Last ten or so pubs of Manfred K. Warmuth
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Last ten or so pubs of Manfred K. Warmuth

Robust Bi-Tempered Logistic Loss Based on Bregman
Divergences
arXiv

Divergence-Based Motivation for Online EM and Combining
Hidden Variable Models
arXiv

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

To appear in ICALP 2019

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

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
NIPS18 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
[NIPS17 paper] [poster]

Online dynamic programming
[NIPS17 paper]