Abolfazl Hashemi

Orcid: 0000-0002-8421-4270

According to our database1, Abolfazl Hashemi authored at least 61 papers between 2016 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Randomized greedy methods for weak submodular sensor selection with robustness considerations.
Autom., 2025

2024
No-Regret Learning in Dynamic Stackelberg Games.
IEEE Trans. Autom. Control., March, 2024

Equitable Federated Learning with Activation Clustering.
CoRR, 2024

Submodular Maximization Approaches for Equitable Client Selection in Federated Learning.
CoRR, 2024

Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes.
CoRR, 2024

Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis.
CoRR, 2024

AdaGossip: Adaptive Consensus Step-size for Decentralized Deep Learning with Communication Compression.
CoRR, 2024

Localized Distributional Robustness in Submodular Multi-Task Subset Selection.
CoRR, 2024

FedNMUT - Federated Noisy Model Update Tracking Convergence Analysis.
CoRR, 2024

Unveiling Privacy, Memorization, and Input Curvature Links.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing.
IEEE Control. Syst. Lett., 2023

Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise.
IEEE Access, 2023

Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications.
IEEE Access, 2023

Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Communication-Constrained Exchange of Zeroth-Order Information with Application to Collaborative Target Tracking.
Proceedings of the IEEE International Conference on Acoustics, 2023

Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks.
Proceedings of the IEEE International Conference on Acoustics, 2023

Randomized Greedy Algorithms for Sensor Selection in Large-Scale Satellite Constellations.
Proceedings of the American Control Conference, 2023

High Probability Guarantees For Federated Learning.
Proceedings of the 59th Annual Allerton Conference on Communication, 2023

Relative Entropy Regularization for Robust Submodular Multi-Task Subset Selection.
Proceedings of the 59th Annual Allerton Conference on Communication, 2023

Noisy Communication of Information in Federated Learning: An Improved Convergence Analysis.
Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

High Probability Guarantees for Submodular Maximization via Boosted Stochastic Greedy.
Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

Predictive Estimation for Reinforcement Learning with Time-Varying Reward Functions.
Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

2022
On the Benefits of Progressively Increasing Sampling Sizes in Stochastic Greedy Weak Submodular Maximization.
IEEE Trans. Signal Process., 2022

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning.
IEEE Trans. Parallel Distributed Syst., 2022

Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization Over Time-Varying Directed Graphs.
IEEE Trans. Autom. Control., 2022

Towards accelerated greedy sampling and reconstruction of bandlimited graph signals.
Signal Process., 2022

Faster non-convex federated learning via global and local momentum.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Robust Training in High Dimensions via Block Coordinate Geometric Median Descent.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Randomized Greedy Sensor Selection: Leveraging Weak Submodularity.
IEEE Trans. Autom. Control., 2021

Robust Generative Adversarial Imitation Learning via Local Lipschitzness.
CoRR, 2021

DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning.
CoRR, 2021

Function Approximation via Sparse Random Features.
CoRR, 2021

No-regret learning with high-probability in adversarial Markov decision processes.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations.
Proceedings of the IEEE International Conference on Acoustics, 2021

On the Performance-Complexity Tradeoff in Stochastic Greedy Weak Submodular Optimization.
Proceedings of the IEEE International Conference on Acoustics, 2021

Decentralized Optimization on Time-Varying Directed Graphs Under Communication Constraints.
Proceedings of the IEEE International Conference on Acoustics, 2021

Online Learning with Implicit Exploration in Episodic Markov Decision Processes.
Proceedings of the 2021 American Control Conference, 2021

2020
Improved Convergence Rates for Non-Convex Federated Learning with Compression.
CoRR, 2020

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization.
CoRR, 2020

Communication-Efficient Algorithms for Decentralized Optimization Over Directed Graphs.
CoRR, 2020

Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach.
Proceedings of the 2020 American Control Conference, 2020

2019
Identifying Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach.
CoRR, 2019

Stochastic-Greedy++: Closing the Optimality Gap in Exact Weak Submodular Maximization.
CoRR, 2019

Submodular Observation Selection and Information Gathering for Quadratic Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

Evolutionary Subspace Clustering: Discovering Structure in Self-expressive Time-series Data.
Proceedings of the IEEE International Conference on Acoustics, 2019

A Map Framework for Support Recovery of Sparse Signals Using Orthogonal Least Squares.
Proceedings of the IEEE International Conference on Acoustics, 2019

Online Topology Inference from Streaming Stationary Graph Signals.
Proceedings of the IEEE Data Science Workshop, 2019

On Submodularity of Quadratic Observation Selection in Constrained Networked Sensing Systems.
Proceedings of the 2019 American Control Conference, 2019

2018
Evolutionary Self-Expressive Models for Subspace Clustering.
IEEE J. Sel. Top. Signal Process., 2018

Accelerated orthogonal least-squares for large-scale sparse reconstruction.
Digit. Signal Process., 2018

Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

A Novel Scheme for Support Identification and Iterative Sampling of Bandlimited Graph Signals.
Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

Near-Optimal Distributed Estimation for a Network of Sensing Units Operating Under Communication Constraints.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
Accelerated Sparse Subspace Clustering.
CoRR, 2017

Recovery of sparse signals via Branch and Bound Least-Squares.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Sparse Tensor Decomposition for Haplotype Assembly of Diploids and Polyploids.
Proceedings of the 8th ACM International Conference on Bioinformatics, 2017

2016
Sparse recovery via Orthogonal Least-Squares under presence of Noise.
CoRR, 2016

Sampling Requirements and Accelerated Schemes for Sparse Linear Regression with Orthogonal Least-Squares.
CoRR, 2016

Sparse linear regression via generalized orthogonal least-squares.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016


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