NIPS Proceedingsβ

Amit Daniely

6 Papers

  • Generalization Bounds for Neural Networks via Approximate Description Length (2019)
  • Locally Private Learning without Interaction Requires Separation (2019)
  • SGD Learns the Conjugate Kernel Class of the Network (2017)
  • Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity (2016)
  • More data speeds up training time in learning halfspaces over sparse vectors (2013)
  • Multiclass Learning Approaches: A Theoretical Comparison with Implications (2012)