Warmuth, Manfred K.

134 publications

ALT 2025 How Rotation Invariant Algorithms Are Fooled by Noise on Sparse Targets Manfred K. Warmuth, Wojciech Kot\polishlowski, Matt Jones, Ehsan Amid

ALT 2024 A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks Jacob Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth

NeurIPS 2024 Hyperbolic Embeddings of Supervised Models Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth

AAAI 2024 Optimal Transport with Tempered Exponential Measures Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth

AISTATS 2023 Clustering Above Exponential Families with Tempered Exponential Measures Ehsan Amid, Richard Nock, Manfred K. Warmuth

TMLR 2023 Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K Warmuth

COLT 2023 Open Problem: Learning Sparse Linear Concepts by Priming the Features Manfred K. Warmuth, Ehsan Amid

NeurIPSW 2022 Fishy: Layerwise Fisher Approximation for Higher-Order Neural Network Optimization Abel Peirson, Ehsan Amid, Yatong Chen, Vladimir Feinberg, Manfred K Warmuth, Rohan Anil

JMLR 2022 Unbiased Estimators for Random Design Regression Michał Dereziński, Manfred K. Warmuth, Daniel Hsu

ALT 2021 A Case Where a Spindly Two-Layer Linear Network Decisively Outperforms Any Neural Network with a Fully Connected Input Layer Manfred K. Warmuth, Wojciech Kotłowski, Ehsan Amid

AAAI 2020 An Implicit Form of Krasulina's K-PCA Update Without the Orthonormality Constraint Ehsan Amid, Manfred K. Warmuth

UAI 2020 Divergence-Based Motivation for Online EM and Combining Hidden Variable Models Ehsan Amid, Manfred K. Warmuth

NeurIPS 2020 Reparameterizing Mirror Descent as Gradient Descent Ehsan Amid, Manfred K. Warmuth

COLT 2020 Winnowing with Gradient Descent Ehsan Amid, Manfred K. Warmuth

ICML 2019 Adaptive Scale-Invariant Online Algorithms for Learning Linear Models Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth

AISTATS 2019 Correcting the Bias in Least Squares Regression with Volume-Rescaled Sampling Michal Derezinski, Manfred K. Warmuth, Daniel Hsu

COLT 2019 Minimax Experimental Design: Bridging the Gap Between Statistical and Worst-Case Approaches to Least Squares Regression Michał Dereziński, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth

NeurIPS 2019 Robust Bi-Tempered Logistic Loss Based on Bregman Divergences Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren

AISTATS 2019 Two-Temperature Logistic Regression Based on the Tsallis Divergence Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan

NeurIPS 2018 Leveraged Volume Sampling for Linear Regression Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu

JMLR 2018 Reverse Iterative Volume Sampling for Linear Regression Michał Dereziński, Manfred K. Warmuth

AISTATS 2018 Subsampling for Ridge Regression via Regularized Volume Sampling Michal Derezinski, Manfred K. Warmuth

NeurIPS 2017 Online Dynamic Programming Holakou Rahmanian, Manfred K. Warmuth

NeurIPS 2017 Unbiased Estimates for Linear Regression via Volume Sampling Michal Derezinski, Manfred K. Warmuth

ALT 2016 Labeled Compression Schemes for Extremal Classes Shay Moran, Manfred K. Warmuth

MLJ 2016 Learning Rotations with Little Regret Elad Hazan, Satyen Kale, Manfred K. Warmuth

JMLR 2016 Online PCA with Optimal Regret Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth

COLT 2015 Minimax Fixed-Design Linear Regression Peter L. Bartlett, Wouter M. Koolen, Alan Malek, Eiji Takimoto, Manfred K. Warmuth

COLT 2015 On-Line Learning Algorithms for Path Experts with Non-Additive Losses Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Manfred K. Warmuth

COLT 2015 Open Problem: Online Sabotaged Shortest Path Wouter M. Koolen, Manfred K. Warmuth, Dmitry Adamskiy

COLT 2014 Open Problem: Shifting Experts on Easy Data Manfred K. Warmuth, Wouter M. Koolen

NeurIPS 2014 The Limits of Squared Euclidean Distance Regularization Michal Derezinski, Manfred K. Warmuth

COLT 2013 Learning a Set of Directions Wouter M. Koolen, Jiazhong Nie, Manfred K. Warmuth

ALT 2013 Online PCA with Optimal Regrets Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth

COLT 2013 Open Problem: Lower Bounds for Boosting with Hadamard Matrices Jiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang

ALT 2012 Kernelization of Matrix Updates, When and How? Manfred K. Warmuth, Wojciech Kotlowski, Shuisheng Zhou

MLJ 2012 Online Variance Minimization Manfred K. Warmuth, Dima Kuzmin

NeurIPS 2012 Putting Bayes to Sleep Dmitry Adamskiy, Manfred K. Warmuth, Wouter M. Koolen

ALT 2011 Combining Initial Segments of Lists Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold

NeurIPS 2011 Learning Eigenvectors for Free Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth

COLT 2011 Minimax Algorithm for Learning Rotations Wojciech Kotłowski, Manfred K. Warmuth

MLJ 2010 Bayesian Generalized Probability Calculus for Density Matrices Manfred K. Warmuth, Dima Kuzmin

COLT 2010 Hedging Structured Concepts Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen

COLT 2010 Learning Rotations with Little Regret Elad Hazan, Satyen Kale, Manfred K. Warmuth

COLT 2010 On-Line Variance Minimization in O(n2) per Trial? Elad Hazan, Satyen Kale, Manfred K. Warmuth

NeurIPS 2010 Repeated Games Against Budgeted Adversaries Jacob D. Abernethy, Manfred K. Warmuth

ALT 2010 The Blessing and the Curse of the Multiplicative Updates Manfred K. Warmuth

JMLR 2009 Learning Permutations with Exponential Weights David P. Helmbold, Manfred K. Warmuth

COLT 2009 Minimax Games with Bandits Jacob D. Abernethy, Manfred K. Warmuth

ICML 2009 Tutorial Summary: Survey of Boosting from an Optimization Perspective Manfred K. Warmuth, S. V. N. Vishwanathan

ALT 2008 Entropy Regularized LPBoost Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan

COLT 2008 Learning Rotations Adam M. Smith, Manfred K. Warmuth

JMLR 2008 Randomized Online PCA Algorithms with Regret Bounds That Are Logarithmic in the Dimension Manfred K. Warmuth, Dima Kuzmin

COLT 2008 When Random Play Is Optimal Against an Adversary Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin

NeurIPS 2007 Boosting Algorithms for Maximizing the Soft Margin Gunnar Rätsch, Manfred K. Warmuth, Karen A. Glocer

COLT 2007 Learning Permutations with Exponential Weights David P. Helmbold, Manfred K. Warmuth

ICML 2007 Online Kernel PCA with Entropic Matrix Updates Dima Kuzmin, Manfred K. Warmuth

JMLR 2007 Unlabeled Compression Schemes for Maximum Classes Dima Kuzmin, Manfred K. Warmuth

COLT 2007 When Is There a Free Matrix Lunch? Manfred K. Warmuth

ICML 2007 Winnowing Subspaces Manfred K. Warmuth

UAI 2006 A Bayesian Probability Calculus for Density Matrices Manfred K. Warmuth, Dima Kuzmin

COLT 2006 Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints Manfred K. Warmuth

COLT 2006 Continuous Experts and the Binning Algorithm Jacob D. Abernethy, John Langford, Manfred K. Warmuth

COLT 2006 Online Variance Minimization Manfred K. Warmuth, Dima Kuzmin

NeurIPS 2006 Randomized PCA Algorithms with Regret Bounds That Are Logarithmic in the Dimension Manfred K. Warmuth, Dima Kuzmin

ICML 2006 Totally Corrective Boosting Algorithms That Maximize the Margin Manfred K. Warmuth, Jun Liao, Gunnar Rätsch

NeurIPS 2005 A Bayes Rule for Density Matrices Manfred K. Warmuth

JMLR 2005 Efficient Margin Maximizing with Boosting Gunnar Rätsch, Manfred K. Warmuth

COLT 2005 Leaving the Span Manfred K. Warmuth, S. V. N. Vishwanathan

JMLR 2005 Matrix Exponentiated Gradient Updates for On-Line Learning and Bregman Projection Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth

COLT 2005 Optimum Follow the Leader Algorithm Dima Kuzmin, Manfred K. Warmuth

COLT 2005 Unlabeled Compression Schemes for Maximum Classes, Dima Kuzmin, Manfred K. Warmuth

NeurIPS 2004 Matrix Exponential Gradient Updates for On-Line Learning and Bregman Projection Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth

COLT 2004 The Optimal PAC Algorithm Manfred K. Warmuth

NeurIPS 2003 Boosting Versus Covering Kohei Hatano, Manfred K. Warmuth

COLT 2003 Compressing to VC Dimension Many Points Manfred K. Warmuth

COLT 2003 Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings Bernhard Schölkopf, Manfred K. Warmuth

JMLR 2003 Path Kernels and Multiplicative Updates Eiji Takimoto, Manfred K. Warmuth

MLJ 2003 Relative Loss Bounds for Temporal-Difference Learning Jürgen Forster, Manfred K. Warmuth

NeurIPS 2002 Adaptive Caching by Refetching Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari

COLT 2002 Maximizing the Margin with Boosting Gunnar Rätsch, Manfred K. Warmuth

COLT 2002 Path Kernels and Multiplicative Updates Eiji Takimoto, Manfred K. Warmuth

JMLR 2002 Tracking a Small Set of Experts by Mixing past Posteriors Olivier Bousquet, Manfred K. Warmuth

NeurIPS 2001 Active Learning in the Drug Discovery Process Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen

NeurIPS 2001 On the Convergence of Leveraging Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth

MLJ 2001 Relative Loss Bounds for Multidimensional Regression Problems Jyrki Kivinen, Manfred K. Warmuth

MLJ 2001 Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions Katy S. Azoury, Manfred K. Warmuth

COLT 2001 Tracking a Small Set of Experts by Mixing past Posteriors Olivier Bousquet, Manfred K. Warmuth

JMLR 2001 Tracking the Best Linear Predictor Mark Herbster, Manfred K. Warmuth

COLT 2000 Barrier Boosting Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller

COLT 2000 Relative Expected Instantaneous Loss Bounds Jürgen Forster, Manfred K. Warmuth

ICML 2000 Relative Loss Bounds for Temporal-Difference Learning Jürgen Forster, Manfred K. Warmuth

ALT 2000 The Last-Step Minimax Algorithm Eiji Takimoto, Manfred K. Warmuth

COLT 2000 The Minimax Strategy for Gaussian Density Estimation. Pp Eiji Takimoto, Manfred K. Warmuth

COLT 1999 Boosting as Entropy Projection Jyrki Kivinen, Manfred K. Warmuth

ALT 1999 Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph Eiji Takimoto, Manfred K. Warmuth

UAI 1999 Relative Loss Bounds for On-Line Density Estirnation with the Exponential Family of Distributions Katy S. Azoury, Manfred K. Warmuth

NeurIPS 1998 Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy Yoram Singer, Manfred K. Warmuth

NeurIPS 1998 Linear Hinge Loss and Average Margin Claudio Gentile, Manfred K. Warmuth

MLJ 1998 Tracking the Best Disjunction Peter Auer, Manfred K. Warmuth

MLJ 1998 Tracking the Best Expert Mark Herbster, Manfred K. Warmuth

COLT 1998 Tracking the Best Regressor Mark Herbster, Manfred K. Warmuth

MLJ 1997 A Comparison of New and Old Algorithms for a Mixture Estimation Problem David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth

NeurIPS 1997 Relative Loss Bounds for Multidimensional Regression Problems Jyrki Kivinen, Manfred K. Warmuth

COLT 1996 Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes (Extended Abstract) Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth

MLJ 1996 On the Worst-Case Analysis of Temporal-Difference Learning Algorithms Robert E. Schapire, Manfred K. Warmuth

ICML 1996 On-Line Portfolio Selection Using Multiplicative Updates David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth

MLJ 1996 On-Line Prediction and Conversion Strategies Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth

NeurIPS 1996 Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions Yoram Singer, Manfred K. Warmuth

COLT 1995 A Comparison of New and Old Algorithms for a Mixture Estimation Problem David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth

ICML 1995 Efficient Learning with Virtual Threshold Gates Wolfgang Maass, Manfred K. Warmuth

NeurIPS 1995 Exponentially Many Local Minima for Single Neurons Peter Auer, Mark Herbster, Manfred K. Warmuth

MLJ 1995 Learning Binary Relations Using Weighted Majority Voting Sally A. Goldman, Manfred K. Warmuth

MLJ 1995 Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension Sally Floyd, Manfred K. Warmuth

COLT 1995 The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds When Few Input Variables Are Relevant Jyrki Kivinen, Manfred K. Warmuth

ICML 1995 Tracking the Best Expert Mark Herbster, Manfred K. Warmuth

NeurIPS 1995 Worst-Case Loss Bounds for Single Neurons David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth

ICML 1994 On the Worst-Case Analysis of Temporal-Difference Learning Algorithms Robert E. Schapire, Manfred K. Warmuth

COLT 1994 Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, COLT 1994, New Brunswick, NJ, USA, July 12-15, 1994 Manfred K. Warmuth

COLT 1993 Learning Binary Relations Using Weighted Majority Voting Sally A. Goldman, Manfred K. Warmuth

COLT 1993 Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth

MLJ 1992 On the Computational Complexity of Approximating Distributions by Probabilistic Automata Naoki Abe, Manfred K. Warmuth

COLT 1992 Some Weak Learning Results David P. Helmbold, Manfred K. Warmuth

COLT 1991 Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence Naoki Abe, Manfred K. Warmuth, Jun'ichi Takeuchi

COLT 1991 Proceedings of the Fourth Annual Workshop on Computational Learning Theory, COLT 1991, Santa Cruz, California, USA, August 5-7, 1991 Manfred K. Warmuth, Leslie G. Valiant

COLT 1990 Composite Geometric Concepts and Polynomial Predictability Philip M. Long, Manfred K. Warmuth

COLT 1990 Learning Integer Lattices David P. Helmbold, Robert Sloan, Manfred K. Warmuth

MLJ 1990 Learning Nested Differences of Intersection-Closed Concept Classes David P. Helmbold, Robert Sloan, Manfred K. Warmuth

COLT 1990 On the Computational Complexity of Approximating Distributions by Probabilistic Automata Naoki Abe, Manfred K. Warmuth

COLT 1989 Learning Nested Differences of Intersection-Closed Concept Classes David P. Helmbold, Robert Sloan, Manfred K. Warmuth

COLT 1989 Proceedings of the Second Annual Workshop on Computational Learning Theory, COLT 1989, Santa Cruz, CA, USA, July 31 - August 2, 1989 Ronald L. Rivest, David Haussler, Manfred K. Warmuth

COLT 1988 Equivalence of Models for Polynomial Learnability David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth

COLT 1988 Predicting 0, 1-Functions on Randomly Drawn Points David Haussler, Nick Littlestone, Manfred K. Warmuth

AAAI 1986 Finding a Shortest Solution for the N × N Extension of the 15-PUZZLE Is Intractable Daniel Ratner, Manfred K. Warmuth