Last ten or so pubs of Manfred K. Warmuth

Last ten or so pubs of Manfred K. Warmuth



  • Rank-smoothed Pairwise Learning in Perceptual Quality Assessment. To appear in IFIP2020. prelim. version

  • Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent arXiv

  • Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. To appear in UAI20.
    prelim. arXiv version

  • Winnowing with Gradient Descent. To appear in COLT20. prelim. version

  • TriMap: Large-scale dimensionality reduction using triplets arXiv
  • Also: A more globally accurate dimensionality reduction method using triplets arXiv

  • An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. AAAI20 paper AAAI20 poster

  • Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. Neurips19 paper poster talk

  • Unlabeled sample compression schemes and corner peelings for ample and maximum classes ICALP19 paper

  • Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression COLT19 paper

  • Adaptive scale-invariant online algorithms for learning linear models ICML19 paper supplement

  • Two-temperature logistic regression based on the Tsallis divergence AISTAT19 paper supplement poster

  • Online Non-Additive Path Learning under Full and Partial Information, ALT19 paper talk

  • Unbiased estimators for random design regression. Expanded paper about the below two conference papers. journal submission

  • Correcting the bias in least squares regression with volume-rescaled sampling AISTAT19 paper supplement

  • Leveraged volume sampling for linear regression NeurIPS18 paper supplement

  • Reverse iterative volume sampling for linear regression
    Journal paper about the following 2 conference papers and more [JMLR paper]
    Long talk about volume sampling [talk]

  • Subsampling for Ridge Regression via Regularized Volume Sampling [AISTATS18 paper] [talk]

  • Unbiased estimates for linear regression via volume sampling [NeurIPS17 paper] [poster]

  • Online dynamic programming [NeurIPS17 paper]