Alexander Tsigler

Statistical Learning Theory · 2019

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Alexander Tsigler trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Roman Vershynin (in Statistics and Probability)
  2. Peter L. Bartlett (in Statistical Learning Theory)

Who trusts Alexander Tsigler

  1. Percy Liang (in Artificial Intelligence)
  2. Aditi Raghunathan (in Artificial Intelligence)

Papers from Alexander Tsigler's perspective

Ordered by how strongly the researchers Alexander Tsigler trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Gradient Descent Provably Optimizes Over-parameterized Neural Networks
  2. Introduction to the non-asymptotic analysis of random matrices
  3. The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network
  4. Gradient Descent Finds Global Minima of Deep Neural Networks
  5. Size-Independent Sample Complexity of Neural Networks
  6. Does data interpolation contradict statistical optimality?
  7. Surprises in High-Dimensional Ridgeless Least Squares Interpolation
  8. Norm-Based Capacity Control in Neural Networks
  9. To understand deep learning we need to understand kernel learning
  10. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

Their papers