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Fingerprint Dive into the research topics where Jason Dean Lee is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 1 Similar Profiles
Neural networks Engineering & Materials Science
Lasso Mathematics
Factorization Engineering & Materials Science
Chemical activation Engineering & Materials Science
Random Projection Mathematics
Polynomials Engineering & Materials Science
Sketching Mathematics
Structure Learning Mathematics

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2019 2023

Research Output 2010 2020

  • 850 Citations
  • 14 h-Index
  • 14 Conference article
  • 12 Article
  • 8 Conference contribution
  • 6 Paper
1 Citation (Scopus)

Stochastic Subgradient Method Converges on Tame Functions

Lee, J. D., Feb 1 2020, In : Foundations of Computational Mathematics. 20, 1, p. 119-154 36 p.

Princeton University

Research output: Contribution to journalArticle

Subgradient Method
Stochastic Methods
Converge
Stratifiable
Locally Lipschitz Function
6 Citations (Scopus)

Communication-Efficient Distributed Statistical Inference

Lee, J. D., Apr 3 2019, In : Journal of the American Statistical Association. 114, 526, p. 668-681 14 p.

Research output: Contribution to journalArticle

Statistical Inference
Likelihood
Bayesian inference
High-dimensional
Markov Chain Monte Carlo Algorithms
5 Citations (Scopus)

First-order methods almost always avoid strict saddle points

Lee, J. D., Jul 1 2019, In : Mathematical Programming. 176, 1-2, p. 311-337 27 p.

Research output: Contribution to journalArticle

Saddlepoint
Initialization
Dynamical systems
Mirrors
Coordinate Descent
6 Citations (Scopus)

Theoretical insights into the optimization landscape of over-parameterized shallow neural networks

Lee, J. D., Feb 2019, In : IEEE Transactions on Information Theory. 65, 2, p. 742-769 28 p., 8409482.

Research output: Contribution to journalArticle

neural network
Neural networks
activation
Chemical activation
Gaussian distribution

Adding one neuron can eliminate all bad local minima

Lee, J. D., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 4350-4360 11 p.

Research output: Contribution to journalConference article

Neurons
Neural networks