Artikel mit Open-Access-Mandaten - Asuman OzdaglarWeitere Informationen
Nicht verfügbar: 2
Learning in repeated stochastic network aggregative games
E Meigs, F Parise, A Ozdaglar
2019 IEEE 58th Conference on Decision and Control (CDC), 6918-6923, 2019
Mandate: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Interconnection and Memory in Linear Time-Invariant Systems
EM Adam, MA Dahleh, A Ozdaglar
IEEE Transactions on Automatic Control 64 (5), 1890-1904, 2018
Mandate: US National Science Foundation
Verfügbar: 48
The network origins of aggregate fluctuations
D Acemoglu, VM Carvalho, A Ozdaglar, A Tahbaz‐Salehi
Econometrica 80 (5), 1977-2016, 2012
Mandate: Government of Spain
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
A Fallah, A Mokhtari, A Ozdaglar
Advances in Neural Information Processing Systems 33, 2020
Mandate: US National Science Foundation, US Department of Defense
Opinion fluctuations and disagreement in social networks
D Acemoğlu, G Como, F Fagnani, A Ozdaglar
Mathematics of Operations Research 38 (1), 1-27, 2013
Mandate: Swedish Research Council
Microeconomic origins of macroeconomic tail risks
D Acemoglu, A Ozdaglar, A Tahbaz-Salehi
American Economic Review 107 (1), 54-108, 2017
Mandate: US Department of Defense
On the convergence theory of gradient-based model-agnostic meta-learning algorithms
A Fallah, A Mokhtari, A Ozdaglar
International Conference on Artificial Intelligence and Statistics, 1082-1092, 2020
Mandate: US Department of Defense
Convergence rate of distributed ADMM over networks
A Makhdoumi, A Ozdaglar
IEEE Transactions on Automatic Control 62 (10), 5082-5095, 2017
Mandate: US Department of Defense
Why random reshuffling beats stochastic gradient descent
M Gürbüzbalaban, A Ozdaglar, P Pablo
Mathematical Programming 186 (1), 49-84, 2021
Mandate: US National Science Foundation
Gans may have no nash equilibria
F Farnia, A Ozdaglar
arXiv preprint arXiv:2002.09124, 2020
Mandate: US Department of Defense
Informational Braess’ paradox: The effect of information on traffic congestion
D Acemoglu, A Makhdoumi, A Malekian, A Ozdaglar
Operations Research 66 (4), 893-917, 2018
Mandate: US National Science Foundation
On the convergence rate of incremental aggregated gradient algorithms
M Gurbuzbalaban, A Ozdaglar, PA Parrilo
SIAM Journal on Optimization 27 (2), 1035-1048, 2017
Mandate: US Department of Defense
Competition in electricity markets with renewable energy sources
D Acemoglu, A Kakhbod, A Ozdaglar
The Energy Journal 38 (1_suppl), 137-156, 2017
Mandate: US National Science Foundation
Graphon games: A statistical framework for network games and interventions
F Parise, A Ozdaglar
Econometrica 91 (1), 191-225, 2023
Mandate: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung …
Learning from reviews: The selection effect and the speed of learning
D Acemoglu, A Makhdoumi, A Malekian, A Ozdaglar
Econometrica 90 (6), 2857-2899, 2022
Mandate: US Department of Defense
Competitive targeted advertising over networks
K Bimpikis, A Ozdaglar, E Yildiz
Operations Research 64 (3), 705-720, 2016
Mandate: US National Science Foundation
Last iterate is slower than averaged iterate in smooth convex-concave saddle point problems
N Golowich, S Pattathil, C Daskalakis, A Ozdaglar
Conference on Learning Theory, 1758-1784, 2020
Mandate: US National Science Foundation, US Department of Energy, US Department of …
Decentralized Q-learning in zero-sum Markov games
M Sayin, K Zhang, D Leslie, T Basar, A Ozdaglar
Advances in Neural Information Processing Systems 34, 18320-18334, 2021
Mandate: US Department of Defense
A variational inequality framework for network games: Existence, uniqueness, convergence and sensitivity analysis
F Parise, A Ozdaglar
Games and Economic Behavior 114, 47-82, 2019
Mandate: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung …
Testing, voluntary social distancing, and the spread of an infection
D Acemoglu, A Makhdoumi, A Malekian, A Ozdaglar
Operations Research 72 (2), 533-548, 2024
Mandate: US National Science Foundation
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