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
Vijay VaziraniDistinguished Professor, Dept. of Computer Science, University of California, Irvine.Verified email at ics.uci.edu
Jason D. LeeAssistant Professor of Electrical Engineering, 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
Prasad TetaliHead, Department of Mathematical Sciences, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Nishanth DikkalaGoogle ResearchVerified email at google.com
Vaggos ChatziafratisStanford UniversityVerified email at cs.stanford.edu
Ioannis AnagnostidesPhD student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Yong-Dian JianVerified email at gatech.edu
Stefanos LeonardosResearch Fellow, Singapore University of Technology & DesignVerified email at sutd.edu.sg
Roy FoxAssistant Professor, UC IrvineVerified email at uci.edu
Stephen McAleerPostdoc, CMUVerified email at uci.edu
Tuomas SandholmAngel Jordan University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Arnab BhattacharyyaNational University of SingaporeVerified email at nus.edu.sg
Stratis SkoulakisSingapore University of Technology and DesignVerified email at sutd.edu.sg

Ioannis Panageas

Assistant Professor, University of California, Irvine

Verified email at ics.uci.edu - Homepage

Algorithms Machine Learning Optimization Probability 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	237	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	180	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	144*	2017
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	88	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	85	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	55	2015
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	36*	2016
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	27	2019
Regression from dependent observations C Daskalakis, N Dikkala, I Panageas Proceedings of the 51st Annual ACM symposium on Theory of Computing, 2019	24	2019
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	18	2021
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	18	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	16*	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	16*	2015
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
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	11	2019
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	10	2021
Logistic regression with peer-group effects via inference in higher-order Ising models C Daskalakis, N Dikkala, I Panageas International Conference on Artificial Intelligence and Statistics, 3653-3663, 2020	10	2020
Support-theoretic subgraph preconditioners for large-scale SLAM YD Jian, D Balcan, I Panageas, P Tetali, F Dellaert 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 9-16, 2013	10	2013
Opinion dynamics in networks: Convergence, stability and lack of explosion T Mai, I Panageas, VV Vazirani Proceedings of the 44th International Colloquium on Automata, Languages, and …, 2016	8	2016
Evolutionary dynamics in finite populations mix rapidly I Panageas, P Srivastava, NK Vishnoi Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete …, 2016	8	2016

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