Abolfazl Hashemi
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MINDS
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 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
: New paper out:
Robust Generative Adversarial Imitation Learning via Local Lipschitzness
June 2021
: New paper out:
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
June 2021
: New paper out:
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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