Preprints

  1. Memarian, F., Hashemi, A., Niekum, S., Topcu, U., “Robust Generative Adversarial Imitation Learning via Local Lipschitzness,” Preprint, 2021. (Link)

  2. Das, R., Hashemi, A., Sanghavi, S., Dhillon, I., “DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning,” Preprint, 2021. (Link)

  3. Das, R., Acharya, A., Hashemi, A., Sanghavi, S., Dhillon, I., Topcu, U., “Faster Non-Convex Federated Learning via Global and Local Momentum,” Preprint, 2021. (Link)

  4. Hashemi, A., Schaeffer, H., Shi, B., Tran, G., Ward, R., “Generalization Bounds for Sparse Random Feature Expansions,” Preprint, 2021. (Link)

  5. Hashemi, A., Vikalo, H., de Veciana, G., “Performance-Complexity Tradeoffs in Greedy Weak Submodular Maximization with Random Sampling,” Preprint, 2021. (Link)

  6. Hashemi, A., Shafipour, R., Vikalo, H., Mateos, G., “Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals,” Preprint, 2021. (Link)

Journals

  1. Hashemi, A., Acharya, A., Das, R., Vikalo, H., Sanghavi, S., Dhillon, I., “On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning,” IEEE Transactions on Parallel and Distributed Systems, Special Section on Parallel and Distributed Computing Techniques for AI, ML, and DL, Accepted in Dec. 2021. (Link)

  2. Chen, Y., Hashemi, A., Vikalo, H., “Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs,” IEEE Transactions on Automatic Control, Accepted in Dec. 2021. (Link)

  3. Hashemi, A., Ghasemi, M., Vikalo, H., Topcu, U., “Randomized greedy sensor selection: Leveraging weak submodularity,” IEEE Transactions on Automatic Control, Jan. 2021. (Link)

  4. Hashemi, A. and Vikalo, H., “Evolutionary Self-Expressive Models for Subspace Clustering,” IEEE Journal of Selected Topics in Signal Processing, Special Issue on Data Science: Robust Subspace Learning and Tracking, vol. 12, no. 6, pp. 1534–1546, Dec. 2018. (Link)

  5. Hashemi, A., Zhu, B., Vikalo, H., “Sparse Tensor Decomposition for Haplotype Assembly of Diploids and Polyploids,” BMC Genomics, vol. 19, no. 4, pp. 1–15, Mar. 2018. (Link)

  6. Hashemi, A. and Vikalo, H., “Accelerated Orthogonal Least-Squares for Large-Scale Sparse Reconstruction,” Digital Signal Processing, vol. 82, pp. 91–105, Nov. 2018. (Link)

Conference Papers

  1. Acharya, A., Hashemi, A., Jain, P., Sanghavi, S., Dhillon, I., Topcu, U., “Robust Training in High Dimensions via Block Coordinate Geometric Median Descent,” The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. (Link) (Slides) (Poster)

  2. Ghasemi, M., Hashemi, A., Vikalo, H., Topcu, U., “No-Regret Learning with High-Probability in Adversarial Markov Decision Processes,” Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (Link) (Slides) (Poster)

  3. Ghasemi, M., Hashemi, A., Topcu, U., Vikalo, H., “Online Learning with Implicit Exploration in Episodic Markov Decision Processes,” American Control Conference (ACC), 2021. (Link) (Slides)

  4. Savas, Y., Hashemi, A., Vinod, AP., Sadler, BM., Topcu, U., “Physical-Layer Security via Distributed Beam-forming in the Presence of Adversaries with Unknown Locations,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021. (Link) (arXiv) (Slides) (Poster)

  5. Chen, Y., Hashemi, A., Vikalo, H., “Decentralized Optimization on Time-Varying Directed Graphs under Communication Constraints,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021. (Link) (Slides) (Poster)

  6. Hashemi, A., Vikalo, H., de Veciana, G., “On the Performance-Complexity Tradeoff in Stochastic Greedy Weak Submodular Optimization,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021. (Link) (Slides) (Poster)

  7. Ghasemi, M., Hashemi, A., Vikalo, H., Topcu, U., “Identifying Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach,” American Control Conference (ACC), Denver, CO, July 2020. (Link) (Slides)

  8. Hashemi, A., Ghasemi, M., Vikalo, H., Topcu, U., “Submodular Observation Selection and Information Gathering for Quadratic Models,” International Conference on Machine Learning (ICML), Long Beach, CA, June 2019. (Link)

  9. Ghasemi, M., Hashemi, A., Vikalo, H., Topcu, U., “On Submodularity of Quadratic Observation Selection in Constrained Networked Sensing Systems,” American Control Conference (ACC), Philadelphia, PA, July 2019. (Link) (Slides)

  10. Shafipour, R., Hashemi, A., Mateos, G., Vikalo, H., “Online topology inference from streaming stationary graph signals,” IEEE Data Science Workshop (DSW), Minneapolis, MN, June 2019. (Link) (Slides)

  11. Hashemi, A. and Vikalo, H., “Evolutionary Subspace Clustering: Discovering Structure In Self-expressive Time-series Data,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), Brighton, UK, May 2019. (Link)

  12. Consul, S., Hashemi, A., Vikalo, H., “A MAP Framework for Support Recovery of Sparse Signals Using Orthogonal Least Squares,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), Brighton, UK, May 2019. (Link) (Poster)

  13. Hashemi, A., Kilic, O.F., Vikalo, H., “Near-Optimal Distributed Estimation for a Network of Sensing Units Operating Under Communication Constraints,” Conference on Decision and Control (CDC), Miami, FL, Dec. 2018. (Link)

  14. Hashemi, A., Shafipour, R., Vikalo, H., Mateos, G., “A Novel Scheme for Support Identification and Iterative Sampling of Bandlimited Graph Signals,” Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, Nov. 2018. (Link) (Poster)

  15. Hashemi, A., Ghasemi, M., Vikalo, H., Topcu, U., “A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks,” American Control Conference (ACC), Milwaukee, WI, Jun. 2018. (Link)

  16. Hashemi, A., Shafipour, R., Vikalo, H., Mateos, G., “Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), Calgary, Alberta, Canada, Apr. 2018. (Link) (Poster)

  17. Hashemi, A. and Vikalo, H., “Sparse Recovery via Branch and Bound Least-Squares,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), New Orleans, LA, Mar. 2017. (Link) (Poster)

  18. Hashemi, A., Zhu, B., Vikalo, H., “A Tensor Factorization Framework for Haplotype Assembly of Diploids and Polyploids,” RECOMB Satellite Workshop on Massively Parallel Sequencing, Hong Kong, May 2017.

  19. Hashemi, A. and Vikalo, H., “Sparse Linear Regression via Generalized Orthogonal Least-Squares,” Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, Dec. 2016. (Link) (Slides)

Technical Reports

  1. Chen, Y., Hashemi, A., Vikalo, H., “Communication-Efficient Algorithms for Distributed Optimization Over Directed Graphs,” Technical Report, 2020. (Link)

  2. Hashemi, A. and Vikalo, H., “Sparse Recovery via Orthogonal Least-Squares under Presence of Noise,” Technical Report, 2016. (Link)

Theses

  1. Hashemi, A., “Efficient Algorithms for Structured Inference and Collaborative Learning,” Dissertation, University of Texas at Austin, Aug. 2020. (Link)

  2. Hashemi, A., “Vision-Based Gait Analysis via Exploiting Human Body-Parts Proportion,” Bachelor Thesis, Sharif University of Technology, June 2014.