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Aryan Mokhtari

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Asuman E. Ozdaglar

> Home > Persons > Aryan Mokhtari

Publications

2021
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Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman E. Ozdaglar:
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. NeurIPS 2021: 3096-3107
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Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks. NeurIPS 2021: 5469-5480
[i46]
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  - journals/corr/abs-2102-03832
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Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks. CoRR abs/2102.03832 (2021)
2020
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Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil:
Convergence Rate of 풪(1/k) for Optimistic Gradient and Extragradient Methods in Smooth Convex-Concave Saddle Point Problems. SIAM J. Optim. 30(4): 3230-3251 (2020)
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Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. AISTATS 2020: 1082-1092
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Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil:
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. AISTATS 2020: 1497-1507
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Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. NeurIPS 2020
[i41]
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  - journals/corr/abs-2002-05135
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Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning. CoRR abs/2002.05135 (2020)
[i40]
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Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Personalized Federated Learning: A Meta-Learning Approach. CoRR abs/2002.07948 (2020)
2019
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Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie:
Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods. AISTATS 2019: 2485-2494
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  - journals/corr/abs-1901-08511
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Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil:
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. CoRR abs/1901.08511 (2019)
[i29]
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  - journals/corr/abs-1906-01115
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Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil:
Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods. CoRR abs/1906.01115 (2019)
[i27]
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  - journals/corr/abs-1908-10400
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Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. CoRR abs/1908.10400 (2019)
[i24]
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  - journals/corr/abs-1910-14380
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Weijie Liu, Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil, Zebang Shen, Nenggan Zheng:
A Decentralized Proximal Point-type Method for Saddle Point Problems. CoRR abs/1910.14380 (2019)
2018
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Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie:
Escaping Saddle Points in Constrained Optimization. NeurIPS 2018: 3633-3643
[i19]
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  - journals/corr/abs-1809-02162
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Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie:
Escaping Saddle Points in Constrained Optimization. CoRR abs/1809.02162 (2018)

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