Preprints
- Straggler-Resilient Personalized Federated Learning[pdf] I. Tziotis, Z. Shen, R. Pedarsani, H. Hassani, A. Mokhtari
- Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models [pdf] Q. Jin, T. Ren, N. Ho, A. Mokhtari
- Generalized Optimistic Methods for Convex-Concave Saddle Point Problems [pdf] R. Jiang, A. Mokhtari
- Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach [pdf] M. Fereydounian, A. Mokhtari, R. Pedarsani, H. Hassani.
2023
- A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem [pdf] R. Jiang, N. Abolfazli, A. Mokhtari, E. Yazdandoost Hamedani. Int. Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
2022
- FedAvg with Fine Tuning: Local Updates Lead to Representation Learning[pdf] L. Collins, H. Hassani, A. Mokhtari, S. Shakkottai, Neural Information Processing Systems (NeurIPS), 2022.
- Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods [pdf] Q. Jin, A. Mokhtari, Mathematical Programming (MAPR), 2022.
- MAML and ANIL Provably Learn Representations [pdf] L. Collins, A. Mokhtari, S. Oh, S. Shakkottai. International Conference on Machine Learning (ICML), 2022.
- Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood [pdf] Q. Jin, A. Koppel, K. Rajawat, A. Mokhtari. International Conference on Machine Learning (ICML), 2022.
- The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance [pdf] M. Faw, I. Tziotis, C. Caramanis, A. Mokhtari, S. Shakkottai, R. Ward. Conference on Learning Theory (COLT), 2022.
- Minimax Optimization: The Case of Convex-Submodular [pdf] (Oral Presentation)
A. Adibi, A. Mokhtari, H. Hassani. International Conference on Artificial Intelligence and Statistics (AISTATS) 2022.
- Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation System [pdf] M. Ye, R. Jiang, H. Wang, D. Choudhary, X. Du, B. Bhushanam, A. Mokhtari, A. Kejariwal, and Q. Liu. Conference on Uncertainty in Artificial Intelligence (UAI) 2022.
- Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity [pdf] A. Reisizadeh, I. Tziotis, H. Hassani, A. Mokhtari, R. Pedarsani. IEEE Journal on Selected Areas in Information Theory (JSAIT), 2022.
- How Does the Task Landscape Affect MAML Performance? [pdf] L. Collins, A. Mokhtari, S. Shakkottai. Conference on Lifelong Learning Agents (CoLLAs) 2022.
2021
- Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach [pdf]
Q. Jin, A. Mokhtari. Neural Information Processing Systems (NeurIPS), 2021.
- Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks [pdf]
A. Fallah, A. Mokhtari, A. Ozdaglar. Neural Information Processing Systems (NeurIPS), 2021.
- On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning [pdf]
A. Fallah, A. Mokhtari, A. Ozdaglar. Neural Information Processing Systems (NeurIPS), 2021.
- Exploiting Shared Representations for Personalized Federated Learning [pdf]
L. Collins, H. Hassani , A. Mokhtari, S. Shakkottai. International Conference on Machine Learning (ICML), 2021.
- Federated Learning with Compression: Unified Analysis and Sharp Guarantees [pdf]
F. Haddadpour, M. M. Kamani, A. Mokhtari, M. Mahdavi. International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
2020
- Task-Robust Model-Agnostic Meta-Learning [pdf] L. Collins, A. Mokhtari, S. Shakkottai. Neural Information Processing Systems (NeurIPS), 2020.
- Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking [pdf] I. Tziotis, C. Caramanis, A. Mokhtari. Neural Information Processing Systems (NeurIPS), 2020.
- Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach [pdf] A. Fallah, A. Mokhtari, A. Ozdaglar. Neural Information Processing Systems (NeurIPS), 2020.
- Convergence Rate of O(1/k) for Optimistic Gradient and Extra-gradient Methods in Smooth Convex-Concave Saddle Point Problems [pdf] (α-β order) A. Mokhtari, A. Ozdaglar, S. Pattathil. SIAM Journal on Optimization (SIOPT), 2020.
- Stochastic Conditional Gradient++: (Non-)Convex Minimization and Continuous Submodular Maximization [pdf] (α-β order) H. Hassani, A. Karbasi, A. Mokhtari, Z. Shen. SIAM Journal on Optimization (SIOPT), 2020.
- High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation [pdf] A. Mokhtari, A. Koppel. IEEE Transactions on Signal Processing (TSP), 2020.
- Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graph. [pdf] H. Taheri, A. Mokhtari, H. Hassani, R. Pedarsani. Int. Conference on Machine Learning (ICML), 2020.
- Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization [pdf] A. Mokhtari, H. Hassani, A. Karbasi. Journal of Machine Learning Research (JMLR), 2020.
- A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning [pdf] A. Mokhtari, A. Koppel, M. Takac, A. Ribeiro. Journal of Machine Learning Research (JMLR), 2020.
- On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms [pdf] A. Fallah, A. Mokhtari, A. Ozdaglar. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
- One Sample Stochastic Frank-Wolfe [pdf] M. Zhang, Z. Shen, A. Mokhtari, H. Hassani, A. Karbasi. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
- FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization [pdf] A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, R. Pedarsani. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
- Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization [pdf] M. Zhang, L. Chen, A. Mokhtari, H. Hassani, A. Karbasi. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
- A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach [pdf] (α-β order) A. Mokhtari, A. Ozdaglar, S. Pattathil. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
- Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy [pdf] M. Jahani, X. He, C. Ma, A. Mokhtari, D. Mudigere, A. Ribeiro, M. Takac. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
- DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate [pdf] S. Soori, K. Mischenko, A. Mokhtari, M. Dehnavi, M. Gurbuzbalaban. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
2019
- Stochastic Continuous Greedy++: When Upper and Lower Bounds Match
(α-β order)
H. Hassani, A. Karbasi, A. Mokhtari, Z. Shen. Advances in Neural Information Processing Systems (NeurIPS), 2019.
- Robust and Communication-Efficient Collaborative Learning [pdf] A. Reisizadeh, H. Taheri, A. Mokhtari, H. Hassani, R. Pedarsani. Advances in Neural Information Processing Systems (NeurIPS), 2019.
- Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods [pdf] A. Mokhtari, A. Ozdaglar, A. Jadbabaie. Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
- A Newton-based Method for Nonconvex Optimization with Fast Evasion of Saddle Points [pdf] S. Paternain, A. Mokhtari, A. Ribeiro. SIAM Journal on Optimization (SIOPT), 2019.
- An Exact Quantized Decentralized Gradient Descent Algorithm [pdf] A. Reisizadeh, A. Mokhtari, H. Hassani, R. Pedarsani. IEEE Transactions on Signal Processing (TSP), 2019.
- A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization [pdf] M. Eisen, A. Mokhtari, A. Ribeiro. IEEE Transactions on Signal Processing (TSP), 2019.
- Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE [pdf] J. Zhang, C. Uribe, A. Mokhtari, A. Jadbabaie. American Control Conference (ACC), 2019.
2018
- Escaping Saddle Points in Constrained Optimization [pdf]
A. Mokhtari, A. Ozdaglar, A. Jadbabaie
Advances in Neural Information Processing Systems (NeurIPS), 2018. (Spotlight: Top 4% of the submitted papers)
- Direct Runge-Kutta Discretization Achieves Acceleration [pdf]
J. Zhang, A. Mokhtari, S. Sra, A. Jadbabaie
Advances in Neural Information Processing Systems (NeurIPS), 2018. (Spotlight: Top 4% of the submitted papers)
- Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings [pdf] [Supplementary material]
A. Mokhtari, H. Hassani, A. Karbasi.
Int. Conference on Machine Learning (ICML), 2018. (Long talk)
- Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication [pdf] [Supp. material]Z. Shen, A. Mokhtari, H. Int. Conference on Machine Learning (ICML), 2018.
- Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap [pdf] [Supplementary material]A. Mokhtari, H. Hassani, A. Karbasi
Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
- Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method [pdf] [Supplementary material]M. Eisen, A. Mokhtari, A. Ribeiro
Int. Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
- IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate [pdf]A. Mokhtari, M. Eisen, A. Ribeiro
SIAM Journal on Optimization (SIOPT), 2018.
- Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate [pdf]
A. Mokhtari, M. Gürbüzbalaban, A. Ribeiro
SIAM Journal on Optimization (SIOPT), 2018.
- Quantized Decentralized Consensus Optimization [pdf]A. Reisizadeh, A. Mokhtari, H. Hassani, R. Pedarsani
IEEE Conference on Decision and Control (CDC), 2018.
- A Newton Method for Faster Navigation in Cluttered Environments [pdf]
S. Paternain, A. Mokhtari, A. Ribeiro
IEEE Conference on Decision and Control (CDC), 2018.
- Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning,
A. Koppel, A. Mokhtari, A. Ribeiro
Int. Conf. Acoustics Speech Signal Processing (ICASSP), 2018.
2017
- Efficient Methods for Large-Scale Empirical Risk Minimization [pdf]A. Mokhtari
Ph.D. Dissertation, University of Pennsylvania, 2017. (Joseph and Rosaline Wolf Award for Best Doctoral Dissertation)
- First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization [pdf] [Supplementary material]
A. Mokhtari, A. Ribeiro
Advances in Neural Information Processing Systems (NeurIPS), 2017.
- Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization [pdf]
A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, A. Ribeiro
IEEE Transactions on Automatic Control (TAC), 2017.
- Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation [pdf]
- T. Chen, A. Mokhtari, X. Wang, A. Ribeiro, G. B. Giannakis
- IEEE Transactions on Signal Processing (TSP), 2017.
- Decentralized Quasi-Newton Methods [pdf]
M. Eisen, A. Mokhtari, A. Ribeiro
IEEE Transactions on Signal Processing (TSP), 2017.
- Network Newton Distributed Optimization Methods [pdf]
A. Mokhtari, Q. Ling, A. Ribeiro
IEEE Transactions on Signal Processing (TSP), 2017.
- A Primal-Dual Quasi-Newton Method for Consensus Optimization [pdf]
M. Eisen, A. Mokhtari, A. Ribeiro
Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2017.
- An Incremental Quasi-Newton Method with a Local Superlinear Convergence Rate [pdf]
A. Mokhtari, M. Eisen, A. Ribeiro
Int. Conf. Acoustics Speech Signal Processing (ICASSP), 2017.
- A Double Incremental Aggregated Gradient Method with Linear Convergence Rate for Large-Scale Optimization [pdf]
A. Mokhtari, M. Gürbüzbalaban, A. Ribeiro
Int. Conf. Acoustics Speech Signal Processing (ICASSP), 2017.
- Large-Scale NonConvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation [pdf]
A. Mokhtari, A. Koppel, G. Scutari, A. Ribeiro
Int. Conf. Acoustics Speech Signal Processing (ICASSP), 2017.
- A Diagonal-Augmented Quasi-Newton Method with Application to Factorization Machines [pdf]
A. Mokhtari, A. Ingber
Int. Conf. Acoustics Speech Signal Processing (ICASSP), 2017.
2016
- DSA: Decentralized Double Stochastic Averaging Gradient Algorithm [pdf]
A. Mokhtari, A. Ribeiro
Journal of Machine Learning Research (JMLR), 2016.
- Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy [pdf] [Supplementary Material]
A. Mokhtari, H. Daneshmand, A. Lucchi, T. Hofmann, A. Ribeiro
Advances in Neural Information Processing Systems (NeurIPS), 2016.
- A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization [pdf]
A. Mokhtari, W. Shi, Q. Ling, A. Ribeiro.
IEEE Transactions on Signal and Information Processing over Networks (TSIPN), 2016
- DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers [pdf]
A. Mokhtari, W. Shi, Q. Ling, A. Ribeiro
IEEE Transactions on Signal Processing (TSP), 2016.
- A Class of Prediction-Correction Methods for Time-Varying Convex Optimization [pdf]
A. Simonetto, A. Mokhtari, A. Koppel, G. Leus, A. Ribeiro.
IEEE Transactions on Signal Processing (TSP), 2016.
- Online Optimization in Dynamic Environments: Improved Regret Rates for Strongly Convex Problems [pdf]
A. Mokhtari, S. Shahrampour, A. Jadbabaie, A. Ribeiro.
IEEE Conference on Decision and Control (CDC), 2016.
- A Decentralized Second-Order Method for Dynamic Optimization [pdf]
A. Mokhtari, W. Shi, Q. Ling, A. Ribeiro
IEEE Conference on Decision and Control (CDC), 2016.
- A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization [pdf]
M. Eisen, A. Mokhtari, A. Ribeiro
IEEE Conference on Decision and Control (CDC), 2016.
- A Quasi-Newton Prediction-Correction Method for Decentralized Dynamic Convex Optimization [pdf]
A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, A. Ribeiro
European Control Conference (ECC), 2016.
- Doubly Random Parallel Stochastic Methods for Large Scale Learning [pdf]
A. Mokhtari, A. Koppel, A. Ribeiro
American Control Conference (ACC), 2016.
- A Data-driven Approach to Stochastic Network Optimization [pdf]
T. Chen, A. Mokhtari, X. Wang, A. Ribeiro, G. B. Giannakis
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016.
- Decentralized Constrained Consensus Optimization with Primal-Dual Splitting Projection [pdf]
H. Zhang, W. Shi, A. Mokhtari, A. Ribeiro, Q. Ling.
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016.
- An Asynchronous Quasi-Newton Method for Consensus Optimization [pdf]
M. Eisen, A. Mokhtari, A. Ribeiro
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016.
- ESOM: Exact Second-Order Method for Consensus Optimization [pdf]
A. Mokhtari, W. Shi, Qing Ling
Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2016.
- Doubly Stochastic Algorithms for Large-Scale Optimization [pdf]
A. Koppel, A. Mokhtari, A. Ribeiro
Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2016.
2015
- Global Convergence of Online Limited Memory BFGS [pdf]
A. Mokhtari, A. Ribeiro
Journal of Machine Learning Research (JMLR), 2015.
- A Decentralized Prediction-Correction Method for Networked Time-Varying Convex Optimization [pdf]
A. Simonetto, A. Mokhtari, A. Koppel, G. Leus, A. Ribeiro
IEEE Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015.
- Decentralized Quadratically Approximated Alternating Direction Method of Multipliers [pdf]
A. Mokhtari, W. Shi, Q. Ling, A. Ribeiro
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015.
- Target Tracking with Dynamic Convex Optimization [pdf]
A. Koppel, A. Simonetto, A. Mokhtari, G. Leus, A. Ribeiro
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015.
- Decentralized Double Stochastic Averaging Gradient [pdf]
A. Mokhtari, A. Ribeiro
Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2015.
- Prediction-Correction Methods for Time-Varying Convex Optimization [pdf]
A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, A. Ribeiro
Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2015.
- An Approximate Newton Method for Distributed Optimization [pdf]
A. Mokhtari, Q. Ling, A. Ribeiro
Int. Conf. Acoustics Speech Signal Processing (ICASSP), 2015.
2014
- RES: Regularized Stochastic BFGS Algorithm [pdf]
A. Mokhtari, A. Ribeiro.
IEEE Transactions on Signal Processing (TSP), 2014.
- Network Newton [pdf]
A. Mokhtari, Q. Ling, A. Ribeiro.
Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2014.
- A Quasi-Newton Method for Large Scale Support Vector Machines [pdf]
A. Mokhtari, A. Ribeiro
Int. Conf. Acoustics Speech Signal Processing (ICASSP), 2014.
2013
- Regularized Stochastic BFGS algorithm [pdf]
A. Mokhtari, A. Ribeiro.
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2013.
- A Dual Stochastic DFP algorithm for Optimal Resource Allocation in Wireless Systems [pdf]
A. Mokhtari, A. Ribeiro.
IEEE Workshop on Signal Process. Advances in Wireless Communication (SPAWC), 2013.