Saber Salehkaleybar

Orcid: 0000-0003-3934-9931

According to our database1, Saber Salehkaleybar authored at least 45 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Order Optimal Bounds for One-Shot Federated Learning Over Non-Convex Loss Functions.
IEEE Trans. Inf. Theory, April, 2024

MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters.
CoRR, 2024

2023
ParaLiNGAM: Parallel causal structure learning for linear non-Gaussian acyclic models.
J. Parallel Distributed Comput., June, 2023

Fast causal orientation learning in directed acyclic graphs.
Int. J. Approx. Reason., 2023

Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data.
CoRR, 2023

Efficiently Escaping Saddle Points for Non-Convex Policy Optimization.
CoRR, 2023

A Cross-Moment Approach for Causal Effect Estimation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Active learning of causal structures with deep reinforcement learning.
Neural Networks, 2022

Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions.
CoRR, 2022

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models.
CoRR, 2022

Adaptive Momentum-Based Policy Gradient with Second-Order Information.
CoRR, 2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions.
IEEE Wirel. Commun. Lett., 2021

gIM: GPU Accelerated RIS-Based Influence Maximization Algorithm.
IEEE Trans. Parallel Distributed Syst., 2021

Adversarial orthogonal regression: Two non-linear regressions for causal inference.
Neural Networks, 2021

One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them.
J. Mach. Learn. Res., 2021

Causal Imitative Model for Autonomous Driving.
CoRR, 2021

Order Optimal One-Shot Federated Learning for non-Convex Loss Functions.
CoRR, 2021

2020
cuPC: CUDA-Based Parallel PC Algorithm for Causal Structure Learning on GPU.
IEEE Trans. Parallel Distributed Syst., 2020

Distributed voting in beep model.
Signal Process., 2020

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.
J. Mach. Learn. Res., 2020

Broadcast distributed voting algorithm in population protocols.
IET Signal Process., 2020

GPU Accelerated RIS-based Influence Maximization Algorithm.
CoRR, 2020

Multi Variable-layer Neural Networks for Decoding Linear Codes.
Proceedings of the Iran Workshop on Communication and Information Theory, 2020

LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments.
Proceedings of the 37th International Conference on Machine Learning, 2020

Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Interventional Experiment Design for Causal Structure Learning.
CoRR, 2019

Seedless Graph Matching via Tail of Degree Distribution for Correlated Erdos-Renyi Graphs.
CoRR, 2019

Theoretical Limits of One-Shot Distributed Learning.
CoRR, 2019

Order Optimal One-Shot Distributed Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Counting and Uniform Sampling from Markov Equivalent DAGs.
CoRR, 2018

Budgeted Experiment Design for Causal Structure Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Learning Vector Autoregressive Models With Latent Processes.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Learning Latent Networks in Vector Auto Regressive Models.
CoRR, 2017

Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments.
CoRR, 2017

Learning Causal Structures Using Regression Invariance.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Identifying nonlinear 1-step causal influences in presence of latent variables.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016
Token-Based Function Computation with Memory.
IEEE Trans. Parallel Distributed Syst., 2016

Distributed binary majority voting via exponential distribution.
IET Signal Process., 2016

A periodic jump-based rendezvous algorithm in cognitive radio networks.
Comput. Commun., 2016

2015
Distributed Voting/Ranking With Optimal Number of States per Node.
IEEE Trans. Signal Inf. Process. over Networks, 2015

2013
Delay analysis and buffer management for random access in cognitive radio networks.
Proceedings of the Iran Workshop on Communication and Information Theory, 2013

Averaging consensus over erasure channels via local synchronization.
Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

2011
QoS-aware joint policies in cognitive radio networks.
Proceedings of the 7th International Wireless Communications and Mobile Computing Conference, 2011


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