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

A case where a spindly two-layer linear network whips any
neural network with a fully connected input layer
arXiv

Rank-smoothed Pairwise Learning in Perceptual Quality
Assessment.
ICIP20 paper
slides
video

Reparameterizing Mirror Descent as Gradient Descent.
NeurIIPS20 paper

See also previous version which contains more material on
matrix updates:

Interpolating Between Gradient Descent and Exponentiated Gradient
Using Reparameterized Gradient Descent
arXiv,v1

Divergence-Based Motivation for Online EM
and Combining Hidden Variable Models.
UAI20 paper
video
slides

Winnowing with Gradient Descent.
COLT20 paper
video
slides

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]