Combinatorial Inference
Combinatorial inference develops novel theories on high dimensional universality phenomenon and combinatorial information-theoretical lower bounds and proposes methods to conduct the global inferential analysis on the topological properties of the graphs in the graphical models and other combinatorial structures.
Inference on the optimal assortment in the multinomial logit model
ACM Conference on Economics and Computation, 2023. |
Combinatorial-Probabilistic Trade-Off: Community Properties Test in the Stochastic Block Models
International Conference on Learning Representations (spotlight paper), 2023. |
StarTrek: Combinatorial Variable Selection with False Discovery Rate Control
Under revision, 2023. |
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model In International Conference on Machine Learning, pp. 4901-4910 |
Lagrangian Inference for Ranking Problems
Operations Research, 2022. |
ARCH: Large-scale Knowledge Graph via Aggregated Narrative Codified Health Records Analysis
Submitted, 2023. |
Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model
Submitted, 2023. |
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Combinatorial Inference for Graphical Models
(*: equal contribution) Annals of Statistics, to appear. [Arxiv] |
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Adaptive Inferential Method for Monotone Graph Invariants
Submitted, 2016. [Arxiv] [R package] ICSA 2017 Student Paper Award |
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Post-Regularization Inference for Dynamic Nonparanormal Graphical Models
Journal of Machine Learning Research, to appear. [Arxiv] |
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Graphical Fermat's Principle and Triangle-Free Graph Estimation
Under revision at Journal of Machine Learning Research, 2016. [Arxiv] |
Complex Data Inference
Modern statistical methods deal with datasets with complex structures including high-dimensionality, heterogeneity, heavy-tailness, time-dependency and so on. Complex data inference aims to develop a new generation of inferential methods like hypothesis testing, confidence intervals and false discovery control for complex datasets.
High Dimensional Inference
Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model
Submitted, 2023. |
Multi-source Learning via Completion of Block-wise
Overlapping Noisy Matrices
Under revision, 2023. |
Graph over-parameterization: Why the graph helps the training of deep graph convolutional network
Neurocomputing, 2023. |
Multimodal representation learning for predicting molecule–disease relations
Bioinformatics, 2023. |
Multiview Incomplete Knowledge Graph Integration with Application to Cross-institutional EHR Data Harmonization
(*: co-senior author) Journal of Biomedical Informatics, 2022 |
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Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs
Submitted, 2017. [Arxiv] ICSA 2017 Student Paper Award |
Heterogenous Data
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Nonparametric Heterogeneity Testing For Massive Data
Under revision at Bernoulli, 2017. [Arxiv] |
Robustness
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Robust Scatter Matrix Estimation for High Dimensional Distributions with Heavy Tails
Submitted, 2016. [PDF] |
Time Series
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Sparse Principal Component Analysis in Frequency Domain for Time Series
Submitted, 2016. [PDF] |
Complicated Algorithms Inference
Modern data analysis introduces novel estimation methods including distributed algorithms, privacy methods, kernel estimators, etc. We aims to develop inference method to handle the uncertainty assessment under these algorithms.
Distributed Data Analysis
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Distributed Testing and
Estimation under Sparse High Dimensional Models
(alphabetical order) Annals of Statistics, to appear. [Arxiv] |
Sparse Additive Model
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Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
Submitted, 2016. [Arxiv] [PDF] ASA Best Student Paper in Nonparametric Statistics Finalist |
Statistical Optimization
The nature of statistical problems brings new insights into our understanding of the geometry of optimization problems. Utilizing these insights, we are able to study the theorectial perfomance of nonconvex optimization problems and propose new algorithms.
Fast Distributed Principal Component Analysis of Large-Scale Federated Data
Submitted, 2023. |
Federated Offline Reinforcement Learning
Under revision, 2023. |
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
NeurIPS 2020. |
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model In International Conference on Machine Learning, pp. 4901-4910 |
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Provable Sparse Tensor Decomposition
Journal of the Royal Statistical Society: Series B, 2016. [Arxiv][Link] |
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Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization
Under revision at IEEE Transactions on Information Theory, 2017. [Arxiv] |
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Application of the Strictly Contractive Peaceman-Rachford Splitting Method to Multi-block Separable Convex Programming
(alphabetical order) Splitting Methods in Communication, Imaging, Science, and Engineering (In Roland Glowinski, Stanley J. Osher, Wotao Yin (Eds.)), Springer, 2017. [Optimization Online] |