Abolfazl Hashemi
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MINDS Group
August 2024
: New paper out:
Submodular Maximization Approaches for Equitable Client Selection in Federated Learning
August 2024
: Our paper below is accepted to
Automatica
:
Randomized Greedy Methods for Weak Submodular Sensor Selection with Robustness Considerations
July 2024
: Our paper below is accepted to
IEEE Conference on Decision and Control
:
Equitable Client Selection in Federated Learning via Truncated Submodular Maximization
June 2024
: New papers out:
A Fast Single-Loop Primal-Dual Algorithm for Non-Convex Functional Constrained Optimization
May 2024
: Our two papers below are accepted to
ICML 2024
:
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
Unveiling Privacy, Memorization, and Input Curvature Links
April 2024
: Our paper below is accepted to
UAI 2024
as
oral presentation
:
Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
April 2024
: Four new papers out:
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Randomized Greedy Methods for Weak Submodular Sensor Selection with Robustness Considerations
Localized Distributional Robustness in Submodular Multi-Task Subset Selection
AdaGossip: Adaptive Consensus Step-size for Decentralized Deep Learning with Communication Compression
March 2024
: New paper out:
FedNMUT – Federated Noisy Model Update Tracking Convergence Analysis
March 2024
: Our paper below is accepted to
IEEE Transactions on Automatic Control
:
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks
February 2024
: New paper out:
Unveiling Privacy, Memorization, and Input Curvature Links
January 2024
: Our paper on MRI phase synthesis with diffusion models is accepted to
32nd Annual Meeting of ISMRM
December 2023
: I am serving as an Area Chair for
ICML 2024
November 2023
: Our paper on Deep Learning-based Image Reconstruction is accpeted to
27th Annual Scientific Sessions of SCMR
October 2023
: Excited to start
NSF CPS Medium: Learning through the Air: Cross-Layer UAV Orchestration for Online Federated Optimization
October 2023
: Our paper “
No-Regret Learning in Dynamic Stackelberg Games
” is accepted to
IEEE Transactions on Automatic Control.
October 2023
: I am serving as an Area Chair for
AISTATS 2024
September 2023
: Our paper
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
is accepted to
NeurIPS 2023
August 2023
: Our two papers on High probability Results for Federated Learning and Robust Sensor Selection are accepted to
2023 Allerton Conference on Communication, Control, and Computing
July 2023
: Our three papers on High probability Results for Sensor Selection, Improved Results for Noisy Federated Learning, and Online Reinforcement Learning are accepted to
2023 Asilomar Conference on Signals, Systems, and Computers
June 2023
: Our journal paper “
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
” is accepted to
IEEE Control Systems Letters
.
June 2023
: Our journal papers “Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise” and “
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications
” are accepted to
IEEE Access
.
March 2023
: I am giving an invited talk on “Theory-guided Methods for Private Federated Learning” at
SIAM CSE
February 2023
: Our papers “
Communication-Constrained Exchange of Zeroth-Order Information with Application to Collaborative Target Tracking
” and “
Accelerated Decentralized Stochastic Non-Convex Optimization over Directed Networks
” are accepted to
ICASSP 2023
February 2023
: I am giving an invited talk on “No-Regret Learning in Dynamic Stackelberg Games” at
ITA 2023
.
January 2023
: Our paper “Randomized Greedy Algorithms for Sensor Selection in Large-Scale Satellite Constellations” is accepted to
ACC 2023
December 2022
: I am co-organizing four invited sessions on Distributed Learning and Decision Making at
ITA 2023
November 2022
: Our paper on Deep Learning for Low-latency Image Reconstruction is accpeted to
31st Annual Meeting of ISMRM
October 2022
: I am serving as an Area Chair for
AISTATS 2023
September 2022
: Invited talk at
SIAM MDS
on Generalization Bounds for Sparse Random Feature Expansions.
(Slides)
August 2022
:
Generalization Bounds for Sparse Random Feature Expansions
is accepted to Applied and Computational Harmonic Analysis.
July 2022
:
On the Benefits of Progressively Increasing Sampling Sizes in Stochastic Greedy Weak Submodular Maximization
is accepted to IEEE Transactions on Signal Processing.
May 2022
:
Faster Non-Convex Federated Learning via Global and Local Momentum
is accepted to The 2022 Conference on Uncertainty in Artificial Intelligence (UAI).
April 2022
: Invited talk at
FLOW
on Privacy Preserving Federated Learning.
(Slides)
April 2022
:I will be teaching a new graduate course on
Optimization for Deep Learning
in Fall 2022.
March 2022
:
Learning in Markov Decision Processes with Varying Rewards: High Probability Regret Bounds under Bandit Feedback and Unknown Horizon
is conditionally accepted to
IEEE Transactions on Automatic Control.
February 2022
: New paper out:
No-Regret Learning in Dynamic Stackelberg Games
February 2022
:
Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals
is accepted to The Elsevier Signal Processing.
January 2022
:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
is accepted to The 2022 International Conference on Artificial Intelligence and Statistics (AISTATS).
December 2021
:
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning
is accepted to IEEE Transactions on Parallel and Distributed System.
November 2021
:
Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs
is accepted to
IEEE Transactions on Automatic Control.
October 2021
: Invited talk at CERIAS on Robustness and Security and in Adversarial Environments
(Slides)
September 2021
: Invited talks at Purdue CS department and ICON on Collaborative Learning
(Slides)
August 2020
: Started as an Assistant Professor of ECE at Purdue!
June 2021
: Three new papers out:
Robust Generative Adversarial Imitation Learning via Local Lipschitzness
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning
May 2021
:
“No-Regret Learning with High-Probability in Adversarial Markov Decision Processes
is accepted to UAI 2021
March 2021
: Our paper
Function Approximation via Sparse Random Features
is trending on
DeepAI
March 2021
: New paper out:
Generalization Bounds for Sparse Random Feature Expansions
January 2021
: Three papers are accepted to ICASSP 2021
January 2021
: One paper is accepted to ACC 2021
January 2021
: New paper out:
Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs
December 2020
: New paper out:
Faster Non-Convex Federated Learning via Global and Local Momentum
November 2020
: New paper out:
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
September 2020
: Started my Postdoc at Oden Institute!
August 2020
: I successfully defended my dissertation!
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