Selected Tutorials
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Statistical and Algorithmic Foundations of Reinforcement Learning, JSM 2023.
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Non-asymptotic Analysis for Reinforcement Learning, SIGMETRICS 2023.
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Advances in Federated Optimization: Efficiency, Resiliency, and Privacy, ICASSP 2023.
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Reinforcement Learning: Fundamentals, Algorithms, and Theory, ICASSP 2022.
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Scalable and Robust Nonconvex Approaches for Low-rank Structure Estimation, International Workshop on Intelligent Signal Processing, 2021.
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Nonconvex Optimization for High-Dimensional Signal Estimation: Spectral and Iterative Methods, EUSIPCO 2020.
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Nonconvex Optimization Meets Low-Rank Matrix Factorization, accompanying slides of an overview paper with the same title.
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Taming Nonconvexity in Information Science, ITW 2018.
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Recent Advances in Nonconvex Methods for High-Dimensional Estimation, ICASSP 2018.
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Convex Optimization Techniques for Super-resolution Parameter Estimation, ICASSP 2016.
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Compressive Parameter Estimation: The Good, The Bad, and The Ugly, SSP 2014.
Selected Plenary and Keynote Talks
A Tale of Preconditioning and Overparameterization in Ill-conditioned Low-rank Estimation, CAMDA Conference, 2023.
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Understanding the Efficacy of Reinforcement
Learning Through a Non-asymptotic Lens, IEEE Data Science and Learning Workshop, 2022.
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Coping with Heterogeneity and Privacy in Communication-Efficient Federated Optimization, CVPR Workshop on Federated Learning for Computer Vision (FedVision), 2022. [Video]
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Model-Free RL: Non-asymptotic Statistical and Computational Guarantees, MIT LIDS Student Conference, 2022. [Video]
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Non-asymptotic Statistical and Computational
Guarantees of Reinforcement Learning Algorithms, Goldsmith Lecture, IEEE East Asian School of Information Theory, 2021.
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Nonconvex Low-Rank Matrix Estimation: Geometry, Robustness, and Acceleration, SIAM Conference on Imaging Science, 2020. [Video]
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Geometry and Regularization in Nonconvex Low-Rank Estimation, Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, 2019.
Selected Research Talks
Sample Complexity of Q-learning: from Single-agent to Federated Learning, 2023.
Reinforcement Learning meets Federated Learning and Distributional Robustness, 2023.
Offline Reinforcement Learning:
Towards Optimal Sample Complexity and Distributional Robustness, 2023.
Multi-agent Reinforcement Learning: Statistical and Optimization Perspectives, 2022.
Coping with Heterogeneity and Privacy in Communication-Efficient Federated Optimization, 2022.
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Policy Optimization in Reinforcement Learning: A Tale of Preconditioning and Regularization, 2021.
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Preconditioning Helps: Faster Convergence in Statistical and Reinforcement Learning, 2021. [Video]
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Implicit Regularization in Nonconvex Statistical Estimation, 2018.
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Exploiting Geometry for High-Resolution Source Localization, 2017.
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Recent Progress on Algorithmic Phase Retrieval, 2017.
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Solving Corrupted Systems of Quadratic Equations, Provably, 2016.
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Covariance Sketching via Quadratic Sampling, 2015.
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Sparse Parameter Estimation:
Compressed Sensing meets Matrix Pencil, 2014.
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