Publications

Causality

Y. Yang, S. Salehkaleybar, and N. Kiyavash, Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

Y. Kivva, S. Salehkaleybar, and N. Kiyavash, A Cross-Moment Approach for Causal Effect Estimation, 37th Conference on Neural Information Processing Systems (NeurIPS), 2023.

E. Mokhtarian, S. Salehkaleybar, A. Ghassami, and N. Kiyavash, A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models, Journal of Machine Learning Research, 2023.

R. Safaeian, S. Salehkaleybar, M. Tabandeh, Fast Causal Orientation Learning in Directed Acyclic Graphs, International Journal of Approximate Reasoning, 2023.

A. Amirinezhad, S. Salehkaleybar, and M. Hashemi, Active Learning of Causal Structures with Deep Reinforcement Learning, Neural Networks, 2022.

M. R. Heydari, S. Salehkaleybar, K. Zhang, Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference, Neural Networks, 2021.

M. Samsami, M. Bahari, S. Salehkaleybar, A. Alahi, Causal Imitative Model for Autonomous Driving, preprint, 2021.

A. AhmadiTeshnizi, S. Salehkaleybar, N. Kiyavash, LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments, 37th International Conference on Machine Learning (ICML), 2020.

S. Salehkaleybar, A. Ghassami, N. Kiyavash, and K. Zhang, Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables, Journal of Machine Learning Research (JMLR), 2020.

A. Ghassami, S. Salehkaleybar, N. Kiyavash, Interventional Experiment Design for Causal Structure Learning, preprint, 2019.

A. Ghassami, S. Salehkaleybar, N. Kiyavash, K. Zhang, Counting and Sampling from Markov Equivalent DAGs Using Clique Trees, 33rd AAAI Conference on Artificial Intelligence (AAAI), 2018.

A. Ghassami, S. Salehkaleybar, N. Kiyavash, E. Bareinboim, Budgeted Experiment Design for Causal Structure Learning, 35th International Conference on Machine Learning (ICML), 2018.

S. Salehkaleybar, J. Etesami, N. Kiyavash, K. Zhang, Learning Vector Autoregressive Models with Latent Processes, 32nd AAAI Conference on Artificial Intelligence (AAAI), 2017.

A. Ghassami, S. Salehkaleybar, N. Kiyavash, Kun Zhang, Learning Causal Structures Using Regression Invariance, 31st Conference on Neural Information Processing Systems (NIPS), 2017.

S. Salehkaleybar, J. Etesami, N. Kiyavash, Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables, International Symposium on Information Theory, 2017.

Stochastic Optimization and Reinforcement Learning

A. Sharifnassab, S. Salehkaleybar, and R. Sutton, MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters, preprint, 2024.

S. Khorasani, S. Salehkaleybar, N. Kiyavash, N. He, and M. Grossglauser, Efficiently Escaping Saddle Points for Non-Convex Policy Optimization, preprint, 2023.

S. Masiha, S. Salehkaleybar, N. He, N. Kiyavash, and P. Thiran, Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions, 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.

S. Salehkaleybar, S. Khorasani, N. Kiyavash, N. He, and P. Thiran, Momentum-Based Policy Gradient with Second-Order Information, preprint, 2022.

Distributed Learning & Network Algorithms

A. Sharifnassab, S. Salehkaleybar, and S. J. Golestani, Order Optimal One-Shot Federated Learning for non-Convex Loss Functions, IEEE Transactions on Information Theory, 2023.

A. Shahbazinia, S. Salehkaleybar, M. Hashemi, ParaLiNGAM: Parallel Causal Structure Learning for Linear non-Gaussian Acyclic Models, Journal of Parallel and Distributed Computing, 2023.

S. Salehkaleybar, A. Sharifnassab, and S. J. Golestani, One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them, Journal of Machine Learning Research (JMLR), 2021.

S. Shahrouz, S. Salehkaleybar, and M. Hashemi, GPU Accelerated RIS-based Influence Maximization Algorithm, IEEE transactions on Parallel and Distributed Systems, 2021.

H. Bandealinaeini, S. Salehkaleybar, Broadcast Distributed Voting Algorithm in Population Protocols, IET Signal Processing, 2021.

B. Ghojogh, S. Salehkaleybar, Distributed Voting for Beep Model, Signal Processing, 2020.

A. Sharif-nassab, S. Salehkaleybar, and S. J. Golestani, Order Optimal One-Shot Distributed Learning, 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019.

B. Zarebavani, F. Jafarinejad, M. Hashemi, and S. Salehkaleybar, cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU, IEEE transactions on Parallel and Distributed Systems, 2019.

S. Salehkaleybar, M. R. Pakravan, A Periodic Jump-based Rendezvous Algorithm in Cognitive Radio Networks, Computer Communications, 2016.

S. Salehkaleybar, A. Sharifnassab, S. J. Golestani, Distributed Voting/Ranking with Optimal Number of States per Node, IEEE transactions on Signal and Information Processing over Networks, 2015.

S. Salehkaleybar, S. J. Golestani, Token-based Function Computation with Memory, IEEE transactions on Parallel and Distributed Systems, 2015.

S. Salehkaleybar, S. J. Golestani, Distributed Binary Majority Voting via Exponential Distribution, IET Signal Processing, 2015.

S. Salehkaleybar, S. J. Golestani, Averaging Consensus over Erasure Channels via Local Synchronization, International Symposium on Information Theory, 2013.

S. A. Majd, S. Salehkaleybar, M. R. Pakravan, Multi-user Opportunistic Spectrum Access with Channel Impairments, AEU International Journal of Electronics and Communications, 2013.

S. Salehkaleybar, S. A. Majd, M. R. Pakravan, Delay Analysis and Buffer Management for Random Access in Cognitive Radio Networks, in proceeding of IWCIT, 2012.

S. Salehkaleybar, S. A. Majd, M. R. Pakravan, QoS Aware Joint Policies in Cognitive Radio Networks, in proceeding of IWCMC, 2011.

S. Salehkaleybar, S. A. Majd, M. R. Pakravan, An Upper Bound on the Throughput for Myopic Policy in Multi-channel Opportunistic Access, in proceeding of IST, 2010.

Machine Learning

S. Dehdashtian, M. Hashemi, and S. Salehkaleybar, Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions, IEEE Wireless Communications Letter, 2021.

A. Sharif-nassab, S. Salehklaeybar, and S. J. Golestani, Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks , Eighth International Conference on Learning Representations (ICLR), 2020.

S. Malek, S. Salehkaleybar, A. Amini, Multi Variable-layer Neural Networks for Decoding Linear Codes, in processing of IWCIT, 2020.

M. Bozorg, S. Salehkaleybar, M. Hashemi, Seedless Graph Matching via Tail of Degree Distribution for Correlated Erdos-Renyi Graphs, preprint, 2019.