NIPS Proceedingsβ

Manfred K. K. Warmuth

22 Papers

  • Robust Bi-Tempered Logistic Loss Based on Bregman Divergences (2019)
  • Leveraged volume sampling for linear regression (2018)
  • Online Dynamic Programming (2017)
  • Unbiased estimates for linear regression via volume sampling (2017)
  • The limits of squared Euclidean distance regularization (2014)
  • Putting Bayes to sleep (2012)
  • Learning Eigenvectors for Free (2011)
  • Repeated Games against Budgeted Adversaries (2010)
  • Boosting Algorithms for Maximizing the Soft Margin (2007)
  • Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension (2006)
  • A Bayes Rule for Density Matrices (2005)
  • Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection (2004)
  • Boosting versus Covering (2003)
  • Adaptive Caching by Refetching (2002)
  • Active Learning in the Drug Discovery Process (2001)
  • On the Convergence of Leveraging (2001)
  • Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy (1998)
  • Linear Hinge Loss and Average Margin (1998)
  • Relative Loss Bounds for Multidimensional Regression Problems (1997)
  • Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions (1996)
  • Exponentially many local minima for single neurons (1995)
  • Worst-case Loss Bounds for Single Neurons (1995)