Haupt, Jarvis

11 publications

TMLR 2025 Early Directional Convergence in Deep Homogeneous Neural Networks for Small Initializations Akshay Kumar, Jarvis Haupt

JMLR 2025 Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin Akshay Kumar, Jarvis Haupt

TMLR 2024 Directional Convergence near Small Initializations and Saddles in Two-Homogeneous Neural Networks Akshay Kumar, Jarvis Haupt

NeurIPS 2020 Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

ICLR 2019 NOODL: Provable Online Dictionary Learning and Sparse Coding Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

AISTATS 2019 On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition Zhehui Chen, Xingguo Li, Lin Yang, Jarvis Haupt, Tuo Zhao

UAI 2019 On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About Its Nonsmooth Loss Function Xinguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao

NeurIPS 2017 Near Optimal Sketching of Low-Rank Tensor Regression Xingguo Li, Jarvis Haupt, David Woodruff

NeurIPS 2017 On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning Xingguo Li, Lin Yang, Jason Ge, Jarvis Haupt, Tong Zhang, Tuo Zhao

ICML 2016 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt

AISTATS 2009 Distilled Sensing: Selective Sampling for Sparse Signal Recovery Jarvis Haupt, Rui Castro, Robert Nowak