NIPS Proceedingsβ

Michael W. Mahoney

10 Papers

  • GIANT: Globally Improved Approximate Newton Method for Distributed Optimization (2018)
  • Hessian-based Analysis of Large Batch Training and Robustness to Adversaries (2018)
  • Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction (2017)
  • Feature-distributed sparse regression: a screen-and-clean approach (2016)
  • Sub-sampled Newton Methods with Non-uniform Sampling (2016)
  • Fast Randomized Kernel Ridge Regression with Statistical Guarantees (2015)
  • Semi-supervised Eigenvectors for Locally-biased Learning (2012)
  • Regularized Laplacian Estimation and Fast Eigenvector Approximation (2011)
  • CUR from a Sparse Optimization Viewpoint (2010)
  • Unsupervised Feature Selection for the $k$-means Clustering Problem (2009)