Distributed Machine Learning and Coded Computing

• A. Reisizadeh, F. Farnia, R. Pedarsani, A. Jadbabaie, “Robust Federated Learning: The Case of Affine Distribution Shifts”, Advances in Neural Information Processing Systems (NeurIPS), 2020.

• H. Taheri, A. Mokhtari, H. Hassani, R. Pedarsani, “Quantized Decentralized Stochastic Learning over Directed Graphs”, International Conference on Machine Learning (ICML), 2020.

• A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, R. Pedarsani, “FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization”, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

• S. Prakash, A. Reisizadeh, R. Pedarsani, S. Avestimehr, "Coded Computing for Distributed Graph Analytics”, IEEE Transactions on Information Theory, Vol. 66, No. 10, 2020.

• A. Reisizadeh, H. Taheri, A. Mokhtari, H. Hassani, R. Pedarsani, “Robust and Communication-Efficient Collaborative Learning”, Advances in Neural Information Processing Systems (NeurIPS), 2019.

• A. Reisizadeh, A. Mokhtari, H. Hassani, R. Pedarsani, “An Exact Quantized Decentralized Gradient Descent Algorithm”, IEEE Transactions on Signal Processing (TSP), Vol. 67, No. 19, 2019.

• C. Yang, R. Pedarsani, S. Avestimehr, “Timely-Throughput Optimal Coded Computing over Cloud Networks”, ACM International Symposium on Mobile Ad Hoc Networking and Computing (Mobihoc), 2019.

• A. Reisizadeh, S. Prakash, R. Pedarsani, S. Avestimehr, “Coded Computation over Heterogeneous Clusters”, IEEE Transactions on Information Theory, Vol. 65, No. 7, 2019.

• K. Lee, M. Lam, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran, “Speeding Up Distributed Machine Learning Using Codes”, IEEE Transactions on Information Theory, Vol. 64, No. 3, 2018.

• A. Reisizadeh, R. Pedarsani, “Latency Analysis of Coded Computation Schemes over Wireless Networks”, Allerton Conference on Communication, Control, and Computing, 2017.

Intelligent Transportation Networks

• D. Lazar, S. Coogan, and R. Pedarsani, “Routing for Traffic Networks with Mixed Autonomy”, to appear in IEEE Transactions on Automatic Control (TAC), 2020.

• Q. Wei, J. A. Rodriguez, R. Pedarsani, S. Coogan, “Ride-Sharing Networks with Mixed Autonomy”, to appear in IEEE Transactions on Control of Network Systems (TCNS), 2020.

• D. Lazar, E. Biyik, D. Sadigh, and R. Pedarsani, “The Green Choice: Learning and Influencing Human Decisions on Shared Roads", IEEE Conference on Decision and Control (CDC), 2019.

• E. Biyik, D. Lazar, R. Pedarsani, D. Sadigh , “Altruistic Autonomy: Beating Congestion on Shared Roads”, 13th International Workshop on Algorithmic Foundations of Robotics (WAFR), 2018.

• D. Lazar, K. Chandrasekher, R. Pedarsani, and D. Sadigh, “Maximizing Road Capacity Using Cars that Influence People”, IEEE Conference on Decision and Control (CDC), 2018.

• N. Mehr, R. Horowtiz, and R. Pedarsani, “Low-Complexity Ramp Metering for Freeway Congestion Control via Network Utility Maximization”, IEEE Conference on Decision and Control (CDC), 2017.

• N. Mehr, J. Lioris, R. Horowtiz, and R. Pedarsani, “Joint Perimeter and Signal Control of Urban Traffic via Network Utility Maximization”, IEEE International Conference on Intelligent Transportation Systems (ITSC), 2017.

• J. Lioris, R. Pedarsani, F. Yildiz, and P. Varaiya, “Platoons of Connected Vehicles Can Double Throughput in Urban Roads”, Journal of Transportation Research Part C, vol. 75, pp. 292-305, 2017.

• A. Muralidharan, R. Pedarsani, and P. Varaiya, “Analysis of Fixed-Time Control”, Journal of Transportation Research Part B, vol. 73, pp. 81–90, 2015.

High-Dimensional Inference Problems

• H. Taheri, R. Pedarsani, C. Thrampoulidis, “Sharp Asymptotics and Optimal Performance for Inference in Binary Models”, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

• H. Taheri, R. Pedarsani, C. Thrampoulidis, “Sharp Guarantees for Solving Random Equations with One-Bit Information”, Allerton Conference on Communication, Control, and Computing, 2019.

• K. Lee, K. Chandrasekher, R. Pedarsani, and K. Ramchandran, “SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing based on Sparse-Graph Codes”, IEEE Transactions on Signal Processing, Vol. 67, No. 17, 2019.

• X. Li, D. Yin, S. Pawar, R. Pedarsani, and K. Ramchandran, “Sub-linear Time Support Recovery for Compressed Sensing Using Sparse-Graph Codes”, IEEE Transactions on Information Theory, Vol. 65, No. 10, 2019.

• D. Yin, R. Pedarsani, Y. Chen, and K. Ramchandran, “Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes”, IEEE Transactions on Information Theory, Vol. 65, No. 3, 2019.

• A. Reisizadeh, P. Abdalla, R. Pedarsani, “Sub-linear Time Stochastic Threshold Group Testing via Sparse-Graph Codes”, Information Theory Workshop (ITW), 2018.

• R. Pedarsani, K. Lee, and K. Ramchandran, “PhaseCode: Fast and Efficient Compressive Phase Retrieval based on Sparse-Graph Codes”, IEEE Transactions on Information Theory, Vol. 63, No. 6, 2017.

Scheduling and Queueing

• C. Yang, R. Pedarsani, S. Avestimehr, “Communication-Aware Scheduling of Serial Tasks for Dispersed Computing”, IEEE/ACM Transactions on Networking, Vol. 27, No. 4, 2019.

• R. Pedarsani, J. Walrand, and Yuan Zhong, “Robust Scheduling for Flexible Processing Networks”, Advances in Applied Probability, Vol. 49, No. 2, 2017.

• K. Lee, R. Pedarsani, and K. Ramchandran, “On Scheduling Redundant Requests with Cancellation Overheads”, IEEE/ACM Transactions on Networking, Vol. 25, No. 2, pp. 1279–1290, 2017.

• R. Pedarsani and J. Walrand, “Stability of Open Multiclass Queueing Networks under Longest-Queue and Longest-Dominating-Queue Scheduling”, Journal of Applied Probability, Vol. 53, No. 2, 2016.