You can find representative papers categorized by topics under Research. My Google Scholar and Arxiv profiles sometimes are more uptodate. Accompanying codes can be downloaded next to the link of the papers when available.
Preprints
Provably Robust ScoreBased Diffusion Posterior Sampling for PlugandPlay Image Reconstruction [Arxiv]
X. Xu and Y. Chi, preprint.
Transformers Provably Learn FeaturePosition Correlations in Masked Image Modeling [Arxiv]
Y. Huang*, Z. Wen*, Y. Chi, and Y. Liang, preprint.
Promptprompted Mixture of Experts for Efficient LLM Generation [Arxiv]
H. Dong, B. Chen, and Y. Chi, preprint.
Beyond Expectations: Learning with Stochastic Dominance Made Practical [Arxiv]
S. Cen, J. Mei, H. Dai, D. Schuurmans, Y. Chi, and B. Dai, preprint.
Federated Natural Policy Gradient Methods for Multitask Reinforcement Learning [Arxiv]
T. Yang, S. Cen, Y. Wei, Y. Chen, and Y. Chi, preprint.
Towards Faster NonAsymptotic Convergence for DiffusionBased Generative Models [Arxiv]
G. Li, Y. Wei, Y. Chen, and Y. Chi, preprint. Short version at ICLR 2024.
A MultiToken Coordinate Descent Method for SemiDecentralized Vertical Federated Learning [Arxiv]
P. Valdeira, Y. Chi, C. Soares, and J. Xavier, preprint. Short version at FLNeurIPS 2022 Workshop.
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression [Arxiv]
B. Li and Y. Chi, preprint.
Fast Computation of Optimal Transport via EntropyRegularized Extragradient Methods [Arxiv]
G. Li, Y. Chen, Y. Chi, H. V. Poor, and Y. Chen, preprint.
Distributionally Robust ModelBased Offline Reinforcement Learning with NearOptimal Sample Complexity [Arxiv] [Code]
L. Shi and Y. Chi, Journal of Machine Learning Research, under revision.
Published
2024+
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes [Arxiv]
H. Wang, L. Shi, and Y. Chi, Reinforcement Learning Conference (RLC), 2024.
Federated Offline Reinforcement Learning: Collaborative SinglePolicy Coverage Suffices [Arxiv]
J. Woo, L. Shi, G. Joshi, and Y. Chi, International Conference on Machine Learning (ICML), 2024.
SampleEfficient Robust MultiAgent 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 ScoreBased Diffusion Models, Provably [Arxiv]
G. Li*, Y. Huang*, T. Efimov, Y. Wei, Y. Chi, and Y. Chen, International Conference on Machine Learning (ICML), 2024.
Highprobability 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, accepted.
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.
CommunicationEfficient Federated Optimization over SemiDecentralized Networks [Arxiv] [Invited Paper]
H. Wang and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024.

Settling the Sample Complexity of ModelBased Offline Reinforcement Learning [Arxiv]
G. Li, L. Shi, Y. Chen, Y. Chi and Y. Wei, The Annals of Statistics, vol. 52, no. 1, pp. 233260, 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. 148, 2024. Short version at NeurIPS 2021.

Is QLearning 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. 222236, 2024. Short version at ICML 2021.

Breaking the Sample Size Barrier in ModelBased Reinforcement Learning with a Generative Model [Arxiv]
G. Li, Y. Wei, Y. Chi, and Y. Chen, Operations Research, vol. 72, no. 1, pp. 203221, 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.
Rewardagnostic Finetuning: 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. 17161758, 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. 707802, 2023. Short version at COLT 2021.
Offline Reinforcement Learning with OnPolicy QFunction Regularization [Arxiv]
L. Shi, R. Dadashi, Y. Chi, P. S. Castro, and M. Geist, European Conference on Machine Learning (ECML), 2023.
The Blessing of Heterogeneity in Federated QLearning: Linear Speedup and Beyond [Arxiv]
J. Woo, G. Joshi, and Y. Chi, International Conference on Machine Learning (ICML), 2023.
The Power of Preconditioning in Overparameterized LowRank 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. 10611091, 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 LowPass Measurements [Arxiv]
M. Ferreira Da Costa and Y. Chi, IEEE Journal on Selected Areas in Information Theory, vol. 4, pp. 115, 2023.
Deep Unfolded Tensor Robust PCA with Selfsupervised 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 ZeroSum Multiagent Games [Arxiv]
R. Ao, S. Cen, and Y. Chi, International Conference on Learning Representations (ICLR), 2023. (Authors are listed alphabetically.)
Faster Lastiterate Convergence of Policy Optimization in ZeroSum 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 RegretOptimal Modelfree 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. 9691043, 2023. Short version at NeurIPS 2021 as a spotlight presentation.
2022

MinimaxOptimal MultiAgent 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: ComputationOptimal and CommunicationEfficient Decentralized Nonconvex FiniteSum Optimization [Arxiv] [Code]
B. Li, Z. Li, and Y. Chi, SIAM Journal on Mathematics of Data Science, vol. 4, no. 3, pp. 10311051, 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. 25632578, 2022.
INFORMS George Nicholson Student Paper Competition Finalist
A Large Collection of Realworld Pediatric Sleep Studies [Arxiv] [NSRR] [PhysioNet] [Code]
H. Lee, B. Li, S. DeForte, M. Splaingard, Y. Huang, Y. Chi, and S. Linwood, Scientific Data, 2022.
This work curates (perhaps) the largest pediatric clinical sleep studies dataset todate.
Pessimistic QLearning 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 LowRank Tensor Estimation from Incomplete Measurements [Arxiv] [Code]
T. Tong, C. Ma, A. PraterBennette, E. Tripp, and Y. Chi, Journal of Machine Learning Research, vol. 23, no. 163, pp. 177, 2022. Short version at AISTATS 2022.

Sample Complexity of Asynchronous QLearning:
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. 448473, 2022. Short version at NeurIPS 2020.
Independent Natural Policy Gradient Methods for Potential Games: Finitetime 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 CommunicationEfficient 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 Perstep MetaQLearning
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 MultiAgent Deep Reinforcement Learning
Y. Zhang, H. Tong, Y. Xia, Y. Zhu, Y. Chi, and L. Ying, AAAI Conference on Artificial Intelligence (AAAI), 2022.

PrivacyPreserving Federated Multitask Linear Regression: A OneShot 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 IllConditioned Robust LowRank 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. 566806, 2021.
SampleEfficient 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 IllConditioned LowRank 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. 163, 2021.

Manifold Gradient Descent Solves Multichannel Sparse Blind Deconvolution Provably and Efficiently [Arxiv] [Slides]
L. Shi and Y. Chi, IEEE Trans. on Information Theory, vol. 67, no. 7, pp. 47844811, 2021. Short version at ICASSP 2020.
LowRank 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. 23962409, 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. 944967, 2021.

Nonconvex Matrix Factorization from RankOne Measurements [Arxiv]
Y. Li, C. Ma, Y. Chen, and Y. Chi, IEEE Trans. on Information Theory, vol. 67, no. 3, pp. 19281950, 2021. Short version at AISTATS 2019.

Beyond Procrustes: Balancingfree Gradient Descent for Asymmetric LowRank Matrix Sensing [Arxiv]
C. Ma, Y. Li, and Y. Chi, IEEE Trans. on Signal Processing, vol. 69, pp. 867877, 2021. Short version at Asilomar 2019.
Compressed SuperResolution of Positive Sources [Arxiv]
M. Ferreira Da Costa and Y. Chi, IEEE Signal Processing Letters, vol. 28, pp. 5660, 2021.

PlugandPlay Image Reconstruction Meets Stochastic VarianceReduced 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. 3957, 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. 30983121, 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. 72377252, 2020. Short version at CISS 2020.

CommunicationEfficient 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. 151, 2020. Short version at AISTATS 2020.

Learning Latent Features with Pairwise Penalties in LowRank Matrix Completion [Arxiv]
K. Ji, J. Tan, J. Xu, and Y. Chi, IEEE Trans. on Signal Processing, vol. 68, pp. 42104225, 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. 39763989, 2020.

Guaranteed Recovery of OneHiddenLayer Neural Networks via Cross Entropy [Arxiv]
H. Fu, Y. Chi, and Y. Liang, IEEE Trans. on Signal Processing, vol. 68, pp. 32253235, 2020. Short version at ISIT 2019.

Nonconvex LowRank Matrix Recovery with Arbitrary Outliers via MedianTruncated 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. 289325, 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. 451632, 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.
VectorValued Graph Trend Filtering with NonConvex Penalties [Arxiv]
[Code]
R. Varma*, H. Lee*, J. Kovacevic and Y. Chi, IEEE Trans. on Signal Processing over Networks, vol. 6, no. 1, pp. 4862, 2020. Short version at ICASSP 2019. (*=equal contribution)
2019

Nonconvex Optimization Meets LowRank Matrix Factorization: An Overview [Arxiv] [Slides]
Y. Chi, Y. M. Lu, and Y. Chen, IEEE Trans. on Signal Processing, vol. 67, no. 20, pp. 52395269, 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. 537, 2019. (Authors are listed alphabetically.)

MedianTruncated 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.

LowRank Structured Covariance Matrix Estimation [PDF] [Code]
A. P. Shikhaliev, L. C. Potter and Y. Chi, IEEE Signal Processing Letters, vol. 26, no. 5, pp. 700704, 2019.

Stable Separation and SuperResolution of Mixture Models [Arxiv]
Y. Li and Y. Chi, Applied and Computational Harmonic Analysis, vol. 46, no. 1, pp. 139, 2019. Short versions at ISIT 2015 and SampTA 2015.

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

Devicefree 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 DeviceFree 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 AmplitudeBased Nonconvex Optimization
V. Monardo, Y. Li, and Y. Chi, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.
ShiftInvariant 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. 12931310, 2018.

Harnessing Structures in Big Data via Guaranteed LowRank Matrix Estimation [Arxiv]
Y. Chen and Y. Chi, IEEE Signal Processing Magazine, vol. 35, no. 4, pp. 1431, 2018.

LowRank Matrix Completion [PDF]
Y. Chi, IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 178181, 2018.

MedianTruncated 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. 72877310, 2018. Short version at ICML 2016.

Quantized Spectral Compressed Sensing: CramerRao Bounds and Recovery Algorithms [Arxiv]
H. Fu and Y. Chi, IEEE Trans. on Signal Processing, vol. 66, no. 12, pp. 32683279, 2018. Short version at Asilomar 2017.

Stochastic Approximation and MemoryLimited Subspace Tracking for Poisson Streaming Data [PDF]
L. Wang and Y. Chi, IEEE Trans. on Signal Processing, vol. 66, no. 4, pp. 10511064, 2018. Short version at CAMSAP 2017.
A Nonconvex 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. KoikeAkino, 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. 135, 2017.

Subspace Learning From Bits [Arxiv]
Y. Chi and H. Fu, IEEE Trans. on Signal Processing, vol. 65, no. 17, pp. 44294442, 2017. Short versions at GlobalSIP 2014 and Asilomar 2016.

SuperResolution Image Reconstruction for HighDensity 3D SingleMolecule Microscopy [PDF] [Code]
J. Huang, M. Sun, J. Ma and Y. Chi, IEEE Trans. on Computational Imaging, vol. 3, no. 4, pp. 763773, 2017. Short version at ISBI 2016.

LowRank Positive Semidefinite Matrix Recovery from Corrupted RankOne Measurements [Arxiv]
Y. Li, Y. Sun and Y. Chi, IEEE Trans. on Signal Processing, vol. 65, no. 2, pp. 397408, 2017. Short version at ICASSP 2016.

Convex Relaxations of Spectral Sparsity for Robust SuperResolution 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. 13841388, 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.

OfftheGrid 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 SpatialTemporal 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, CH Huang, and L. Dolecek, IEEE Trans. on Communications, vol. 63, pp. 2541  2554, 2015.

Fast Twodimensional Superresolution Image Reconstruction Algorithm for Ultrahigh 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 TwoDimensional 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 MultiChannel Samplers using Sparse Recovery [Invited Paper]
Y. Li, Y. He, Y. Chi and Y. M. Lu, IEEE International Workshop on Computational Advances in MultiSensor Adaptive Processing (CAMSAP), 2015.

ChangePoint Estimation of HighDimensional 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. 65766601, 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.

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

Analysis of Fisher Information and the CramerRao 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.

LowRank Matrix Recovery With Poisson Noise
Y. Xie, Y. Chi and R. Calderbank, IEEE GlobalSIP Symposium on LowDimensional 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 SpectrallyEfficient MultiUser MIMOOFDM Systems
Y. Chi, A. Gomaa, N. AlDhahir and R. Calderbank. IEEE Trans. on Wireless Communications, vol. 10, pp. 2234  2245, 2011.

CoherenceBased 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
