Mahdi Soltanolkotabi
Orcid: 0000-0003-2101-6418Affiliations:
- University of Southern California, USA
According to our database1,
Mahdi Soltanolkotabi
authored at least 87 papers
between 2008 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models.
CoRR, 2024
2023
CoRR, 2023
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization.
CoRR, 2023
CoRR, 2023
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory.
CoRR, 2023
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023
2022
Convergence and Sample Complexity of Gradient Methods for the Model-Free Linear-Quadratic Regulator Problem.
IEEE Trans. Autom. Control., 2022
CoRR, 2022
CoRR, 2022
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction.
CoRR, 2022
HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022
Statistical Minimax Lower Bounds for Transfer Learning in Linear Binary Classification.
Proceedings of the IEEE International Symposium on Information Theory, 2022
On The Effectiveness of Active Learning by Uncertainty Sampling in Classification of High-Dimensional Gaussian Mixture Data.
Proceedings of the IEEE International Conference on Acoustics, 2022
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022
2021
IEEE Control. Syst. Lett., 2021
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision.
CoRR, 2021
CoRR, 2021
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers.
CoRR, 2021
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Generalization Guarantees for Neural Architecture Search with Train-Validation Split.
Proceedings of the 38th International Conference on Machine Learning, 2021
Data augmentation for deep learning based accelerated MRI reconstruction with limited data.
Proceedings of the 38th International Conference on Machine Learning, 2021
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models.
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the 9th International Conference on Learning Representations, 2021
On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
2020
Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks.
IEEE J. Sel. Areas Inf. Theory, 2020
CoRR, 2020
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation.
CoRR, 2020
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation.
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators.
Proceedings of the 8th International Conference on Learning Representations, 2020
Proceedings of the 28th European Signal Processing Conference, 2020
Proceedings of the Conference on Learning Theory, 2020
Proceedings of the Conference on Learning Theory, 2020
Proceedings of the 2020 American Control Conference, 2020
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks.
IEEE Trans. Inf. Theory, 2019
Structured Signal Recovery From Quadratic Measurements: Breaking Sample Complexity Barriers via Nonconvex Optimization.
IEEE Trans. Inf. Theory, 2019
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian.
CoRR, 2019
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks.
CoRR, 2019
Proceedings of the IEEE International Symposium on Information Theory, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
Global exponential convergence of gradient methods over the nonconvex landscape of the linear quadratic regulator.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
2018
IEEE Trans. Inf. Theory, 2018
CoRR, 2018
CoRR, 2018
Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, 2018
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018
2017
SIAM J. Optim., 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
2016
Proceedings of the 33nd International Conference on Machine Learning, 2016
2015
IEEE Trans. Inf. Theory, 2015
CoRR, 2015
2014
2013
2012
EURASIP J. Adv. Signal Process., 2012
CoRR, 2012
2011
2009
Proceedings of the 2009 International Conference on Telecommunications, 2009
Proceedings of IEEE International Conference on Communications, 2009
Proceedings of the 17th European Signal Processing Conference, 2009
2008
Proceedings of the 2008 International Conference on Telecommunications, 2008