| Abolfazl Hashemi

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!