Yuejie Chi

Published

2024+

• The Sample-Communication Complexity Trade-off in Federated Q-Learning [Arxiv]
  S. Salgia and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2024, oral presentation.

• In-Context Learning with Representations: Contextual Generalization of Trained Transformers [Arxiv]
  T. Yang, Y. Huang, Y. Liang, and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2024.

• Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction [Arxiv] [Code]
  X. Xu and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2024.

• Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning [Arxiv]
  T. Yang, S. Cen, Y. Wei, Y. Chen, and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2024.

• Learning Discrete Concepts in Latent Hierarchical Models [Arxiv]
  L. Kong, G. Chen, B. Huang, E. Xing, Y. Chi, and K. Zhang, Conference on Neural Information Processing Systems (NeurIPS), 2024.

• Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity [Arxiv] [Code]
  L. Shi and Y. Chi, Journal of Machine Learning Research, vol. 25, no. 200, pp. 1-91, 2024.

• Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation [Arxiv] [Code]
  H. Dong, B. Chen, and Y. Chi, Conference on Language Modeling (COLM), 2024.

• Communication-efficient Vertical Federated Learning via Compressed Error Feedback [Arxiv] [Invited Paper]
  P. Valdeira, J. Xavier, C. Soares, and Y. Chi, European Signal Processing Conference (EUSIPCO), 2024.

• Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes [Arxiv]
  H. Wang, L. Shi, and Y. Chi, Reinforcement Learning Journal, vol. 3, pp. 1467-1510, 2024.

• High-probability Sample Complexities for Policy Evaluation with Linear Function Approximation [Arxiv]
  G. Li*, W. Wu*, Y. Chi, C. Ma, A. Rinaldo, and Y. Wei, IEEE Trans. on Information Theory, vol. 70, no. 8, pp. 5969-5999, 2024.

• Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices [Arxiv]
  J. Woo, L. Shi, G. Joshi, and Y. Chi, International Conference on Machine Learning (ICML), 2024.

• Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty [Arxiv]
  L. Shi, E. Mazumdar, Y. Chi, and A. Wierman, International Conference on Machine Learning (ICML), 2024.

• Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference [Arxiv] [Code]
  H. Dong, X. Yang, Z. Zhang, Z. Wang, Y. Chi, and B. Chen, International Conference on Machine Learning (ICML), 2024.

• Accelerating Convergence of Score-Based Diffusion Models, Provably [Arxiv] [Code]
  G. Li*, Y. Huang*, T. Efimov, Y. Wei, Y. Chi, and Y. Chen, International Conference on Machine Learning (ICML), 2024.

• Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models [Arxiv]
  G. Li, Y. Wei, Y. Chen, and Y. Chi, International Conference on Learning Representations (ICLR), 2024.

• Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression [Arxiv]
  S. Chen, Z. Li, and Y. Chi, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

• Communication-Efficient Federated Optimization over Semi-Decentralized Networks [Arxiv] [Invited Paper]
  H. Wang and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024.

• Settling the Sample Complexity of Model-Based Offline Reinforcement Learning [Arxiv]
  G. Li, L. Shi, Y. Chen, Y. Chi and Y. Wei, The Annals of Statistics, vol. 52, no. 1, pp. 233-260, 2024.

• Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization [Arxiv] [Talk]
  S. Cen, Y. Wei, and Y. Chi, Journal of Machine Learning Research, vol. 25, no. 4, pp. 1-48, 2024. Short version at NeurIPS 2021.

• Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis [Arxiv]
  G. Li, C. Cai, Y. Chen, Y. Wei, and Y. Chi, Operations Research, vol. 72, no. 1, pp. 222-236, 2024. Short version at ICML 2021.

• Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model [Arxiv]
  G. Li, Y. Wei, Y. Chi, and Y. Chen, Operations Research, vol. 72, no. 1, pp. 203-221, 2024. Short version at NeurIPS 2020.

  2023

• A Lightweight Transformer for Faster and Robust EBSD Data Collection [Arxiv] [Code]
  H. Dong, S. Donegan, M. Shah, and Y. Chi, Scientific Reports, vol. 23, pp. 21253, 2023.

• The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model [Arxiv]
  L. Shi, G. Li, Y. Wei, Y. Chen, M. Geist, and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2023.

• Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning [Arxiv]
  G. Li*, W. Zhan*, J. D. Lee, Y. Chi, and Y. Chen, Conference on Neural Information Processing Systems (NeurIPS), 2023. (*=equal contribution)

• Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation [Arxiv] [Website]
  W. Ding*, L. Shi*, Y. Chi, and D. Zhao, Conference on Neural Information Processing Systems (NeurIPS), 2023. (*=equal contribution)

• Identification of Nonlinear Latent Hierarchical Models [Arxiv]
  L. Kong, B. Huang, F. Xie, E. Xing, Y. Chi, and K. Zhang, Conference on Neural Information Processing Systems (NeurIPS), 2023.

• Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent [Arxiv] [Code]
  H. Dong, T. Tong, C. Ma, and Y. Chi, Information and Inference: A Journal of the IMA, vol. 12, no. 3, pp. 1716-1758, 2023.

• Softmax Policy Gradient Methods Can Take Exponential Time to Converge [Arxiv]
  G. Li, Y. Wei, Y. Chi, and Y. Chen, Mathematical Programming, vol. 201, pp. 707-802, 2023. Short version at COLT 2021.

• Offline Reinforcement Learning with On-Policy Q-Function Regularization [Arxiv]
  L. Shi, R. Dadashi, Y. Chi, P. S. Castro, and M. Geist, European Conference on Machine Learning (ECML), 2023.

• The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing [Arxiv]
  X. Xu, Y. Shen, Y. Chi, and C. Ma, International Conference on Machine Learning (ICML), 2023.

• A Trajectory is Worth Three Sentences: Multimodal Transformer for Offline Reinforcement Learning
  Y. Wang, M. Xu, L. Shi, and Y. Chi, Conference on Uncertainty in Artificial Intelligence (UAI), 2023.

• Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence [Arxiv]
  W. Zhan*, S. Cen*, B. Huang, Y. Chen, J. D. Lee, and Y. Chi, SIAM Journal on Optimization, vol. 33, no. 2, pp. 1061-1091, 2023. Short version at OPT 2021 as an oral presentation. (*=equal contribution)

• Understanding Masked Autoencoders via Hierarchical Latent Variable Models [Arxiv]
  L. Kong, M. Q. Ma, G. Chen, E. Xing, Y. Chi, L.-P. Morency, and K. Zhang, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023, highlight presentation.

• Local Geometry of Nonconvex Spike Deconvolution from Low-Pass Measurements [Arxiv]
  M. Ferreira Da Costa and Y. Chi, IEEE Journal on Selected Areas in Information Theory, vol. 4, pp. 1-15, 2023.

• Deep Unfolded Tensor Robust PCA with Self-supervised Learning [Arxiv] [Code]
  H. Dong, M. Shah, S. Donegan, and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.

• Asynchronous Gradient Play in Zero-Sum Multi-agent Games [Arxiv]
  R. Ao, S. Cen, and Y. Chi, International Conference on Learning Representations (ICLR), 2023. (Authors are listed alphabetically.)

• Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games [Arxiv]
  S. Cen, Y. Chi, S. Du, and L. Xiao, International Conference on Learning Representations (ICLR), 2023. (Authors are listed alphabetically.)

• Breaking the Sample Complexity Barrier to Regret-Optimal Model-free Reinforcement Learning [Arxiv] [Talk]
  G. Li, L. Shi, Y. Chen, and Y. Chi, Information and Inference: A Journal of the IMA, vol. 12, no. 2, pp. 969-1043, 2023. Short version at NeurIPS 2021 as a spotlight presentation.

  2022

• Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model [Arxiv]
  G. Li, Y. Chi, Y. Wei, and Y. Chen, Conference on Neural Information Processing Systems (NeurIPS), 2022, oral presentation.

• BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression [Arxiv] [Code]
  H. Zhao, B. Li, Z. Li, P. Richtarik, and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2022.

• SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression [Arxiv] [Code]
  Z. Li, H. Zhao, B. Li, and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2022.

• DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization [Arxiv] [Code]
  B. Li, Z. Li, and Y. Chi, SIAM Journal on Mathematics of Data Science, vol. 4, no. 3, pp. 1031-1051, 2022. Short version at OPT 2021 as a spotlight presentation.

• Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization [Arxiv] [Slides] [Code]
  S. Cen, C. Cheng, Y. Chen, Y. Wei, and Y. Chi, Operations Research, vol. 70, no. 4, pp. 2563-2578, 2022.
  INFORMS George Nicholson Student Paper Competition Finalist

• Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity [Arxiv]
  L. Shi, G. Li, Y. Wei, Y. Chen, and Y. Chi, International Conference on Machine Learning (ICML), 2022.

• Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements [Arxiv] [Code]
  T. Tong, C. Ma, A. Prater-Bennette, E. Tripp, and Y. Chi, Journal of Machine Learning Research, vol. 23, no. 163, pp. 1-77, 2022. Short version at AISTATS 2022.

• Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction [Arxiv]
  G. Li, Y. Wei, Y. Chi, Y. Gu, and Y. Chen, IEEE Trans. on Information Theory, vol. 68, no. 1, pp. 448-473, 2022. Short version at NeurIPS 2020.

• Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization [Arxiv] [Invited Paper]
  S. Cen, F. Chen, and Y. Chi, IEEE Conference on Decision and Control (CDC), 2022.

• Harvesting Curvatures for Communication-Efficient Distributed Optimization [PDF]
  D. Cardoso, B. Li, Y. Chi, and J. Xavier, Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2022.

• Zoom Out: Abstractions for Efficient Radar Algorithms on COTS Architectures
  T. Low, Y. Chi, J. Hoe, S. Kumar, A. Prabhakara, L. Shi, U. Sridhar, N. Tukanov, C. Wang, and Y. Wu, IEEE International Symposium on Phased Array Systems and Technology (PAST), 2022.

• Active Heterogeneous Graph Neural Networks with Per-step Meta-Q-Learning
  Y. Zhang, Y. Xia, Y. Zhu, Y. Chi, L. Ying and H. Tong, IEEE International Conference on Data Mining (ICDM), 2022.

• Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning
  Y. Zhang, H. Tong, Y. Xia, Y. Zhu, Y. Chi, and L. Ying, AAAI Conference on Artificial Intelligence (AAAI), 2022.

• Privacy-Preserving Federated Multi-task Linear Regression: A One-Shot Linear Mixing Approach Inspired by Graph Regularization [PDF]
  H. Lee, A. Bertozzi, J. Kovacevic, and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022.

• Accelerating Ill-Conditioned Robust Low-Rank Tensor Regression [PDF]
  T. Tong, C. Ma, and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022.

  2021

• Spectral Methods for Data Science: A Statistical Perspective [Arxiv]
  Y. Chen, Y. Chi, J. Fan, and C. Ma, Foundation and Trends in Machine Learning, vol. 14, no. 5, pp. 566-806, 2021.

• Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting [Arxiv]
  G. Li, Y. Chen, Y. Chi, Y. Gu, and Y. Wei, Conference on Neural Information Processing Systems (NeurIPS), 2021.

• Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent [Arxiv] [Slides] [Code]
  T. Tong, C. Ma, and Y. Chi, Journal of Machine Learning Research, vol. 22, no. 150, pp. 1-63, 2021.

• Manifold Gradient Descent Solves Multi-channel Sparse Blind Deconvolution Provably and Efficiently [Arxiv] [Slides]
  L. Shi and Y. Chi, IEEE Trans. on Information Theory, vol. 67, no. 7, pp. 4784-4811, 2021. Short version at ICASSP 2020.

• Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number [Arxiv] [Slides]
  T. Tong, C. Ma, and Y. Chi, IEEE Trans. on Signal Processing, vol. 69, pp. 2396-2409, 2021. Short version received Audience Choice Award at DSLW 2021.

• Subspace Estimation from Unbalanced and Incomplete Data Matrices: $\ell_{2,\infty}$ Statistical Guarantees [Main] [Arxiv]
  C. Cai, G. Li, Y. Chi, H. V. Poor, and Y. Chen, Annals of Statistics, vol. 49, no. 2, pp. 944-967, 2021.

• Nonconvex Matrix Factorization from Rank-One Measurements [Arxiv]
  Y. Li, C. Ma, Y. Chen, and Y. Chi, IEEE Trans. on Information Theory, vol. 67, no. 3, pp. 1928-1950, 2021. Short version at AISTATS 2019.

• Beyond Procrustes: Balancing-free Gradient Descent for Asymmetric Low-Rank Matrix Sensing [Arxiv]
  C. Ma, Y. Li, and Y. Chi, IEEE Trans. on Signal Processing, vol. 69, pp. 867-877, 2021. Short version at Asilomar 2019.

• Compressed Super-Resolution of Positive Sources [Arxiv]
  M. Ferreira Da Costa and Y. Chi, IEEE Signal Processing Letters, vol. 28, pp. 56-60, 2021.

• Plug-and-Play Image Reconstruction Meets Stochastic Variance-Reduced Gradient Methods
  V. Monardo, A. Iyer, S. Donegan, M. De Graef, and Y. Chi, IEEE International Conference on Image Processing (ICIP), 2021.

  2020

• Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution [Arxiv] [Code]
  Y. Chi and M. Ferreira Da Costa, IEEE Signal Processing Magazine, vol. 37, no. 2, pp. 39-57, 2020.

• Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization [Arxiv]
  Y. Chen, Y. Chi, J. Fan, C. Ma and Y. Yan, SIAM Journal on Optimization, vol. 30, no. 4, pp. 3098-3121, 2020. (Authors are listed alphabetically.)

• On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution [Arxiv]
  M. Ferreira Da Costa and Y. Chi, IEEE Trans. on Information Theory, vol. 66, no. 11, pp. 7237-7252, 2020. Short version at CISS 2020.

• Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction [Arxiv] [Code]
  B. Li, S. Cen, Y. Chen, and Y. Chi, Journal of Machine Learning Research, vol. 21, no. 180, pp. 1-51, 2020. Short version at AISTATS 2020.

• Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion [Arxiv]
  K. Ji, J. Tan, J. Xu, and Y. Chi, IEEE Trans. on Signal Processing, vol. 68, pp. 4210-4225, 2020. Short version at SAM 2020.

• Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data [Arxiv]
  S. Cen, H. Zhang, Y. Chi, W. Chen and T.-Y. Liu, IEEE Trans. on Signal Processing, vol. 68, pp. 3976-3989, 2020.

• Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy [Arxiv]
  H. Fu, Y. Chi, and Y. Liang, IEEE Trans. on Signal Processing, vol. 68, pp. 3225-3235, 2020. Short version at ISIT 2019.

• Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent [Arxiv]
  Y. Li, Y. Chi, H. Zhang, and Y. Liang, Information and Inference: A Journal of the IMA, vol. 9, no. 2, pp. 289-325, 2020. Short version at SampTA 2017.

• Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion and Blind Deconvolution [Main] [Supplementary] [Full] [Arxiv]
  C. Ma, K. Wang, Y. Chi, and Y. Chen, Foundations of Computational Mathematics, vol. 20, pp. 451-632, 2020. Short version at ICML 2018.
  2024 SIAM Activity Group on Imaging Science Best Paper Prize

• Analytical Convergence Regions of Accelerated Gradient Descent in Nonconvex Optimization under Regularity Condition [Arxiv]
  H. Xiong, Y. Chi, B. Hu, and W. Zhang, Automatica, vol. 113, pp. 108715, 2020. Short version at MTNS 2018.

• Vector-Valued Graph Trend Filtering with Non-Convex Penalties [Arxiv] [Code]
  R. Varma*, H. Lee*, J. Kovacevic and Y. Chi, IEEE Trans. on Signal Processing over Networks, vol. 6, no. 1, pp. 48-62, 2020. Short version at ICASSP 2019. (*=equal contribution)

  2019

• Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview [Arxiv] [Slides]
  Y. Chi, Y. M. Lu, and Y. Chen, IEEE Trans. on Signal Processing, vol. 67, no. 20, pp. 5239-5269, 2019.

• Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval [Main] [Supplementary] [Arxiv]
  Y. Chen, Y. Chi, J. Fan and C. Ma, Mathematical Programming, vol. 176, no. 1, pp. 5-37, 2019. (Authors are listed alphabetically.)

• Median-Truncated Gradient Descent: A Robust and Scalable Nonconvex Approach for Signal Estimation
  Y. Chi, Y. Li, H. Zhang, and Y. Liang, Compressed Sensing and Its Applications, Springer, Birkhauser, 2019.

• Low-Rank Structured Covariance Matrix Estimation [PDF] [Code]
  A. P. Shikhaliev, L. C. Potter and Y. Chi, IEEE Signal Processing Letters, vol. 26, no. 5, pp. 700-704, 2019.

• Stable Separation and Super-Resolution of Mixture Models [Arxiv]
  Y. Li and Y. Chi, Applied and Computational Harmonic Analysis, vol. 46, no. 1, pp. 1-39, 2019. Short versions at ISIT 2015 and SampTA 2015.

• Self-Calibrated Super Resolution [PDF]
  M. Ferreira Da Costa and Y. Chi, Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2019.

• Device-free Multiple People Localization through Floor Vibration
  L. Shi, M. Mirshekari, J. Fagert, Y. Chi, H. Y. Noh, P. Zhang, and S. Pan, First ACM Workshop on Device-Free Human Sensing, 2019.

• On the Sensitivity of Spectral Initialization for Noisy Phase Retrieval [Extended]
  V. Monardo and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.

• Solving Quadratic Equations via Amplitude-Based Nonconvex Optimization
  V. Monardo, Y. Li, and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.

• Shift-Invariant Subspace Tracking with Missing Data [Invited Paper]
  M. Cho and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.

  2018

• Streaming PCA and Subspace Tracking: The Missing Data Case [Arxiv] [Code]
  L. Balzano, Y. Chi, and Y. M. Lu, Proceedings of the IEEE, vol. 106, no. 8, pp. 1293-1310, 2018.

• Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation [Arxiv]
  Y. Chen and Y. Chi, IEEE Signal Processing Magazine, vol. 35, no. 4, pp. 14-31, 2018.

• Low-Rank Matrix Completion [PDF]
  Y. Chi, IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 178-181, 2018.

• Median-Truncated Nonconvex Approach for Phase Retrieval with Outliers [Arxiv]
  H. Zhang, Y. Chi and Y. Liang, IEEE Trans. on Information Theory, vol. 64, no. 11, pp. 7287-7310, 2018. Short version at ICML 2016.

• Quantized Spectral Compressed Sensing: Cramer-Rao Bounds and Recovery Algorithms [Arxiv]
  H. Fu and Y. Chi, IEEE Trans. on Signal Processing, vol. 66, no. 12, pp. 3268-3279, 2018. Short version at Asilomar 2017.

• Stochastic Approximation and Memory-Limited Subspace Tracking for Poisson Streaming Data [PDF]
  L. Wang and Y. Chi, IEEE Trans. on Signal Processing, vol. 66, no. 4, pp. 1051-1064, 2018. Short version at CAMSAP 2017.

• A Non-convex Approach to Joint Sensor Calibration and Spectrum Estimation
  M. Cho, W. Liao, and Y. Chi, IEEE Statistical Signal Processing Workshop (SSP), 2018.

• Terahertz Imaging of Binary Reflectance with Variational Bayesian Inference
  H. Fu, P. Wang, T. Koike-Akino, P. V. Orlik, and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.

  2017

• A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms [Code]
  H. Zhang, Y. Zhou, Y. Liang and Y. Chi, Journal of Machine Learning Research, vol. 18, no. 141, pp. 1-35, 2017.

• Subspace Learning From Bits [Arxiv]
  Y. Chi and H. Fu, IEEE Trans. on Signal Processing, vol. 65, no. 17, pp. 4429-4442, 2017. Short versions at GlobalSIP 2014 and Asilomar 2016.

• Super-Resolution Image Reconstruction for High-Density 3D Single-Molecule Microscopy [PDF] [Code]
  J. Huang, M. Sun, J. Ma and Y. Chi, IEEE Trans. on Computational Imaging, vol. 3, no. 4, pp. 763-773, 2017. Short version at ISBI 2016.

• Low-Rank Positive Semidefinite Matrix Recovery from Corrupted Rank-One Measurements [Arxiv]
  Y. Li, Y. Sun and Y. Chi, IEEE Trans. on Signal Processing, vol. 65, no. 2, pp. 397-408, 2017. Short version at ICASSP 2016.

• Convex Relaxations of Spectral Sparsity for Robust Super-Resolution and Line Spectrum Estimation [Invited Paper]
  Y. Chi, SPIE Wavelets and Sparsity XVII, 2017.

• Performance Bounds for Modal Analysis using Sparse Linear Arrays
  Y. Li, A. Pezeshki, L. L. Scharf, and Y. Chi, SPIE Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 2017.

  2016

• Blind Deconvolution from Multiple Sparse Inputs [Extended]
  L. Wang and Y. Chi, IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1384-1388, 2016.

• Kaczmarz Method for Solving Quadratic Equations
  Y. Chi and Y. M. Lu, IEEE Signal Processing Letters, vol. 23, no. 9, pp. 1183 - 1187, 2016.

• Guaranteed Blind Sparse Spikes Deconvolution via Lifting and Convex Optimization [Arxiv] [Code]
  Y. Chi, IEEE Journal of Selected Topics in Signal Processing - Special Issue on Structured Matrices in Signal and Data Processing, vol. 10, no. 4, pp. 782 - 794, 2016. Short versions at Asilomar 2015 and ICASSP 2016.

• Off-the-Grid Line Spectrum Denoising and Estimation with Multiple Measurement Vectors [Arxiv] [Code]
  Y. Li and Y. Chi, IEEE Trans. on Signal Processing, vol. 64, pp. 1257 - 1269, 2016. Short versions at ICASSP 2014 and SSP 2014.

• Kronecker Covariance Sketching for Spatial-Temporal Data [Invited Paper]
  Y. Chi, European Signal Processing Conference (EUSIPCO), 2016.

  2015

• Compressed Sensing, Sparse Inversion, and Model Mismatch
  A. Pezeshki, Y. Chi, L. L. Scharf, and E. K. Chong. Compressed Sensing and Its Applications, Birkhauser, 2015.

• Orthogonal Matching Pursuit on Faulty Circuits
  Y. Li, Y. Chi, C-H Huang, and L. Dolecek, IEEE Trans. on Communications, vol. 63, pp. 2541 - 2554, 2015.

• Fast Two-dimensional Super-resolution Image Reconstruction Algorithm for Ultra-high Emitter Density [Code]
  J. Huang, K. Gumpper, Y. Chi, M. Sun and J. Ma, Optics Letters, vol. 40, pp. 2989 - 2992, 2015.

• Exact and Stable Covariance Estimation from Quadratic Sampling via Convex Programming [Arxiv]
  Y. Chen, Y. Chi and A. J. Goldsmith, IEEE Trans. on Information Theory, vol. 61, pp. 4034 - 4059, 2015. Short versions at ISIT 2014 and ICASSP 2014.

• 3D Multifocus Astigmatism and Compressed Sensing (3D MACS) Based Superresolution Reconstruction [Code]
  J. Huang, M. Sun, K. Gumpper, Y. Chi and J. Ma, Biomedical Optics Express, vol. 6, pp. 902 - 917, 2015.

• Compressive Two-Dimensional Harmonic Retrieval via Atomic Norm Minimization [Code]
  Y. Chi and Y. Chen, IEEE Trans. on Signal Processing, vol. 63, pp. 1030 - 1042, 2015. Short version at Asilomar 2013.

• Blind Calibration of Multi-Channel Samplers using Sparse Recovery [Invited Paper]
  Y. Li, Y. He, Y. Chi and Y. M. Lu, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015.

• Change-Point Estimation of High-Dimensional Streaming Data via Sketching
  Y. Chi and Y. Wu, Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2015.

• Covariance Tracking From Sketches of Rapid Data Streams
  Y. Jiang and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2015.

• Compressive Graph Clustering From Random Sketches
  Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2015.

  2014

• Robust Spectral Compressed Sensing via Structured Matrix Completion [Arxiv] [Code]
  Y. Chen and Y. Chi, IEEE Trans. on Information Theory, vol. 60, pp. 6576-6601, 2014. Short version at ICML 2013 and Best Student Paper Award Finalist at SPARS 2013.

• Classification and Boosting with Multiple Collaborative Representations
  Y. Chi and F. Porikli, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 36, pp. 1519 - 1531, 2014. Short version at CVPR 2012.

  2013

• Resolving Spatial Modes of Lasers via Matrix Completion
  Y. Chi and B. L. Anderson, Optics Letters, vol. 38, pp. 3957 - 3960, 2013.

• PETRELS: Parallel Subspace Estimation and Tracking using Recursive Least Squares from Partial Observations [Code]
  Y. Chi, Y. C. Eldar, and R. Calderbank, IEEE Trans. on Signal Processing, vol. 61, pp. 5947 - 5959, 2013. Short version received Best Student Paper Award at ICASSP 2012.

• Knowledge-Enhanced Matching Pursuit
  Y. Chi and R. Calderbank, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013.

• Analysis of Fisher Information and the Cramer-Rao Bound for Nonlinear Parameter Estimation after Compressed Sensing
  P. Pakrooh, L. L. Scharf, A. Pezeshki, and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013.

• Low-Rank Matrix Recovery With Poisson Noise
  Y. Xie, Y. Chi and R. Calderbank, IEEE GlobalSIP Symposium on Low-Dimensional Models and Optimization in Signal Processing, 2013.

• Sparse MIMO Radar Via Structured Matrix Completion [Invited Paper]
  Y. Chi, IEEE GlobalSIP Symposium on Emerging Challenges in Network Sensing, Inference, and Communication, 2013.

• Nearest Subspace Classification with Missing Data
  Y. Chi, Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2013.

  Before 2012

• Sensitivity of Basis Mismatch to Compressed Sensing
  Y. Chi, L. L. Scharf, A. Pezeshki and R. Calderbank, IEEE Trans. on Signal Processing, vol. 59, pp. 2182 - 2195, 2011. Short versions at ICASSP 2010 and DASP 2009.
  2013 IEEE Signal Processing Society Young Author Best Paper Award

• Training Signal Design and Tradeoffs for Spectrally-Efficient Multi-User MIMO-OFDM Systems
  Y. Chi, A. Gomaa, N. Al-Dhahir and R. Calderbank. IEEE Trans. on Wireless Communications, vol. 10, pp. 2234 - 2245, 2011.

• Coherence-Based Performance Guarantees for Orthogonal Matching Pursuit
  Y. Chi and R. Calderbank, Allerton Conference on Control, Communications and Computing (Allerton), 2012.

• Diagnostic Grade Wireless ECG Monitoring
  H. Garudadri, Y. Chi, S. Baker, S. Majumdar, P. K. Baheti and D. Ballard, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2011.

• Complementary Waveforms for Sidelobe Suppression and Radar Polarimetry
  Y. Chi, A. Pezeshki and R. Calderbank. Principles of Waveform Diversity and Design, SciTech Publishing, Inc., 2010.

• Regularized Blind Detection for MIMO Communications
  Y. Chi, Y. Wu and R. Calderbank, International Symposium on Information Theory (ISIT), 2010.

• Range Sidelobe Suppression in a Desired Doppler Band [Invited Paper]
  Y. Chi, A. Pezeshki, R. Calderbank and S. Howard, International Waveform Diversity & Design Conference (WDD), 2009.

Dissertation

• Exploitation of Geometry in Signal Processing and Sensing, Ph. D. Dissertation, Princeton University, Sep. 2012.