Manfred K. Warmuth

Publication Activity (10 Years)

Years Active: 1984-2024
Publications (10 Years): 66

Top Venues

CoRR

COLT

AISTATS

ALT

Publications

Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth
Tempered Calculus for ML: Application to Hyperbolic Model Embedding. CoRR (2024)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
Optimal Transport with Tempered Exponential Measures. AAAI (2024)
Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. ALT (2024)
Manfred K. Warmuth, Wojciech Kotlowski, Matt Jones, Ehsan Amid
Noise misleads rotation invariant algorithms on sparse targets. CoRR (2024)
Richard Nock, Ehsan Amid, Manfred K. Warmuth
Boosting with Tempered Exponential Measures. NeurIPS (2023)
Jacob D. Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. CoRR (2023)
Manfred K. Warmuth, Ehsan Amid
Open Problem: Learning sparse linear concepts by priming the features. COLT (2023)
Richard Nock, Ehsan Amid, Manfred K. Warmuth
Boosting with Tempered Exponential Measures. CoRR (2023)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
Optimal Transport with Tempered Exponential Measures. CoRR (2023)
Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth
Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation. Trans. Mach. Learn. Res. 2023 (2023)
Ehsan Amid, Richard Nock, Manfred K. Warmuth
Clustering above Exponential Families with Tempered Exponential Measures. AISTATS (2023)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. CoRR (2023)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Unbiased estimators for random design regression. J. Mach. Learn. Res. 23 (2022)
Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. CoRR (2022)
Ehsan Amid, Richard Nock, Manfred K. Warmuth
Clustering above Exponential Families with Tempered Exponential Measures. CoRR (2022)
Ehsan Amid, Rohan Anil, Manfred K. Warmuth
LocoProp: Enhancing BackProp via Local Loss Optimization. AISTATS (2022)
Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth
Step-size Adaptation Using Exponentiated Gradient Updates. CoRR (2022)
Ehsan Amid, Rohan Anil, Wojciech Kotlowski, Manfred K. Warmuth
Learning from Randomly Initialized Neural Network Features. CoRR (2022)
Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth
Unlabeled sample compression schemes and corner peelings for ample and maximum classes. J. Comput. Syst. Sci. 127 (2022)
Manfred K. Warmuth, Wojciech Kotlowski, Ehsan Amid
A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer. ALT (2021)
Negin Majidi, Ehsan Amid, Hossein Talebi, Manfred K. Warmuth
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. CoRR (2021)
Ehsan Amid, Rohan Anil, Manfred K. Warmuth
LocoProp: Enhancing BackProp via Local Loss Optimization. CoRR (2021)
Ehsan Amid, Manfred K. Warmuth
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. UAI (2020)
Ehsan Amid, Manfred K. Warmuth
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. AAAI (2020)
Ehsan Amid, Manfred K. Warmuth
Winnowing with Gradient Descent. COLT (2020)
Manfred K. Warmuth, Wojciech Kotlowski, Ehsan Amid
A case where a spindly two-layer linear network whips any neural network with a fully connected input layer. CoRR (2020)
Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth
Rank-smoothed Pairwise Learning In Perceptual Quality Assessment. CoRR (2020)
Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth
Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment. ICIP (2020)
Ehsan Amid, Manfred K. Warmuth
Reparameterizing Mirror Descent as Gradient Descent. NeurIPS (2020)
Ehsan Amid, Manfred K. Warmuth
Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent. CoRR (2020)
Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. COLT (2019)
Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth
Adaptive scale-invariant online algorithms for learning linear models. CoRR (2019)
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth
Online Non-Additive Path Learning under Full and Partial Information. ALT (2019)
Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth
Mistake bounds on the noise-free multi-armed bandit game. Inf. Comput. 269 (2019)
Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. NeurIPS (2019)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Correcting the bias in least squares regression with volume-rescaled sampling. AISTATS (2019)
Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth
Unlabeled Sample Compression Schemes and Corner Peelings for Ample and Maximum Classes. ICALP (2019)
Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models. ICML (2019)
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth
TriMap: Large-scale Dimensionality Reduction Using Triplets. CoRR (2019)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Unbiased estimators for random design regression. CoRR (2019)
Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan
Two-temperature logistic regression based on the Tsallis divergence. AISTATS (2019)
Michal Derezinski, Manfred K. Warmuth
Reverse Iterative Volume Sampling for Linear Regression. J. Mach. Learn. Res. 19 (2018)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Tail bounds for volume sampled linear regression. CoRR (2018)
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
Leveraged volume sampling for linear regression. NeurIPS (2018)
Sanjay Krishna Gouda, Salil Kanetkar, David Harrison, Manfred K. Warmuth
Speech Recognition: Keyword Spotting Through Image Recognition. CoRR (2018)
Michal Derezinski, Manfred K. Warmuth, Daniel Hsu
Correcting the bias in least squares regression with volume-rescaled sampling. CoRR (2018)
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth
Online Non-Additive Path Learning under Full and Partial Information. CoRR (2018)
Michal Derezinski, Manfred K. Warmuth
Subsampling for Ridge Regression via Regularized Volume Sampling. AISTATS (2018)
Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth
Unlabeled sample compression schemes and corner peelings for ample and maximum classes. CoRR (2018)
Ehsan Amid, Manfred K. Warmuth
A more globally accurate dimensionality reduction method using triplets. CoRR (2018)
Michal Derezinski, Manfred K. Warmuth
Reverse iterative volume sampling for linear regression. CoRR (2018)
Holakou Rahmanian, Manfred K. Warmuth
Online Dynamic Programming. NIPS (2017)
Michal Derezinski, Manfred K. Warmuth
Unbiased estimates for linear regression via volume sampling. NIPS (2017)
Michal Derezinski, Manfred K. Warmuth
Unbiased estimates for linear regression via volume sampling. CoRR (2017)
Holakou Rahmanian, S. V. N. Vishwanathan, Manfred K. Warmuth
Online Dynamic Programming. CoRR (2017)
Ehsan Amid, Manfred K. Warmuth
Two-temperature logistic regression based on the Tsallis divergence. CoRR (2017)
Michal Derezinski, Manfred K. Warmuth
Subsampling for Ridge Regression via Regularized Volume Sampling. CoRR (2017)
Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth
Online PCA with Optimal Regret. J. Mach. Learn. Res. 17 (2016)
Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth
Noise Free Multi-armed Bandit Game. LATA (2016)
Elad Hazan, Satyen Kale, Manfred K. Warmuth
Learning rotations with little regret. Mach. Learn. 104 (1) (2016)
Ehsan Amid, Nikos Vlassis, Manfred K. Warmuth
t-Exponential Triplet Embedding. CoRR (2016)
Shay Moran, Manfred K. Warmuth
Labeled Compression Schemes for Extremal Classes. ALT (2016)
Wouter M. Koolen, Manfred K. Warmuth, Dmitry Adamskiy
Open Problem: Online Sabotaged Shortest Path. COLT (2015)
Wojciech Kotlowski, Manfred K. Warmuth
PCA with Gaussian perturbations. CoRR (2015)
Peter L. Bartlett, Wouter M. Koolen, Alan Malek, Eiji Takimoto, Manfred K. Warmuth
Minimax Fixed-Design Linear Regression. COLT (2015)
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Manfred K. Warmuth
On-Line Learning Algorithms for Path Experts with Non-Additive Losses. COLT (2015)
Shay Moran, Manfred K. Warmuth
Labeled compression schemes for extremal classes. CoRR (2015)
Manfred K. Warmuth, Dima Kuzmin
A Bayesian Probability Calculus for Density Matrices. CoRR (2014)
Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
Combining initial segments of lists. Theor. Comput. Sci. 519 (2014)
Manfred K. Warmuth, Wojciech Kotlowski, Shuisheng Zhou
Kernelization of matrix updates, when and how? Theor. Comput. Sci. 558 (2014)
Michal Derezinski, Manfred K. Warmuth
The limits of squared Euclidean distance regularization. NIPS (2014)
Manfred K. Warmuth, Wouter M. Koolen
Open Problem: Shifting Experts on Easy Data. COLT (2014)
Katy S. Azoury, Manfred K. Warmuth
Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributions CoRR (2013)
Jiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang
Open Problem: Lower bounds for Boosting with Hadamard Matrices. COLT (2013)
Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth
Online PCA with Optimal Regrets. ALT (2013)
Wouter M. Koolen, Jiazhong Nie, Manfred K. Warmuth
Learning a set of directions. COLT (2013)
Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth
On-line PCA with Optimal Regrets. CoRR (2013)
Manfred K. Warmuth, Dima Kuzmin
Online variance minimization. Mach. Learn. 87 (1) (2012)
Wouter M. Koolen, Dmitry Adamskiy, Manfred K. Warmuth
Putting Bayes to sleep. NIPS (2012)
Manfred K. Warmuth, Wojciech Kotlowski, Shuisheng Zhou
Kernelization of Matrix Updates, When and How? ALT (2012)
Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold
Combining Initial Segments of Lists. ALT (2011)
Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth
Learning Eigenvectors for Free. NIPS (2011)
Wojciech Kotlowski, Manfred K. Warmuth
Minimax Algorithm for Learning Rotations. COLT (2011)
Manfred K. Warmuth, Dima Kuzmin
Bayesian generalized probability calculus for density matrices. Mach. Learn. 78 (1-2) (2010)
Elad Hazan, Satyen Kale, Manfred K. Warmuth
Learning Rotations with Little Regret. COLT (2010)
Manfred K. Warmuth
The Blessing and the Curse of the Multiplicative Updates. Discovery Science (2010)
Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen
Hedging Structured Concepts. COLT (2010)
Shuisheng Zhou, Manfred K. Warmuth, Yinli Dong, Feng Ye
New combination coefficients for AdaBoost algorithms. ICNC (2010)
Jacob D. Abernethy, Manfred K. Warmuth
Repeated Games against Budgeted Adversaries. NIPS (2010)
Elad Hazan, Satyen Kale, Manfred K. Warmuth
On-line Variance Minimization in O(n2) per Trial? COLT (2010)
Manfred K. Warmuth
The Blessing and the Curse of the Multiplicative Updates. ALT (2010)
Jacob D. Abernethy, Manfred K. Warmuth
Minimax Games with Bandits. COLT (2009)
David P. Helmbold, Manfred K. Warmuth
Learning Permutations with Exponential Weights. J. Mach. Learn. Res. 10 (2009)
Manfred K. Warmuth, S. V. N. Vishwanathan
Tutorial summary: Survey of boosting from an optimization perspective. ICML (2009)
Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin
When Random Play is Optimal Against an Adversary. COLT (2008)
Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan
Entropy Regularized LPBoost. ALT (2008)

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Publication Activity (10 Years)

Top Topics

Top Venues

Manfred K. Warmuth