Yuejie Chi

Selected Tutorials

• Generative Priors in Data Science: From Low-rank to Diffusion Models, NASIT 2024.

• Information-theoretic, Statistical and Algorithmic Foundations of Reinforcement Learning, ISIT 2024.

• Statistical and Algorithmic Foundations of Reinforcement Learning, JSM 2023.

• Non-asymptotic Analysis for Reinforcement Learning, SIGMETRICS 2023.

• Advances in Federated Optimization: Efficiency, Resiliency, and Privacy, ICASSP 2023.

• Reinforcement Learning: Fundamentals, Algorithms, and Theory, ICASSP 2022.

• Scalable and Robust Nonconvex Approaches for Low-rank Structure Estimation, International Workshop on Intelligent Signal Processing, 2021.

• Nonconvex Optimization for High-Dimensional Signal Estimation: Spectral and Iterative Methods, EUSIPCO 2020.

• Nonconvex Optimization Meets Low-Rank Matrix Factorization, accompanying slides of an overview paper with the same title.

• Taming Nonconvexity in Information Science, ITW 2018.

• Recent Advances in Nonconvex Methods for High-Dimensional Estimation, ICASSP 2018.

• Convex Optimization Techniques for Super-resolution Parameter Estimation, ICASSP 2016.

• Compressive Parameter Estimation: The Good, The Bad, and The Ugly, SSP 2014.

Selected Plenary and Keynote Talks

• Federated Reinforcement Learning: Statistical and Communication Trade-offs, Learning for Dynamics & Control (L4DC) Conference, 2025.

• Fantastic Diffusion Models and Where to Apply Them, IEEE Information Theory Workshop, 2024.

• Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models, NIST/IEEE Conference on Computational Imaging Using Synthetic Apertures, 2024.

• A Tale of Preconditioning and Overparameterization in Ill-conditioned Low-rank Estimation, CAMDA Conference, 2023.

• Understanding the Efficacy of Reinforcement Learning Through a Non-asymptotic Lens, IEEE Data Science and Learning Workshop, 2022.

• Model-Free RL: Non-asymptotic Statistical and Computational Guarantees, MIT LIDS Student Conference, 2022. [Video]

• Non-asymptotic Statistical and Computational Guarantees of Reinforcement Learning Algorithms, Goldsmith Lecture, IEEE East Asian School of Information Theory, 2021.

• Nonconvex Low-Rank Matrix Estimation: Geometry, Robustness, and Acceleration, SIAM Conference on Imaging Science, 2020. [Video]

• Geometry and Regularization in Nonconvex Low-Rank Estimation, Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, 2019.

Selected Research Talks

• Taming the Sim-to-Real Gap in Reinforcement Learning, 2024.

• From Single-agent to Federated Reinforcement Learning, 2024.

• 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.

• Policy Optimization in Reinforcement Learning: A Tale of Preconditioning and Regularization, 2021.

• Implicit Regularization in Nonconvex Statistical Estimation, 2018.

• Exploiting Geometry for High-Resolution Source Localization, 2017.

• Recent Progress on Algorithmic Phase Retrieval, 2017.

• Solving Corrupted Systems of Quadratic Equations, Provably, 2016.

• Covariance Sketching via Quadratic Sampling, 2015.

• Sparse Parameter Estimation: Compressed Sensing meets Matrix Pencil, 2014.