NIPS Proceedingsβ

Nati Srebro

18 Papers

  • Exploring Generalization in Deep Learning (2017)
  • Implicit Regularization in Matrix Factorization (2017)
  • The Marginal Value of Adaptive Gradient Methods in Machine Learning (2017)
  • Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis (2016)
  • Equality of Opportunity in Supervised Learning (2016)
  • Global Optimality of Local Search for Low Rank Matrix Recovery (2016)
  • Normalized Spectral Map Synchronization (2016)
  • Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations (2016)
  • Tight Complexity Bounds for Optimizing Composite Objectives (2016)
  • Path-SGD: Path-Normalized Optimization in Deep Neural Networks (2015)
  • Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm (2014)
  • Auditing: Active Learning with Outcome-Dependent Query Costs (2013)
  • Stochastic Optimization of PCA with Capped MSG (2013)
  • The Power of Asymmetry in Binary Hashing (2013)
  • Beating SGD: Learning SVMs in Sublinear Time (2011)
  • Better Mini-Batch Algorithms via Accelerated Gradient Methods (2011)
  • Learning with the weighted trace-norm under arbitrary sampling distributions (2011)
  • On the Universality of Online Mirror Descent (2011)