Manfred K. Warmuth

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All published works

Action	Title	Year	Authors
+ PDF Chat	Optimal Transport with Tempered Exponential Measures	2024	Ehsan Amid Frank Nielsen Richard Nock Manfred K. Warmuth
+ PDF Chat	Noise misleads rotation invariant algorithms on sparse targets	2024	Manfred K. Warmuth Wojciech Kotłowski Matt Jones Ehsan Amid
+ PDF Chat	Tempered Calculus for ML: Application to Hyperbolic Model Embedding	2024	Richard Nock Ehsan Amid Frank Nielsen Alexander Soen Manfred K. Warmuth
+	A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks	2023	Jacob Abernethy Alekh Agarwal Teodor V. Marinov Manfred K. Warmuth
+	Boosting with Tempered Exponential Measures	2023	Richard Nock Ehsan Amid Manfred K. Warmuth
+	Optimal Transport with Tempered Exponential Measures	2023	Ehsan Amid Frank Nielsen Richard Nock Manfred K. Warmuth
+	The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs	2023	Ehsan Amid Frank Nielsen Richard Nock Manfred K. Warmuth
+	Unlabeled sample compression schemes and corner peelings for ample and maximum classes	2022	Jérémie Chalopin Victor Chepoi Shay Moran Manfred K. Warmuth
+	Step-size Adaptation Using Exponentiated Gradient Updates	2022	Ehsan Amid Rohan Anil Christopher Fifty Manfred K. Warmuth
+	Learning from Randomly Initialized Neural Network Features	2022	Ehsan Amid Rohan Anil Wojciech Kotłowski Manfred K. Warmuth
+	Layerwise Bregman Representation Learning with Applications to Knowledge Distillation	2022	Ehsan Amid Rohan Anil Christopher Fifty Manfred K. Warmuth
+	Clustering above Exponential Families with Tempered Exponential Measures	2022	Ehsan Amid Richard Nock Manfred K. Warmuth
+	LocoProp: Enhancing BackProp via Local Loss Optimization	2021	Ehsan Amid Rohan Anil Manfred K. Warmuth
+	Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond	2021	Negin Majidi Ehsan Amid Hossein Talebi Manfred K. Warmuth
+	LocoProp: Enhancing BackProp via Local Loss Optimization	2021	Ehsan Amid Rohan Anil Manfred K. Warmuth
+	Rank-smoothed Pairwise Learning In Perceptual Quality Assessment	2020	Hossein Talebi Ehsan Amid Peyman Milanfar Manfred K. Warmuth
+ PDF Chat	Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment	2020	Hossein Talebi Ehsan Amid Peyman Milanfar Manfred K. Warmuth
+ PDF Chat	An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint	2020	Ehsan Amid Manfred K. Warmuth
+	Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent.	2020	Ehsan Amid Manfred K. Warmuth
+	Reparameterizing Mirror Descent as Gradient Descent	2020	Ehsan Amid Manfred K. Warmuth
+	A case where a spindly two-layer linear network whips any neural network with a fully connected input layer	2020	Manfred K. Warmuth Wojciech Kotłowski Ehsan Amid
+	TriMap: Large-scale Dimensionality Reduction Using Triplets	2019	Ehsan Amid Manfred K. Warmuth
+	An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint	2019	Ehsan Amid Manfred K. Warmuth
+	Unbiased estimators for random design regression	2019	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression.	2019	Michał Dereziński Kenneth L. Clarkson Michael W. Mahoney Manfred K. Warmuth
+	Robust Bi-Tempered Logistic Loss Based on Bregman Divergences	2019	Ehsan Amid Manfred K. Warmuth Rohan Anil Tomer Koren
+	Two-temperature logistic regression based on the Tsallis divergence	2019	Ehsan Amid Manfred K. Warmuth Sriram Srinivasan
+	Correcting the bias in least squares regression with volume-rescaled sampling	2019	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Divergence-Based Motivation for Online EM and Combining Hidden Variable Models	2019	Ehsan Amid Manfred K. Warmuth
+	Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression	2019	Michał Dereziński Kenneth L. Clarkson Michael W. Mahoney Manfred K. Warmuth
+	Adaptive scale-invariant online algorithms for learning linear models	2019	Michał Kempka Wojciech Kotłowski Manfred K. Warmuth
+	Divergence-Based Motivation for Online EM and Combining Hidden Variable Models.	2019	Ehsan Amid Manfred K. Warmuth
+	TriMap: Large-scale Dimensionality Reduction Using Triplets	2019	Ehsan Amid Manfred K. Warmuth
+	An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint	2019	Ehsan Amid Manfred K. Warmuth
+	Unbiased estimators for random design regression	2019	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Divergence-Based Motivation for Online EM and Combining Hidden Variable Models	2019	Ehsan Amid Manfred K. Warmuth
+	Robust Bi-Tempered Logistic Loss Based on Bregman Divergences	2019	Ehsan Amid Manfred K. Warmuth Rohan Anil Tomer Koren
+	A More Globally Accurate Dimensionality Reduction Method Using Triplets	2018	Ehsan Amid Manfred K. Warmuth
+	Reverse iterative volume sampling for linear regression	2018	Michał Dereziński Manfred K. Warmuth
+	Tail bounds for volume sampled linear regression.	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Leveraged volume sampling for linear regression	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Speech Recognition: Keyword Spotting Through Image Recognition	2018	Sanjay Krishna Gouda Salil Kanetkar David C. Harrison Manfred K. Warmuth
+	Online Non-Additive Path Learning under Full and Partial Information	2018	Corinna Cortes Vitaly Kuznetsov Mehryar Mohri Holakou Rahmanian Manfred K. Warmuth
+	Correcting the bias in least squares regression with volume-rescaled sampling	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Leveraged volume sampling for linear regression	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Reverse iterative volume sampling for linear regression	2018	Michał Dereziński Manfred K. Warmuth
+	A more globally accurate dimensionality reduction method using triplets	2018	Ehsan Amid Manfred K. Warmuth
+	Subsampling for Ridge Regression via Regularized Volume Sampling	2017	Michał Dereziński Manfred K. Warmuth
+	Online Dynamic Programming	2017	Holakou Rahmanian Manfred K. Warmuth
+	Two-temperature logistic regression based on the Tsallis divergence.	2017	Ehsan Amid Manfred K. Warmuth Sriram Srinivasan
+	Unbiased estimates for linear regression via volume sampling	2017	Michał Dereziński Manfred K. Warmuth
+	Unbiased estimates for linear regression via volume sampling	2017	Michał Dereziński Manfred K. Warmuth
+	Subsampling for Ridge Regression via Regularized Volume Sampling	2017	Michał Dereziński Manfred K. Warmuth
+	Two-temperature logistic regression based on the Tsallis divergence	2017	Ehsan Amid Manfred K. Warmuth Sriram Srinivasan
+	Online Dynamic Programming	2017	Holakou Rahmanian Manfred K. Warmuth
+	t-Exponential Triplet Embedding.	2016	Ehsan Amid Nikos Vlassis Manfred K. Warmuth
+	Low-dimensional Data Embedding via Robust Ranking	2016	Ehsan Amid Nikos Vlassis Manfred K. Warmuth
+ PDF Chat	Labeled Compression Schemes for Extremal Classes	2016	Shay Moran Manfred K. Warmuth
+	Low-dimensional Data Embedding via Robust Ranking	2016	Ehsan Amid Nikos Vlassis Manfred K. Warmuth
+	PCA with Gaussian perturbations	2015	Wojciech Kotłowski Manfred K. Warmuth
+	PCA with Gaussian perturbations	2015	Wojciech Kotłowski Manfred K. Warmuth
+	Labeled compression schemes for extremal classes	2015	Shay Moran Manfred K. Warmuth
+	A Bayesian Probability Calculus for Density Matrices	2014	Manfred K. Warmuth Dima Kuzmin
+	A Bayesian Probability Calculus for Density Matrices	2014	Manfred K. Warmuth Dima Kuzmin
+ PDF Chat	Online PCA with Optimal Regrets	2013	Jiazhong Nie Wojciech Kotłowski Manfred K. Warmuth
+	On-line PCA with Optimal Regrets	2013	Jiazhong Nie Wojciech Kotłowski Manfred K. Warmuth
+	Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributions	2013	Katy S. Azoury Manfred K. Warmuth
+	Using Experts for Predicting Continuous Outcomes	2010	J. Kivinen Manfred K. Warmuth
+	On-line variance minimization in O(n2) per trial?	2010	Elad Hazan Satyen Kale Manfred K. Warmuth
+ PDF Chat	Bayesian generalized probability calculus for density matrices	2009	Manfred K. Warmuth Dima Kuzmin
+	Bayesian Generalized Probability Calculus for Density Matrices	2009	Manfred K. Warmuth Dima Kuzmin
+	Winnowing subspaces	2007	Manfred K. Warmuth
+	When Is There a Free Matrix Lunch?	2007	Manfred K. Warmuth
+ PDF Chat	None	2001	Katy S. Azoury Manfred K. Warmuth
+ PDF Chat	On the worst-case analysis of temporal-difference learning algorithms	1996	Robert E. Schapire Manfred K. Warmuth
+	Bounds on approximate steepest descent for likelihood maximization in exponential families	1994	Nicolò Cesa‐Bianchi Anders Krogh Manfred K. Warmuth
+	WORST-CASE QUADRATIC LOSS BOUNDS FOR ON-LINE PREDICTION OF LINEAR FUNCTIONS BY GRADIENT DESCENT	1993	Nicolò Cesa‐Bianchi Philip M. Long Manfred K. Warmuth
+	Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule	1993	Nicolò Cesa‐Bianchi Philip M. Long Manfred K. Warmuth
+ PDF Chat	The Weighted Majority Algorithm	1989	Nick Littlestone Manfred K. Warmuth
+	On the Complexity of Iterated Shuffle ; CU-CS-201-81	1981	Manfred K. Warmuth David Haussler

Common Coauthors

Coauthor	Papers Together
Ehsan Amid	35
Michał Dereziński	15
Wojciech Kotłowski	8
Daniel Hsu	7
Rohan Anil	7
Richard Nock	6
Dima Kuzmin	4
Frank Nielsen	4
Nicolò Cesa‐Bianchi	3
Nikos Vlassis	3
Shay Moran	3
Holakou Rahmanian	3
Hossein Talebi	3
Katy S. Azoury	2
Tomer Koren	2
Michael W. Mahoney	2
Sriram Srinivasan	2
Kenneth L. Clarkson	2
Jiazhong Nie	2
Christopher Fifty	2
Peyman Milanfar	2
Philip M. Long	2
Matt Jones	1
Nick Littlestone	1
Satyen Kale	1
Corinna Cortes	1
Salil Kanetkar	1
Sanjay Krishna Gouda	1
Jacob Abernethy	1
Elad Hazan	1
Teodor V. Marinov	1
Alexander Soen	1
David C. Harrison	1
Victor Chepoi	1
Alekh Agarwal	1
Jérémie Chalopin	1
Vitaly Kuznetsov	1
Ehsan Amid	1
Mehryar Mohri	1
J. Kivinen	1
Negin Majidi	1
Anders Krogh	1
Michał Kempka	1
Robert E. Schapire	1
David Haussler	1
Sriram Srinivasan	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming	1967	L.M. Bregman	6
+	Matrix approximation and projective clustering via volume sampling	2006	Amit Deshpande Luis Rademacher Santosh Vempala Grant Wang	6
+ PDF Chat	User-Friendly Tail Bounds for Sums of Random Matrices	2011	Joel A. Tropp	6
+	Winnowing subspaces	2007	Manfred K. Warmuth	4
+	Measures on the Closed Subspaces of a Hilbert Space	1975	Andrew M. Gleason	4
+ PDF Chat	Quantum extension of conditional probability	1999	Nicolas J. Cerf Christoph Adami	4
+ PDF Chat	Randomized Algorithms for Matrices and Data	2012	Michael W. Mahoney	4
+ PDF Chat	Deformed exponentials and logarithms in generalized thermostatistics	2002	Jan Naudts	4
+	WORST-CASE QUADRATIC LOSS BOUNDS FOR ON-LINE PREDICTION OF LINEAR FUNCTIONS BY GRADIENT DESCENT	1993	Nicolò Cesa‐Bianchi Philip M. Long Manfred K. Warmuth	4
+ PDF Chat	Gleason-Type Derivations of the Quantum Probability Rule for Generalized Measurements	2004	Carlton M. Caves Christopher A. Fuchs Kiran K. Manne Joseph M. Renes	4
+ PDF Chat	Efficient Volume Sampling for Row/Column Subset Selection	2010	Amit Deshpande Luis Rademacher	4
+	Fast approximation of matrix coherence and statistical leverage	2012	Petros Drineas Malik Magdon‐Ismail Michael W. Mahoney David P. Woodruff	4
+	Linear combination of transformations	2002	Marc Alexa	4
+ PDF Chat	Quantum Bayes rule	2001	Rüdiger Schack Todd A. Brun Carlton M. Caves	3
+ PDF Chat	None	2001	Katy S. Azoury Manfred K. Warmuth	3
+ PDF Chat	Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities	2010	Andrzej Cichocki Шун-ичи Амари	3
+ PDF Chat	Stochastic optimization for PCA and PLS	2012	Raman Arora Andrew Cotter Karen Livescu Nathan Srebro	3
+	A geometric property of the least squares solution of linear equations	1990	Aharon Ben‐Tal Marc Teboulle	3
+ PDF Chat	Learned perceptual image enhancement	2018	Hossein Talebi Peyman Milanfar	3
+	t-logistic regression	2010	Nan Ding S. V. N. Vishwanathan	3
+	Online convex programming and generalized infinitesimal gradient ascent	2003	Martin Zinkevich	3
+ PDF Chat	NIMA: Neural Image Assessment	2018	Hossein Talebi Peyman Milanfar	3
+	Unbiased estimates for linear regression via volume sampling	2017	Michał Dereziński Manfred K. Warmuth	3
+	Subsampling for Ridge Regression via Regularized Volume Sampling	2017	Michał Dereziński Manfred K. Warmuth	3
+ PDF Chat	Faster Subset Selection for Matrices and Applications	2013	Haim Avron Christos Boutsidis	3
+	Leveraged volume sampling for linear regression	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu	3
+ PDF Chat	Determinantal Processes and Independence	2006	J. Hough Manjunath Krishnapur Yuval Peres Bálint Virág	2
+ PDF Chat	Sample Compression Schemes for VC Classes	2016	Shay Moran Amir Yehudayoff	2
+	How to be Fair and Diverse	2016	L. Elisa Celis Amit Deshpande Tarun Kathuria Nisheeth K. Vishnoi	2
+	A density-free approach to the matrix variate beta distribution	1970	Sujit Kumar Mitra	2
+ PDF Chat	Rethinking the Inception Architecture for Computer Vision	2016	Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens Zbigniew Wojna	2
+	Shattering Extremal Systems	2012	Shay Moran Kurt Mehlhorn Ami Litman	2
+	Fast randomized kernel ridge regression with statistical guarantees	2015	A. El Alaoui Michael W. Mahoney	2
+	Sketching as a Tool for Numerical Linear Algebra	2014	David P. Woodruff	2
+	Recursive Parameter Estimation Using Incomplete Data	1984	D. M. Titterington	2
+	A geometric approach to sample compression	2012	Benjamin I. P. Rubinstein J Rubinstein	2
+	Stochastic Optimization of PCA with Capped MSG	2013	Raman Arora Andy Cotter Nati Srebro	2
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	Fast Determinantal Point Process Sampling with Application to Clustering	2013	Byungkon Kang	2
+ PDF Chat	Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions	2011	Nathan Halko Per‐Gunnar Martinsson Joel A. Tropp	2
+ PDF Chat	Relative-Error $CUR$ Matrix Decompositions	2008	Petros Drineas Michael W. Mahoney S. Muthukrishnan	2
+	EM Algorithms for PCA and SPCA	1997	Sam T. Roweis	2
+ PDF Chat	On-Line Expectation–Maximization Algorithm for latent Data Models	2009	Olivier Cappé Éric Moulines	2
+	A combinatorial, primal-dual approach to semidefinite programs	2007	Sanjeev Arora Satyen Kale	2
+ PDF Chat	Determinantal Point Processes for Machine Learning	2012	Alex Kulesza	2
+ PDF Chat	Improved second-order bounds for prediction with expert advice	2006	Nicolò Cesa‐Bianchi Yishay Mansour Gilles Stoltz	2
+ PDF Chat	Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition	2015	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	None	2001	Lars Holst	2
+ PDF Chat	Ranking individuals by group comparisons	2006	Tzu-Kuo Huang Chih‐Jen Lin Ruby C. Weng	2
+	An iterative row-action method for interval convex programming	1981	Yair Censor Arnold Lent	2