default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDManfred K. Warmuth
Person information
- affiliation: University of California, Santa Cruz, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c133]Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
Optimal Transport with Tempered Exponential Measures. AAAI 2024: 10838-10846- [c132]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: 3-46 - [i41]Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth:
Tempered Calculus for ML: Application to Hyperbolic Model Embedding. CoRR abs/2402.04163 (2024) - [i40]Manfred K. Warmuth, Wojciech Kotlowski, Matt Jones, Ehsan Amid:
Noise misleads rotation invariant algorithms on sparse targets. CoRR abs/2403.02697 (2024) - 2023
- [j68]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) - [c131]Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. AISTATS 2023: 2994-3017 - [c130]Manfred K. Warmuth, Ehsan Amid:
Open Problem: Learning sparse linear concepts by priming the features. COLT 2023: 5937-5942 - [c129]Richard Nock, Ehsan Amid, Manfred K. Warmuth:
Boosting with Tempered Exponential Measures. NeurIPS 2023 - [i39]Jacob D. Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth:
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. CoRR abs/2305.17040 (2023) - [i38]Richard Nock, Ehsan Amid, Manfred K. Warmuth:
Boosting with Tempered Exponential Measures. CoRR abs/2306.05487 (2023) - [i37]Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
Optimal Transport with Tempered Exponential Measures. CoRR abs/2309.04015 (2023) - [i36]Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth:
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. CoRR abs/2311.13459 (2023) - 2022
- [j67]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: 1-28 (2022) - [j66]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Unbiased estimators for random design regression. J. Mach. Learn. Res. 23: 167:1-167:46 (2022) - [c128]Ehsan Amid, Rohan Anil, Manfred K. Warmuth:
LocoProp: Enhancing BackProp via Local Loss Optimization. AISTATS 2022: 9626-9642 - [i35]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Step-size Adaptation Using Exponentiated Gradient Updates. CoRR abs/2202.00145 (2022) - [i34]Ehsan Amid, Rohan Anil, Wojciech Kotlowski, Manfred K. Warmuth:
Learning from Randomly Initialized Neural Network Features. CoRR abs/2202.06438 (2022) - [i33]Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth:
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation. CoRR abs/2209.07080 (2022) - [i32]Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. CoRR abs/2211.02765 (2022) - 2021
- [c127]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: 1214-1236 - [i31]Negin Majidi, Ehsan Amid, Hossein Talebi, Manfred K. Warmuth:
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. CoRR abs/2104.01493 (2021) - [i30]Ehsan Amid, Rohan Anil, Manfred K. Warmuth:
LocoProp: Enhancing BackProp via Local Loss Optimization. CoRR abs/2106.06199 (2021) - 2020
- [c126]Ehsan Amid, Manfred K. Warmuth:
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. AAAI 2020: 3179-3186 - [c125]Ehsan Amid, Manfred K. Warmuth:
Winnowing with Gradient Descent. COLT 2020: 163-182 - [c124]Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth:
Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment. ICIP 2020: 3413-3417 - [c123]Ehsan Amid, Manfred K. Warmuth:
Reparameterizing Mirror Descent as Gradient Descent. NeurIPS 2020 - [c122]Ehsan Amid, Manfred K. Warmuth:
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. UAI 2020: 81-90 - [i29]Ehsan Amid, Manfred K. Warmuth:
Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent. CoRR abs/2002.10487 (2020) - [i28]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 abs/2010.08625 (2020) - [i27]Hossein Talebi, Ehsan Amid, Peyman Milanfar, Manfred K. Warmuth:
Rank-smoothed Pairwise Learning In Perceptual Quality Assessment. CoRR abs/2011.10893 (2020)
2010 – 2019
- 2019
- [j65]Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth:
Mistake bounds on the noise-free multi-armed bandit game. Inf. Comput. 269 (2019) - [c121]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Correcting the bias in least squares regression with volume-rescaled sampling. AISTATS 2019: 944-953 - [c120]Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan:
Two-temperature logistic regression based on the Tsallis divergence. AISTATS 2019: 2388-2396 - [c119]Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth:
Online Non-Additive Path Learning under Full and Partial Information. ALT 2019: 274-299 - [c118]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: 1050-1069 - [c117]Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth:
Unlabeled Sample Compression Schemes and Corner Peelings for Ample and Maximum Classes. ICALP 2019: 34:1-34:15 - [c116]Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth:
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models. ICML 2019: 3321-3330 - [c115]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. NeurIPS 2019: 14987-14996 - [i26]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 abs/1902.00995 (2019) - [i25]Ehsan Amid, Manfred K. Warmuth:
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. CoRR abs/1902.04107 (2019) - [i24]Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth:
Adaptive scale-invariant online algorithms for learning linear models. CoRR abs/1902.07528 (2019) - [i23]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. CoRR abs/1906.03361 (2019) - [i22]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Unbiased estimators for random design regression. CoRR abs/1907.03411 (2019) - [i21]Ehsan Amid, Manfred K. Warmuth:
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint. CoRR abs/1909.04803 (2019) - [i20]Ehsan Amid, Manfred K. Warmuth:
TriMap: Large-scale Dimensionality Reduction Using Triplets. CoRR abs/1910.00204 (2019) - 2018
- [j64]Michal Derezinski, Manfred K. Warmuth:
Reverse Iterative Volume Sampling for Linear Regression. J. Mach. Learn. Res. 19: 23:1-23:39 (2018) - [c114]Michal Derezinski, Manfred K. Warmuth:
Subsampling for Ridge Regression via Regularized Volume Sampling. AISTATS 2018: 716-725 - [c113]Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu:
Leveraged volume sampling for linear regression. NeurIPS 2018: 2510-2519 - [i19]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Tail bounds for volume sampled linear regression. CoRR abs/1802.06749 (2018) - [i18]Ehsan Amid, Manfred K. Warmuth:
A more globally accurate dimensionality reduction method using triplets. CoRR abs/1803.00854 (2018) - [i17]Sanjay Krishna Gouda, Salil Kanetkar, David Harrison, Manfred K. Warmuth:
Speech Recognition: Keyword Spotting Through Image Recognition. CoRR abs/1803.03759 (2018) - [i16]Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth:
Online Non-Additive Path Learning under Full and Partial Information. CoRR abs/1804.06518 (2018) - [i15]Michal Derezinski, Manfred K. Warmuth:
Reverse iterative volume sampling for linear regression. CoRR abs/1806.01969 (2018) - [i14]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Correcting the bias in least squares regression with volume-rescaled sampling. CoRR abs/1810.02453 (2018) - [i13]Jérémie Chalopin, Victor Chepoi, Shay Moran, Manfred K. Warmuth:
Unlabeled sample compression schemes and corner peelings for ample and maximum classes. CoRR abs/1812.02099 (2018) - 2017
- [c112]Holakou Rahmanian, Manfred K. Warmuth:
Online Dynamic Programming. NIPS 2017: 2827-2837 - [c111]Michal Derezinski, Manfred K. Warmuth:
Unbiased estimates for linear regression via volume sampling. NIPS 2017: 3084-3093 - [i12]Michal Derezinski, Manfred K. Warmuth:
Unbiased estimates for linear regression via volume sampling. CoRR abs/1705.06908 (2017) - [i11]Ehsan Amid, Manfred K. Warmuth:
Two-temperature logistic regression based on the Tsallis divergence. CoRR abs/1705.07210 (2017) - [i10]Holakou Rahmanian, S. V. N. Vishwanathan, Manfred K. Warmuth:
Online Dynamic Programming. CoRR abs/1706.00834 (2017) - [i9]Michal Derezinski, Manfred K. Warmuth:
Subsampling for Ridge Regression via Regularized Volume Sampling. CoRR abs/1710.05110 (2017) - 2016
- [j63]Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth:
Online PCA with Optimal Regret. J. Mach. Learn. Res. 17: 173:1-173:49 (2016) - [j62]Elad Hazan, Satyen Kale, Manfred K. Warmuth:
Learning rotations with little regret. Mach. Learn. 104(1): 129-148 (2016) - [c110]Shay Moran, Manfred K. Warmuth:
Labeled Compression Schemes for Extremal Classes. ALT 2016: 34-49 - [c109]Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth:
Noise Free Multi-armed Bandit Game. LATA 2016: 412-423 - [i8]Ehsan Amid, Nikos Vlassis, Manfred K. Warmuth:
t-Exponential Triplet Embedding. CoRR abs/1611.09957 (2016) - 2015
- [c108]Peter L. Bartlett, Wouter M. Koolen, Alan Malek, Eiji Takimoto, Manfred K. Warmuth:
Minimax Fixed-Design Linear Regression. COLT 2015: 226-239 - [c107]Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Manfred K. Warmuth:
On-Line Learning Algorithms for Path Experts with Non-Additive Losses. COLT 2015: 424-447 - [c106]Wouter M. Koolen, Manfred K. Warmuth, Dmitry Adamskiy:
Open Problem: Online Sabotaged Shortest Path. COLT 2015: 1764-1766 - [i7]Shay Moran, Manfred K. Warmuth:
Labeled compression schemes for extremal classes. CoRR abs/1506.00165 (2015) - [i6]Wojciech Kotlowski, Manfred K. Warmuth:
PCA with Gaussian perturbations. CoRR abs/1506.04855 (2015) - 2014
- [j61]Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold:
Combining initial segments of lists. Theor. Comput. Sci. 519: 29-45 (2014) - [j60]Manfred K. Warmuth, Wojciech Kotlowski, Shuisheng Zhou:
Kernelization of matrix updates, when and how? Theor. Comput. Sci. 558: 159-178 (2014) - [c105]Manfred K. Warmuth, Wouter M. Koolen:
Open Problem: Shifting Experts on Easy Data. COLT 2014: 1295-1298 - [c104]Michal Derezinski, Manfred K. Warmuth:
The limits of squared Euclidean distance regularization. NIPS 2014: 2807-2815 - [i5]Manfred K. Warmuth, Dima Kuzmin:
A Bayesian Probability Calculus for Density Matrices. CoRR abs/1408.3100 (2014) - 2013
- [c103]Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth:
Online PCA with Optimal Regrets. ALT 2013: 98-112 - [c102]Wouter M. Koolen, Jiazhong Nie, Manfred K. Warmuth:
Learning a set of directions. COLT 2013: 851-866 - [c101]Jiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang:
Open Problem: Lower bounds for Boosting with Hadamard Matrices. COLT 2013: 1076-1079 - [i4]Katy S. Azoury, Manfred K. Warmuth:
Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributions. CoRR abs/1301.6677 (2013) - [i3]Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth:
On-line PCA with Optimal Regrets. CoRR abs/1306.3895 (2013) - 2012
- [j59]Manfred K. Warmuth, Dima Kuzmin:
Online variance minimization. Mach. Learn. 87(1): 1-32 (2012) - [c100]Manfred K. Warmuth, Wojciech Kotlowski, Shuisheng Zhou:
Kernelization of Matrix Updates, When and How? ALT 2012: 350-364 - [c99]Wouter M. Koolen, Dmitry Adamskiy, Manfred K. Warmuth:
Putting Bayes to sleep. NIPS 2012: 135-143 - 2011
- [c98]Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold:
Combining Initial Segments of Lists. ALT 2011: 219-233 - [c97]Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth:
Learning Eigenvectors for Free. NIPS 2011: 945-953 - [c96]Wojciech Kotlowski, Manfred K. Warmuth:
Minimax Algorithm for Learning Rotations. COLT 2011: 821-824 - 2010
- [j58]Manfred K. Warmuth, Dima Kuzmin:
Bayesian generalized probability calculus for density matrices. Mach. Learn. 78(1-2): 63-101 (2010) - [c95]Manfred K. Warmuth:
The Blessing and the Curse of the Multiplicative Updates. ALT 2010: 31 - [c94]Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen:
Hedging Structured Concepts. COLT 2010: 93-105 - [c93]Elad Hazan, Satyen Kale, Manfred K. Warmuth:
Learning Rotations with Little Regret. COLT 2010: 144-154 - [c92]Elad Hazan, Satyen Kale, Manfred K. Warmuth:
On-line Variance Minimization in O(n2) per Trial? COLT 2010: 314-315 - [c91]Manfred K. Warmuth:
The Blessing and the Curse of the Multiplicative Updates. Discovery Science 2010: 382 - [c90]Shuisheng Zhou, Manfred K. Warmuth, Yinli Dong, Feng Ye:
New combination coefficients for AdaBoost algorithms. ICNC 2010: 3194-3198 - [c89]Jacob D. Abernethy, Manfred K. Warmuth:
Repeated Games against Budgeted Adversaries. NIPS 2010: 1-9
2000 – 2009
- 2009
- [j57]David P. Helmbold, Manfred K. Warmuth:
Learning Permutations with Exponential Weights. J. Mach. Learn. Res. 10: 1705-1736 (2009) - [c88]Jacob D. Abernethy, Manfred K. Warmuth:
Minimax Games with Bandits. COLT 2009 - [c87]Manfred K. Warmuth, S. V. N. Vishwanathan:
Tutorial summary: Survey of boosting from an optimization perspective. ICML 2009: 15 - 2008
- [c86]Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan:
Entropy Regularized LPBoost. ALT 2008: 256-271 - [c85]Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin:
When Random Play is Optimal Against an Adversary. COLT 2008: 437-446 - [c84]Adam M. Smith, Manfred K. Warmuth:
Learning Rotations. COLT 2008: 517 - 2007
- [j56]Dima Kuzmin, Manfred K. Warmuth:
Unlabeled Compression Schemes for Maximum Classes. J. Mach. Learn. Res. 8: 2047-2081 (2007) - [c83]David P. Helmbold, Manfred K. Warmuth:
Learning Permutations with Exponential Weights. COLT 2007: 469-483 - [c82]Manfred K. Warmuth:
When Is There a Free Matrix Lunch? COLT 2007: 630-632 - [c81]Dima Kuzmin, Manfred K. Warmuth:
Online kernel PCA with entropic matrix updates. ICML 2007: 465-472 - [c80]Manfred K. Warmuth:
Winnowing subspaces. ICML 2007: 999-1006 - [c79]Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch:
Boosting Algorithms for Maximizing the Soft Margin. NIPS 2007: 1585-1592 - 2006
- [j55]Jyrki Kivinen, Manfred K. Warmuth, Babak Hassibi:
The p-norm generalization of the LMS algorithm for adaptive filtering. IEEE Trans. Signal Process. 54(5): 1782-1793 (2006) - [c78]Manfred K. Warmuth, Dima Kuzmin:
Online Variance Minimization. COLT 2006: 514-528 - [c77]Jacob D. Abernethy, John Langford, Manfred K. Warmuth:
Continuous Experts and the Binning Algorithm. COLT 2006: 544-558 - [c76]Manfred K. Warmuth:
Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints. COLT 2006: 653-654 - [c75]Manfred K. Warmuth, Jun Liao, Gunnar Rätsch:
Totally corrective boosting algorithms that maximize the margin. ICML 2006: 1001-1008 - [c74]Manfred K. Warmuth, Dima Kuzmin:
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension. NIPS 2006: 1481-1488 - [c73]Manfred K. Warmuth, Dima Kuzmin:
A Bayesian Probability Calculus for Density Matrices. UAI 2006 - 2005
- [j54]Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth:
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. J. Mach. Learn. Res. 6: 995-1018 (2005) - [j53]Gunnar Rätsch, Manfred K. Warmuth:
Efficient Margin Maximizing with Boosting. J. Mach. Learn. Res. 6: 2131-2152 (2005) - [c72]Manfred K. Warmuth, S. V. N. Vishwanathan:
Leaving the Span. COLT 2005: 366-381 - [c71]Dima Kuzmin, Manfred K. Warmuth:
Unlabeled Compression Schemes for Maximum Classes, . COLT 2005: 591-605 - [c70]Dima Kuzmin, Manfred K. Warmuth:
Optimum Follow the Leader Algorithm. COLT 2005: 684-686 - [c69]Manfred K. Warmuth:
A Bayes Rule for Density Matrices. NIPS 2005: 1457-1464 - 2004
- [c68]Manfred K. Warmuth:
The Optimal PAC Algorithm. COLT 2004: 641-642 - [c67]Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth:
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. NIPS 2004: 1425-1432 - 2003
- [j52]Manfred K. Warmuth, Jun Liao, Gunnar Rätsch, Michael Mathieson, Santosh Putta, Christian Lemmen:
Active Learning with Support Vector Machines in the Drug Discovery Process. J. Chem. Inf. Comput. Sci. 43(2): 667-673 (2003) - [j51]Eiji Takimoto, Manfred K. Warmuth:
Path Kernels and Multiplicative Updates. J. Mach. Learn. Res. 4: 773-818 (2003) - [j50]Jürgen Forster, Manfred K. Warmuth:
Relative Loss Bounds for Temporal-Difference Learning. Mach. Learn. 51(1): 23-50 (2003) - [c66]Manfred K. Warmuth:
Compressing to VC Dimension Many Points. COLT 2003: 743-744 - [c65]Ashutosh Garg, Manfred K. Warmuth:
Inline updates for HMMs. INTERSPEECH 2003: 1005-1008 - [c64]Rita Singh, Manfred K. Warmuth, Bhiksha Raj, Paul Lamere:
Classification with free energy at raised temperatures. INTERSPEECH 2003: 1773-1776 - [c63]Kohei Hatano, Manfred K. Warmuth:
Boosting versus Covering. NIPS 2003: 1109-1116 - [e4]Bernhard Schölkopf, Manfred K. Warmuth:
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. Lecture Notes in Computer Science 2777, Springer 2003, ISBN 3-540-40720-0 [contents] - 2002
- [j49]Jürgen Forster, Manfred K. Warmuth:
Relative Expected Instantaneous Loss Bounds. J. Comput. Syst. Sci. 64(1): 76-102 (2002) - [j48]Olivier Bousquet, Manfred K. Warmuth:
Tracking a Small Set of Experts by Mixing Past Posteriors. J. Mach. Learn. Res. 3: 363-396 (2002) - [j47]David P. Helmbold, Sandra Panizza, Manfred K. Warmuth:
Direct and indirect algorithms for on-line learning of disjunctions. Theor. Comput. Sci. 284(1): 109-142 (2002) - [j46]Eiji Takimoto, Manfred K. Warmuth:
Predicting nearly as well as the best pruning of a planar decision graph. Theor. Comput. Sci. 288(2): 217-235 (2002) - [c62]Eiji Takimoto, Manfred K. Warmuth:
Path Kernels and Multiplicative Updates. COLT 2002: 74-89 - [c61]Gunnar Rätsch, Manfred K. Warmuth:
Maximizing the Margin with Boosting. COLT 2002: 334-350 - [c60]Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari:
Adaptive Caching by Refetching. NIPS 2002: 1465-1472 - 2001
- [j45]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Linear Predictor. J. Mach. Learn. Res. 1: 281-309 (2001) - [j44]Katy S. Azoury, Manfred K. Warmuth:
Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions. Mach. Learn. 43(3): 211-246 (2001) - [j43]Jyrki Kivinen, Manfred K. Warmuth:
Relative Loss Bounds for Multidimensional Regression Problems. Mach. Learn. 45(3): 301-329 (2001) - [c59]Olivier Bousquet, Manfred K. Warmuth:
Tracking a Small Set of Experts by Mixing Past Posteriors. COLT/EuroCOLT 2001: 31-47 - [c58]Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth:
On the Convergence of Leveraging. NIPS 2001: 487-494 - [c57]Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen:
Active Learning in the Drug Discovery Process. NIPS 2001: 1449-1456 - 2000
- [c56]Eiji Takimoto, Manfred K. Warmuth:
The Last-Step Minimax Algorithm. ALT 2000: 279-290 - [c55]Jürgen Forster, Manfred K. Warmuth:
Relative Expected Instantaneous Loss Bounds. COLT 2000: 90-99 - [c54]Eiji Takimoto, Manfred K. Warmuth:
The Minimax Strategy for Gaussian Density Estimation. pp. COLT 2000: 100-106 - [c53]Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller:
Barrier Boosting. COLT 2000: 170-179 - [c52]Jürgen Forster, Manfred K. Warmuth:
Relative Loss Bounds for Temporal-Difference Learning. ICML 2000: 295-302 - [i2]Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth:
Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes. Electron. Colloquium Comput. Complex. TR00 (2000) - [i1]Peter Auer, Manfred K. Warmuth:
Tracking the best disjunction. Electron. Colloquium Comput. Complex. TR00 (2000)
1990 – 1999
- 1999
- [j42]David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth:
Relative loss bounds for single neurons. IEEE Trans. Neural Networks 10(6): 1291-1304 (1999) - [c51]Eiji Takimoto, Manfred K. Warmuth:
Predicting Nearly as well as the best Pruning of a Planar Decision Graph. ALT 1999: 335-346 - [c50]Jyrki Kivinen, Manfred K. Warmuth:
Boosting as Entropy Projection. COLT 1999: 134-144 - [c49]David P. Helmbold, Sandra Panizza, Manfred K. Warmuth:
Direct and Indirect Algorithms for On-line Learning of Disjunctions. EuroCOLT 1999: 138-152 - [c48]Jyrki Kivinen, Manfred K. Warmuth:
Averaging Expert Predictions. EuroCOLT 1999: 153-167 - [c47]Katy S. Azoury, Manfred K. Warmuth:
Relative Loss Bounds for On-line Density Estirnation with the Exponential Family of Distributions. UAI 1999: 31-40 - 1998
- [j41]Wolfgang Maass, Manfred K. Warmuth:
Efficient Learning With Virtual Threshold Gates. Inf. Comput. 141(1): 66-83 (1998) - [j40]Peter Auer, Manfred K. Warmuth:
Tracking the Best Disjunction. Mach. Learn. 32(2): 127-150 (1998) - [j39]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Expert. Mach. Learn. 32(2): 151-178 (1998) - [j38]David Haussler, Jyrki Kivinen, Manfred K. Warmuth:
Sequential Prediction of Individual Sequences Under General Loss Functions. IEEE Trans. Inf. Theory 44(5): 1906-1925 (1998) - [c46]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Regressor. COLT 1998: 24-31 - [c45]Claudio Gentile, Manfred K. Warmuth:
Linear Hinge Loss and Average Margin. NIPS 1998: 225-231 - [c44]Yoram Singer, Manfred K. Warmuth:
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. NIPS 1998: 578-584 - 1997
- [j37]Jyrki Kivinen, Manfred K. Warmuth, Peter Auer:
The Perceptron Algorithm Versus Winnow: Linear Versus Logarithmic Mistake Bounds when Few Input Variables are Relevant (Technical Note). Artif. Intell. 97(1-2): 325-343 (1997) - [j36]Jyrki Kivinen, Manfred K. Warmuth:
Exponentiated Gradient Versus Gradient Descent for Linear Predictors. Inf. Comput. 132(1): 1-63 (1997) - [j35]Nicolò Cesa-Bianchi, Yoav Freund, David Haussler, David P. Helmbold, Robert E. Schapire, Manfred K. Warmuth:
How to use expert advice. J. ACM 44(3): 427-485 (1997) - [j34]David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Mach. Learn. 27(1): 97-119 (1997) - [c43]Manfred K. Warmuth:
Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. EuroCOLT 1997: 1-2 - [c42]Jyrki Kivinen, Manfred K. Warmuth:
Relative Loss Bounds for Multidimensional Regression Problems. NIPS 1997: 287-293 - [c41]Manfred K. Warmuth:
Relative Loss Bounds, the Minimum Relative Entropy Principle, and EM. NIPS 1997 - [c40]Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
Using and Combining Predictors That Specialize. STOC 1997: 334-343 - 1996
- [j33]Robert E. Schapire, Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. Mach. Learn. 22(1-3): 95-121 (1996) - [j32]Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth:
On-line Prediction and Conversion Strategies. Mach. Learn. 25(1): 71-110 (1996) - [j31]Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth:
Worst-case quadratic loss bounds for prediction using linear functions and gradient descent. IEEE Trans. Neural Networks 7(3): 604-619 (1996) - [c39]Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth:
Learning of Depth Two Neural Networks with Constant Fan-In at the Hidden Nodes (Extended Abstract). COLT 1996: 333-343 - [c38]David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth:
On-Line Portfolio Selection Using Multiplicative Updates. ICML 1996: 243-251 - [c37]Yoram Singer, Manfred K. Warmuth:
Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions. NIPS 1996: 641-647 - 1995
- [j30]Nick Littlestone, Philip M. Long, Manfred K. Warmuth:
On-line Learning of Linear Functions. Comput. Complex. 5(1): 1-23 (1995) - [j29]David P. Helmbold, Manfred K. Warmuth:
On Weak Learning. J. Comput. Syst. Sci. 50(3): 551-573 (1995) - [j28]Sally A. Goldman, Manfred K. Warmuth:
Learning Binary Relations Using Weighted Majority Voting. Mach. Learn. 20(3): 245-271 (1995) - [j27]Sally Floyd, Manfred K. Warmuth:
Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. Mach. Learn. 21(3): 269-304 (1995) - [c36]David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth:
A Comparison of New and Old Algorithms for a Mixture Estimation Problem. COLT 1995: 69-78 - [c35]Jyrki Kivinen, Manfred K. Warmuth:
The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant. COLT 1995: 289-296 - [c34]David Haussler, Jyrki Kivinen, Manfred K. Warmuth:
Tight worst-case loss bounds for predicting with expert advice. EuroCOLT 1995: 69-83 - [c33]Peter Auer, Manfred K. Warmuth:
Tracking the Best Disjunction. FOCS 1995: 312-321 - [c32]Mark Herbster, Manfred K. Warmuth:
Tracking the Best Expert. ICML 1995: 286-294 - [c31]Wolfgang Maass, Manfred K. Warmuth:
Efficient Learning with Virtual Threshold Gates. ICML 1995: 378-386 - [c30]David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth:
Worst-case Loss Bounds for Single Neurons. NIPS 1995: 309-315 - [c29]Peter Auer, Mark Herbster, Manfred K. Warmuth:
Exponentially many local minima for single neurons. NIPS 1995: 316-322 - [c28]Jyrki Kivinen, Manfred K. Warmuth:
Additive versus exponentiated gradient updates for linear prediction. STOC 1995: 209-218 - 1994
- [j26]Hans L. Bodlaender, Shlomo Moran, Manfred K. Warmuth:
The Distributed Bit Complexity of the Ring: From the Anonymous to the Non-anonymous Case. Inf. Comput. 108(1): 34-50 (1994) - [j25]Nick Littlestone, Manfred K. Warmuth:
The Weighted Majority Algorithm. Inf. Comput. 108(2): 212-261 (1994) - [j24]Philip M. Long, Manfred K. Warmuth:
Composite Geometric Concepts and Polynomial Predictability. Inf. Comput. 113(2): 230-252 (1994) - [j23]David Haussler, Nick Littlestone, Manfred K. Warmuth:
Predicting \0,1\-Functions on Randomly Drawn Points. Inf. Comput. 115(2): 248-292 (1994) - [j22]Nicolò Cesa-Bianchi, Anders Krogh, Manfred K. Warmuth:
Bounds on approximate steepest descent for likelihood maximization in exponential families. IEEE Trans. Inf. Theory 40(4): 1215-1218 (1994) - [c27]Robert E. Schapire, Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. ICML 1994: 266-274 - [e3]Manfred K. Warmuth:
Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, COLT 1994, New Brunswick, NJ, USA, July 12-15, 1994. ACM 1994, ISBN 0-89791-655-7 [contents] - 1993
- [j21]Leonard Pitt, Manfred K. Warmuth:
The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial. J. ACM 40(1): 95-142 (1993) - [j20]Shlomo Moran, Manfred K. Warmuth:
Gap Theorems for Distributed Computation. SIAM J. Comput. 22(2): 379-394 (1993) - [c26]Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth:
Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule. COLT 1993: 429-438 - [c25]Sally A. Goldman, Manfred K. Warmuth:
Learning Binary Relations Using Weighted Majority Voting. COLT 1993: 453-462 - [c24]Jyrki Kivinen, Manfred K. Warmuth:
Using experts for predicting continuous outcomes. EuroCOLT 1993: 109-120 - [c23]Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth:
On-line prediction and conversion strategies. EuroCOLT 1993: 205-216 - [c22]Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth:
How to use expert advice. STOC 1993: 382-391 - 1992
- [j19]Naoki Abe, Manfred K. Warmuth:
On the Computational Complexity of Approximating Distributions by Probabilistic Automata. Mach. Learn. 9: 205-260 (1992) - [j18]David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth:
Learning Integer Lattices. SIAM J. Comput. 21(2): 240-266 (1992) - [c21]David P. Helmbold, Manfred K. Warmuth:
Some Weak Learning Results. COLT 1992: 399-412 - 1991
- [j17]David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth:
Equivalence of Models for Polynomial Learnability. Inf. Comput. 95(2): 129-161 (1991) - [c20]Naoki Abe, Manfred K. Warmuth, Jun'ichi Takeuchi:
Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence. COLT 1991: 277-289 - [c19]Nick Littlestone, Philip M. Long, Manfred K. Warmuth:
On-Line Learning of Linear Functions. STOC 1991: 465-475 - [e2]Manfred K. Warmuth, Leslie G. Valiant:
Proceedings of the Fourth Annual Workshop on Computational Learning Theory, COLT 1991, Santa Cruz, California, USA, August 5-7, 1991. Morgan Kaufmann 1991, ISBN 1-55860-213-5 [contents] - 1990
- [j16]Leonard Pitt, Manfred K. Warmuth:
Prediction-Preserving Reducibility. J. Comput. Syst. Sci. 41(3): 430-467 (1990) - [j15]Daniel Ratner, Manfred K. Warmuth:
NxN Puzzle and Related Relocation Problem. J. Symb. Comput. 10(2): 111-138 (1990) - [j14]David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth:
Learning Nested Differences of Intersection-Closed Concept Classes. Mach. Learn. 5: 165-196 (1990) - [c18]Naoki Abe, Manfred K. Warmuth:
On the Computational Complexity of Approximating Distributions by Probabilistic Automata. COLT 1990: 52-66 - [c17]Philip M. Long, Manfred K. Warmuth:
Composite Geometric Concepts and Polynomial Predictability. COLT 1990: 273-287 - [c16]David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth:
Learning Integer Lattices. COLT 1990: 288-302
1980 – 1989
- 1989
- [j13]Jakob Gonczarowski, Manfred K. Warmuth:
Scattered Versus Context-Sensitive Rewriting. Acta Informatica 27(1): 81-95 (1989) - [j12]Richard J. Anderson, Ernst W. Mayr, Manfred K. Warmuth:
Parallel Approximation Algorithms for Bin Packing. Inf. Comput. 82(3): 262-277 (1989) - [j11]Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth:
Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36(4): 929-965 (1989) - [j10]Barbara B. Simons, Manfred K. Warmuth:
A Fast Algorithm for Multiprocessor Scheduling of Unit-Length Jobs. SIAM J. Comput. 18(4): 690-710 (1989) - [c15]Manfred K. Warmuth:
Towards Representation Independence in PAC Learning. AII 1989: 78-103 - [c14]Leonard Pitt, Manfred K. Warmuth:
The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial (abstract). SCT 1989: 230 - [c13]David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth:
Learning Nested Differences of Intersection-Closed Concept Classes. COLT 1989: 41-56 - [c12]Hans L. Bodlaender, Shlomo Moran, Manfred K. Warmuth:
The Distributed Bit Complexity of the Ring: From the Anonymous to the Non-anonymous Case. FCT 1989: 58-67 - [c11]Nick Littlestone, Manfred K. Warmuth:
The Weighted Majority Algorithm. FOCS 1989: 256-261 - [c10]Leonard Pitt, Manfred K. Warmuth:
The Minimum Consistent DFA Problem Cannot Be Approximated within any Polynomial. STOC 1989: 421-432 - [e1]Ronald L. Rivest, David Haussler, Manfred K. Warmuth:
Proceedings of the Second Annual Workshop on Computational Learning Theory, COLT 1989, Santa Cruz, CA, USA, July 31 - August 2, 1989. Morgan Kaufmann 1989, ISBN 1-55860-086-8 [contents] - 1988
- [j9]Hagit Attiya, Marc Snir, Manfred K. Warmuth:
Computing on an anonymous ring. J. ACM 35(4): 845-875 (1988) - [c9]Leonard Pitt, Manfred K. Warmuth:
Reductions among prediction problems: on the difficulty of predicting automata. SCT 1988: 60-69 - [c8]David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth:
Equivalence of Models for Polynomial Learnability. COLT 1988: 42-55 - [c7]David Haussler, Nick Littlestone, Manfred K. Warmuth:
Predicting {0, 1}-Functions on Randomly Drawn Points. COLT 1988: 280-296 - [c6]David Haussler, Nick Littlestone, Manfred K. Warmuth:
Predicting {0,1}-Functions on Randomly Drawn Points (Extended Abstract). FOCS 1988: 100-109 - 1987
- [j8]Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth:
Occam's Razor. Inf. Process. Lett. 24(6): 377-380 (1987) - 1986
- [j7]Elias Dahlhaus, Manfred K. Warmuth:
Membership for Growing Context-Sensitive Grammars is Polynomial. J. Comput. Syst. Sci. 33(3): 456-472 (1986) - [j6]Danny Dolev, Eli Upfal, Manfred K. Warmuth:
The Parallel Complexity of Scheduling with Precedence Constraints. J. Parallel Distributed Comput. 3(4): 553-576 (1986) - [j5]Jakob Gonczarowski, Manfred K. Warmuth:
Manipulating Derivation Forests by Scheduling Techniques. Theor. Comput. Sci. 45(1): 87-119 (1986) - [c5]Daniel Ratner, Manfred K. Warmuth:
Finding a Shortest Solution for the N × N Extension of the 15-PUZZLE Is Intractable. AAAI 1986: 168-172 - [c4]Elias Dahlhaus, Manfred K. Warmuth:
Membership for Growing Context Sensitive Grammars is Polynomial. CAAP 1986: 85-99 - [c3]Shlomo Moran, Manfred K. Warmuth:
Gap Theorems for Distributed Computation. PODC 1986: 131-140 - [c2]Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth:
Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis Dimension (Extended Abstract). STOC 1986: 273-282 - 1985
- [j4]Danny Dolev, Manfred K. Warmuth:
Scheduling Flat Graphs. SIAM J. Comput. 14(3): 638-657 (1985) - [j3]Jakob Gonczarowski, Manfred K. Warmuth:
Applications of Scheduling Theory to Formal Language Theory. Theor. Comput. Sci. 37: 217-243 (1985) - [c1]Chagit Attiya, Marc Snir, Manfred K. Warmuth:
Computing on an Anonymous Ring. PODC 1985: 196-203 - 1984
- [j2]Danny Dolev, Manfred K. Warmuth:
Scheduling Precedence Graphs of Bounded Height. J. Algorithms 5(1): 48-59 (1984) - [j1]Manfred K. Warmuth, David Haussler:
On the Complexity of Iterated Shuffle. J. Comput. Syst. Sci. 28(3): 345-358 (1984)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-09-13 00:45 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
