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Tengyuan Liang

University of Chicago · US
Area of research
Statistics and Probability · Artificial Intelligence
Research interest
Research interests include Statistical Methods and Inference, Sparse and Compressive Sensing Techniques, Stochastic Gradient Optimization Techniques, and Machine Learning and Algorithms.
h-index
18
citations
1,012
works
77
NIH funding
primary concept
email

Recent publications

A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers
The Annals of Statistics 2022cited by 22position: firstdoi
Training Neural Networks as Learning Data-Adaptive Kernels: Provable Representation and Approximation Benefits
Figshare 2020cited by 29position: lastdoi
Statistical Inference for the Population Landscape via Moment-Adjusted Stochastic Gradients
Journal of the Royal Statistical Society Series B (Statistical Methodology) 2019cited by 18position: firstdoi
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
DSpace@MIT (Massachusetts Institute of Technology) 2017cited by 36position: first

Grants

CAREER: New Statistical Paradigms Reconciling Empirical Surprises in Modern Machine Learning
NSF2042473$400,0002021–2026PIRePORTER

Frequent collaborators

· 1 papers (2022–2022)Tomaso Poggio · Massachusetts Institute of Technology1 papers (2017–2017) · 1 papers (2017–2017)Xialiang Dou · University of Chicago1 papers (2020–2020)Alexander Rakhlin · California University of Pennsylvania1 papers (2017–2017)Weijie Su · The First Affiliated Hospital, Sun Yat-sen University1 papers (2019–2019)