email at cs.illinois.edu
Fivos KalogiannisPhD student, UCSDVerified email at ucsd.edu
William OvermanPhD Student, Stanford GSBVerified email at stanford.edu
Vijay VaziraniDistinguished Professor, Dept. of Computer Science, University of California, Irvine.Verified email at ics.uci.edu
Stefanos LeonardosLecturer in machine learning, King's College LondonVerified email at kcl.ac.uk
Tuomas SandholmAngel Jordan University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Gabriele FarinaAssistant Professor of Computer Science, MITVerified email at mit.edu
Vaggos ChatziafratisUniversity of California, Santa CruzVerified email at cs.stanford.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 RechtProfessor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Emmanouil-Vasileios Vlatakis-Gkarag...PhD Candidate in Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Stephen McAleerOpenAIVerified email at openai.com
Prasad TetaliHead, Department of Mathematical Sciences, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Yi FengShanghai University of Finance and EconomicsVerified email at live.sufe.edu.cn

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	383	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	314	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	199*	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	192	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	144	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	141	2021
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	66	2019
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	62	2015
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	58	2022
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	52	2021
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	50*	2016
On last-iterate convergence beyond zero-sum games I Anagnostides, I Panageas, G Farina, T Sandholm International Conference on Machine Learning, 536-581, 2022	43	2022
Regression from dependent observations C Daskalakis, N Dikkala, I Panageas Proceedings of the 51st Annual ACM symposium on Theory of Computing, 2019	40	2019
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	31	2019
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	23	2020
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	23*	2018
On the Analysis of EM for truncated mixtures of two Gaussians SG Nagarajan, I Panageas Algorithmic Learning Theory, 634-659, 2020	22	2020
On the convergence of no-regret learning dynamics in time-varying games I Anagnostides, I Panageas, G Farina, T Sandholm Advances in Neural Information Processing Systems 36, 2024	21	2024
Efficiently computing nash equilibria in adversarial team markov games F Kalogiannis, I Anagnostides, I Panageas, EV Vlatakis-Gkaragkounis, ... arXiv preprint arXiv:2208.02204, 2022	20	2022
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	18	2020

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