World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
7542
World Ranking
9241
National Ranking
3934

Overview

Yuejie Chi is affiliated with Carnegie Mellon University in the United States and works primarily within the field of Computer Science. Their research encompasses a range of subfields including Artificial Intelligence, Computational Mechanics, Computer Networks and Communications, Management Science and Operations Research, and Computer Vision and Pattern Recognition.

The scientist's publication record highlights a focus on topics such as Reinforcement Learning in Robotics, Sparse and Compressive Sensing Techniques, Stochastic Gradient Optimization Techniques, Advanced Bandit Algorithms Research, Privacy-Preserving Technologies in Data, Adaptive Dynamic Programming Control, and Tensor Decomposition and Applications.

Frequent coauthors collaborating with Yuejie Chi include:

  • Yuxin Chen
  • Yuting Wei
  • Laixi Shi
  • Shicong Cen
  • Cong Ma

Yuejie Chi has contributed extensively to several publication venues, with many papers appearing in:

  • arXiv (Cornell University)
  • IEEE Transactions on Signal Processing
  • IEEE Transactions on Information Theory
  • SIAM Journal on Optimization
  • Operations Research

Among the recent research works, notable papers include:

  • Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization, 2020, SIAM Journal on Optimization
  • A large collection of real-world pediatric sleep studies, 2022, Scientific Data
  • Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization, 2021, Operations Research
  • Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization, 2020, arXiv (Cornell University)
  • Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent, 2020, arXiv (Cornell University)

Their body of work contributes to advancing techniques in optimization, machine learning, and data-driven methodologies, particularly through investigations into gradient methods and matrix estimation in various application contexts.

Best Publications

  • Sensitivity to Basis Mismatch in Compressed Sensing

    Yuejie Chi;Louis L Scharf;Ali Pezeshki;A Robert Calderbank

  • Robust Spectral Compressed Sensing via Structured Matrix Completion

    Yuxin Chen;Yuejie Chi

  • Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

    Yuejie Chi;Yue M. Lu;Yuxin Chen

  • Off-the-Grid Line Spectrum Denoising and Estimation With Multiple Measurement Vectors

    Yuanxin Li;Yuejie Chi

  • Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution

    Cong Ma;Kaizheng Wang;Yuejie Chi;Yuxin Chen

  • Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval

    Yuxin Chen;Yuejie Chi;Jianqing Fan;Cong Ma

  • Spectral Compressed Sensing via Structured Matrix Completion

    Yuxin Chen;Yuejie Chi

  • Exact and Stable Covariance Estimation From Quadratic Sampling via Convex Programming

    Yuxin Chen;Yuejie Chi;Andrea J. Goldsmith

  • Compressive Two-Dimensional Harmonic Retrieval via Atomic Norm Minimization

    Yuejie Chi;Yuxin Chen

  • PETRELS: Parallel Subspace Estimation and Tracking by Recursive Least Squares From Partial Observations

    Yuejie Chi;Yonina C. Eldar;Robert Calderbank

  • Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation: Recent Theory and Fast Algorithms via Convex and Nonconvex Optimization

    Yudong Chen;Yuejie Chi

  • Guaranteed Blind Sparse Spikes Deconvolution via Lifting and Convex Optimization

    Yuejie Chi

  • Spectral Methods for Data Science: A Statistical Perspective

    Yuxin Chen;Yuejie Chi;Jianqing Fan;Cong Ma

  • Harnessing Sparsity Over the Continuum: Atomic norm minimization for superresolution

    Yuejie Chi;Maxime Ferreira Da Costa

  • A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms

    Huishuai Zhang;Yingbin Liang;Yuejie Chi

  • Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization.

    Shicong Cen;Chen Cheng;Yuxin Chen;Yuting Wei

  • Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution

    Yuejie Chi;Maxime Ferreira Da Costa

  • Sensitivity to basis mismatch in compressed sensing

    Yuejie Chi;Ali Pezeshki;Louis Scharf;Robert Calderbank

  • Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

    Yuxin Chen;Yuejie Chi;Jianqing Fan;Cong Ma

  • Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion

    Cong Ma;Kaizheng Wang;Yuejie Chi;Yuxin Chen

  • Provable non-convex phase retrieval with outliers: median truncated wirtinger flow

    Huishuai Zhang;Yuejie Chi;Yingbin Liang

  • Streaming PCA and Subspace Tracking: The Missing Data Case

    Laura Balzano;Yuejie Chi;Yue M. Lu

  • Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

    Gen Li;Yuting Wei;Yuejie Chi;Yuantao Gu

Frequent Co-Authors

Yingbin Liang
Yingbin Liang The Ohio State University
Yue M. Lu
Yue M. Lu Beijing University of Posts and Telecommunications
Louis L. Scharf
Louis L. Scharf Colorado State University
Andrea Goldsmith
Andrea Goldsmith Stony Brook University
Jianjie Ma
Jianjie Ma The Ohio State University
Naofal Al-Dhahir
Naofal Al-Dhahir The University of Texas at Dallas
Jelena Kovacevic
Jelena Kovacevic New York University
Fatih Porikli
Fatih Porikli Australian National University
H. Vincent Poor
H. Vincent Poor Princeton University
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science

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