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

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

Adaptive scale-invariant online algorithms for learning
linear models
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
arXiv

Two-temperature logistic regression based on the Tsallis
divergence, to appear in AISTATS19,
pdf

Correcting the bias in least squares regression with
volume-rescaled sampling, to appear in AISTATS19,
arXiv

Online Non-Additive Path Learning under Full and Partial
Information, to appear in ALT19,
arXiv

Leveraged volume sampling for linear regression. To
appear in NIPS18
NIPS18 paper

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]

Labeled compression schemes for extremal classes
[ALT16 paper]

Minimax Fixed-Design Linear Regression
[COLT15 paper]

Online Learning Algorithms for Path Experts with Non-Additive Losses
[COLT15 paper]

Online Sabotaged Shortest Path
[COLT15 open problem]

The blessing and the curse of the multiplicative updates
[video]
[slides]
[book chapter]