| 2008 |
|---|
| 139 | EE | Manfred K. Warmuth,
Karen A. Glocer,
S. V. N. Vishwanathan:
Entropy Regularized LPBoost.
ALT 2008: 256-271 |
| 138 | EE | Jacob Abernethy,
Manfred K. Warmuth,
Joel Yellin:
When Random Play is Optimal Against an Adversary.
COLT 2008: 437-446 |
| 137 | EE | Adam M. Smith,
Manfred K. Warmuth:
Learning Rotations.
COLT 2008: 517 |
| 2007 |
|---|
| 136 | EE | David P. Helmbold,
Manfred K. Warmuth:
Learning Permutations with Exponential Weights.
COLT 2007: 469-483 |
| 135 | EE | Manfred K. Warmuth:
When Is There a Free Matrix Lunch?
COLT 2007: 630-632 |
| 134 | EE | Dima Kuzmin,
Manfred K. Warmuth:
Online kernel PCA with entropic matrix updates.
ICML 2007: 465-472 |
| 133 | EE | Manfred K. Warmuth:
Winnowing subspaces.
ICML 2007: 999-1006 |
| 132 | EE | Manfred K. Warmuth,
Karen A. Glocer,
Gunnar Rätsch:
Boosting Algorithms for Maximizing the Soft Margin.
NIPS 2007 |
| 2006 |
|---|
| 131 | EE | Manfred K. Warmuth,
Dima Kuzmin:
Online Variance Minimization.
COLT 2006: 514-528 |
| 130 | EE | Jacob Abernethy,
John Langford,
Manfred K. Warmuth:
Continuous Experts and the Binning Algorithm.
COLT 2006: 544-558 |
| 129 | EE | Manfred K. Warmuth:
Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints.
COLT 2006: 653-654 |
| 128 | EE | Manfred K. Warmuth,
Jun Liao,
Gunnar Rätsch:
Totally corrective boosting algorithms that maximize the margin.
ICML 2006: 1001-1008 |
| 127 | EE | Manfred K. Warmuth,
Dima Kuzmin:
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension.
NIPS 2006: 1481-1488 |
| 126 | EE | Manfred K. Warmuth:
A Bayesian Probability Calculus for Density Matrices.
UAI 2006 |
| 125 | EE | Manfred K. Warmuth,
Dima Kuzmin:
A Bayesian Probability Calculus for Density Matrices.
UAI 2006 |
| 124 | EE | Jyrki Kivinen,
Manfred K. Warmuth,
Babak Hassibi:
The p-norm generalization of the LMS algorithm for adaptive filtering.
IEEE Transactions on Signal Processing 54(5): 1782-1793 (2006) |
| 2005 |
|---|
| 123 | EE | Manfred K. Warmuth,
S. V. N. Vishwanathan:
Leaving the Span.
COLT 2005: 366-381 |
| 122 | EE | Dima Kuzmin,
Manfred K. Warmuth:
Unlabeled Compression Schemes for Maximum Classes, .
COLT 2005: 591-605 |
| 121 | EE | Dima Kuzmin,
Manfred K. Warmuth:
Optimum Follow the Leader Algorithm.
COLT 2005: 684-686 |
| 120 | EE | Manfred K. Warmuth:
A Bayes Rule for Density Matrices.
NIPS 2005 |
| 119 | EE | Gunnar Rätsch,
Manfred K. Warmuth:
Efficient Margin Maximizing with Boosting.
Journal of Machine Learning Research 6: 2131-2152 (2005) |
| 118 | EE | Koji Tsuda,
Gunnar Rätsch,
Manfred K. Warmuth:
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection.
Journal of Machine Learning Research 6: 995-1018 (2005) |
| 2004 |
|---|
| 117 | EE | Manfred K. Warmuth:
The Optimal PAC Algorithm.
COLT 2004: 641-642 |
| 116 | EE | Koji Tsuda,
Gunnar Rätsch,
Manfred K. Warmuth:
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection.
NIPS 2004 |
| 2003 |
|---|
| 115 | | 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
Springer 2003 |
| 114 | EE | Manfred K. Warmuth:
Compressing to VC Dimension Many Points.
COLT 2003: 743-744 |
| 113 | EE | Kohei Hatano,
Manfred K. Warmuth:
Boosting versus Covering.
NIPS 2003 |
| 112 | EE | Manfred K. Warmuth,
Jun Liao,
Gunnar Rätsch,
Michael Mathieson,
Santosh Putta,
Christian Lemmen:
Active Learning with Support Vector Machines in the Drug Discovery Process.
Journal of Chemical Information and Computer Sciences 43(2): 667-673 (2003) |
| 111 | EE | Eiji Takimoto,
Manfred K. Warmuth:
Path Kernels and Multiplicative Updates.
Journal of Machine Learning Research 4: 773-818 (2003) |
| 110 | | Jürgen Forster,
Manfred K. Warmuth:
Relative Loss Bounds for Temporal-Difference Learning.
Machine Learning 51(1): 23-50 (2003) |
| 2002 |
|---|
| 109 | EE | Gunnar Rätsch,
Manfred K. Warmuth:
Maximizing the Margin with Boosting.
COLT 2002: 334-350 |
| 108 | EE | Eiji Takimoto,
Manfred K. Warmuth:
Path Kernels and Multiplicative Updates.
COLT 2002: 74-89 |
| 107 | EE | Robert B. Gramacy,
Manfred K. Warmuth,
Scott A. Brandt,
Ismail Ari:
Adaptive Caching by Refetching.
NIPS 2002: 1465-1472 |
| 106 | EE | Jürgen Forster,
Manfred K. Warmuth:
Relative Expected Instantaneous Loss Bounds.
J. Comput. Syst. Sci. 64(1): 76-102 (2002) |
| 105 | EE | Olivier Bousquet,
Manfred K. Warmuth:
Tracking a Small Set of Experts by Mixing Past Posteriors.
Journal of Machine Learning Research 3: 363-396 (2002) |
| 104 | EE | 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) |
| 103 | | 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) |
| 2001 |
|---|
| 102 | EE | Olivier Bousquet,
Manfred K. Warmuth:
Tracking a Small Set of Experts by Mixing Past Posteriors.
COLT/EuroCOLT 2001: 31-47 |
| 101 | EE | Manfred K. Warmuth,
Gunnar Rätsch,
Michael Mathieson,
Jun Liao,
Christian Lemmen:
Active Learning in the Drug Discovery Process.
NIPS 2001: 1449-1456 |
| 100 | EE | Gunnar Rätsch,
Sebastian Mika,
Manfred K. Warmuth:
On the Convergence of Leveraging.
NIPS 2001: 487-494 |
| 99 | EE | Mark Herbster,
Manfred K. Warmuth:
Tracking the Best Linear Predictor.
Journal of Machine Learning Research 1: 281-309 (2001) |
| 98 | | Katy S. Azoury,
Manfred K. Warmuth:
Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions.
Machine Learning 43(3): 211-246 (2001) |
| 97 | | Jyrki Kivinen,
Manfred K. Warmuth:
Relative Loss Bounds for Multidimensional Regression Problems.
Machine Learning 45(3): 301-329 (2001) |
| 2000 |
|---|
| 96 | EE | Eiji Takimoto,
Manfred K. Warmuth:
The Last-Step Minimax Algorithm.
ALT 2000: 279-290 |
| 95 | | Eiji Takimoto,
Manfred K. Warmuth:
The Minimax Strategy for Gaussian Density Estimation. pp.
COLT 2000: 100-106 |
| 94 | | Gunnar Rätsch,
Manfred K. Warmuth,
Sebastian Mika,
Takashi Onoda,
Steven Lemm,
Klaus-Robert Müller:
Barrier Boosting.
COLT 2000: 170-179 |
| 93 | | Jürgen Forster,
Manfred K. Warmuth:
Relative Expected Instantaneous Loss Bounds.
COLT 2000: 90-99 |
| 92 | | Jürgen Forster,
Manfred K. Warmuth:
Relative Loss Bounds for Temporal-Difference Learning.
ICML 2000: 295-302 |
| 91 | EE | Peter Auer,
Stephen Kwek,
Wolfgang Maass,
Manfred K. Warmuth:
Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes
Electronic Colloquium on Computational Complexity (ECCC) 7(55): (2000) |
| 90 | EE | Peter Auer,
Manfred K. Warmuth:
Tracking the best disjunction
Electronic Colloquium on Computational Complexity (ECCC) 7(70): (2000) |
| 1999 |
|---|
| 89 | EE | Eiji Takimoto,
Manfred K. Warmuth:
Predicting Nearly as well as the best Pruning of a Planar Decision Graph.
ATL 1999: 335-346 |
| 88 | EE | Jyrki Kivinen,
Manfred K. Warmuth:
Boosting as Entropy Projection.
COLT 1999: 134-144 |
| 87 | EE | David P. Helmbold,
Sandra Panizza,
Manfred K. Warmuth:
Direct and Indirect Algorithms for On-line Learning of Disjunctions.
EuroCOLT 1999: 138-152 |
| 86 | EE | Jyrki Kivinen,
Manfred K. Warmuth:
Averaging Expert Predictions
EuroCOLT 1999: 153-167 |
| 85 | EE | Katy S. Azoury,
Manfred K. Warmuth:
Relative Loss Bounds for On-line Density Estirnation with the Exponential Family of Distributions.
UAI 1999: 31-40 |
| 84 | EE | David P. Helmbold,
Jyrki Kivinen,
Manfred K. Warmuth:
Relative loss bounds for single neurons.
IEEE Transactions on Neural Networks 10(6): 1291-1304 (1999) |
| 1998 |
|---|
| 83 | EE | Mark Herbster,
Manfred K. Warmuth:
Tracking the Best Regressor.
COLT 1998: 24-31 |
| 82 | EE | Claudio Gentile,
Manfred K. Warmuth:
Linear Hinge Loss and Average Margin.
NIPS 1998: 225-231 |
| 81 | EE | Yoram Singer,
Manfred K. Warmuth:
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy.
NIPS 1998: 578-584 |
| 80 | | David Haussler,
Jyrki Kivinen,
Manfred K. Warmuth:
Sequential Prediction of Individual Sequences Under General Loss Functions.
IEEE Transactions on Information Theory 44(5): 1906-1925 (1998) |
| 79 | | Wolfgang Maass,
Manfred K. Warmuth:
Efficient Learning With Virtual Threshold Gates.
Inf. Comput. 141(1): 66-83 (1998) |
| 78 | | Peter Auer,
Manfred K. Warmuth:
Tracking the Best Disjunction.
Machine Learning 32(2): 127-150 (1998) |
| 77 | | Mark Herbster,
Manfred K. Warmuth:
Tracking the Best Expert.
Machine Learning 32(2): 151-178 (1998) |
| 1997 |
|---|
| 76 | | Manfred K. Warmuth:
Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension.
EuroCOLT 1997: 1-2 |
| 75 | | Jyrki Kivinen,
Manfred K. Warmuth:
Relative Loss Bounds for Multidimensional Regression Problems.
NIPS 1997 |
| 74 | | Manfred K. Warmuth:
Relative Loss Bounds, the Minimum Relative Entropy Principle, and EM.
NIPS 1997 |
| 73 | EE | Yoav Freund,
Robert E. Schapire,
Yoram Singer,
Manfred K. Warmuth:
Using and Combining Predictors That Specialize.
STOC 1997: 334-343 |
| 72 | EE | 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) |
| 71 | | Jyrki Kivinen,
Manfred K. Warmuth:
Exponentiated Gradient Versus Gradient Descent for Linear Predictors.
Inf. Comput. 132(1): 1-63 (1997) |
| 70 | EE | 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) |
| 69 | | David P. Helmbold,
Robert E. Schapire,
Yoram Singer,
Manfred K. Warmuth:
A Comparison of New and Old Algorithms for a Mixture Estimation Problem.
Machine Learning 27(1): 97-119 (1997) |
| 1996 |
|---|
| 68 | EE | 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 |
| 67 | | David P. Helmbold,
Robert E. Schapire,
Yoram Singer,
Manfred K. Warmuth:
On-Line Portfolio Selection Using Multiplicative Updates.
ICML 1996: 243-251 |
| 66 | EE | Yoram Singer,
Manfred K. Warmuth:
Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions.
NIPS 1996: 641-647 |
| 65 | | Robert E. Schapire,
Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms.
Machine Learning 22(1-3): 95-121 (1996) |
| 64 | | Nicolò Cesa-Bianchi,
Yoav Freund,
David P. Helmbold,
Manfred K. Warmuth:
On-line Prediction and Conversion Strategies.
Machine Learning 25(1): 71-110 (1996) |
| 1995 |
|---|
| 63 | EE | 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 |
| 62 | EE | 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 |
| 61 | | David Haussler,
Jyrki Kivinen,
Manfred K. Warmuth:
Tight worst-case loss bounds for predicting with expert advice.
EuroCOLT 1995: 69-83 |
| 60 | | Peter Auer,
Manfred K. Warmuth:
Tracking the Best Disjunction.
FOCS 1995: 312-321 |
| 59 | | Mark Herbster,
Manfred K. Warmuth:
Tracking the Best Expert.
ICML 1995: 286-294 |
| 58 | | Wolfgang Maass,
Manfred K. Warmuth:
Efficient Learning with Virtual Threshold Gates.
ICML 1995: 378-386 |
| 57 | EE | David P. Helmbold,
Jyrki Kivinen,
Manfred K. Warmuth:
Worst-case Loss Bounds for Single Neurons.
NIPS 1995: 309-315 |
| 56 | EE | Peter Auer,
Mark Herbster,
Manfred K. Warmuth:
Exponentially many local minima for single neurons.
NIPS 1995: 316-322 |
| 55 | EE | Jyrki Kivinen,
Manfred K. Warmuth:
Additive versus exponentiated gradient updates for linear prediction.
STOC 1995: 209-218 |
| 54 | | Nick Littlestone,
Philip M. Long,
Manfred K. Warmuth:
On-line Learning of Linear Functions.
Computational Complexity 5(1): 1-23 (1995) |
| 53 | | David P. Helmbold,
Manfred K. Warmuth:
On Weak Learning.
J. Comput. Syst. Sci. 50(3): 551-573 (1995) |
| 52 | | Sally A. Goldman,
Manfred K. Warmuth:
Learning Binary Relations Using Weighted Majority Voting.
Machine Learning 20(3): 245-271 (1995) |
| 51 | | Sally Floyd,
Manfred K. Warmuth:
Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension.
Machine Learning 21(3): 269-304 (1995) |
| 1994 |
|---|
| 50 | | Robert E. Schapire,
Manfred K. Warmuth:
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms.
ICML 1994: 266-274 |
| 49 | | Nicolò Cesa-Bianchi,
Anders Krogh,
Manfred K. Warmuth:
Bounds on approximate steepest descent for likelihood maximization in exponential families.
IEEE Transactions on Information Theory 40(4): 1215- (1994) |
| 48 | | 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) |
| 47 | | Nick Littlestone,
Manfred K. Warmuth:
The Weighted Majority Algorithm
Inf. Comput. 108(2): 212-261 (1994) |
| 46 | | Philip M. Long,
Manfred K. Warmuth:
Composite Geometric Concepts and Polynomial Predictability
Inf. Comput. 113(2): 230-252 (1994) |
| 45 | | David Haussler,
Nick Littlestone,
Manfred K. Warmuth:
Predicting \0,1\-Functions on Randomly Drawn Points
Inf. Comput. 115(2): 248-292 (1994) |
| 1993 |
|---|
| 44 | EE | 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 |
| 43 | EE | Sally A. Goldman,
Manfred K. Warmuth:
Learning Binary Relations Using Weighted Majority Voting.
COLT 1993: 453-462 |
| 42 | EE | 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 |
| 41 | EE | Leonard Pitt,
Manfred K. Warmuth:
The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial.
J. ACM 40(1): 95-142 (1993) |
| 40 | | Shlomo Moran,
Manfred K. Warmuth:
Gap Theorems for Distributed Computation.
SIAM J. Comput. 22(2): 379-394 (1993) |
| 1992 |
|---|
| 39 | EE | David P. Helmbold,
Manfred K. Warmuth:
Some Weak Learning Results.
COLT 1992: 399-412 |
| 38 | | Naoki Abe,
Manfred K. Warmuth:
On the Computational Complexity of Approximating Distributions by Probabilistic Automata.
Machine Learning 9: 205-260 (1992) |
| 37 | | David P. Helmbold,
Robert H. Sloan,
Manfred K. Warmuth:
Learning Integer Lattices.
SIAM J. Comput. 21(2): 240-266 (1992) |
| 1991 |
|---|
| 36 | EE | Naoki Abe,
Manfred K. Warmuth,
Jun-ichi Takeuchi:
Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence.
COLT 1991: 277-289 |
| 35 | | Nick Littlestone,
Philip M. Long,
Manfred K. Warmuth:
On-Line Learning of Linear Functions
STOC 1991: 465-475 |
| 34 | | David Haussler,
Michael J. Kearns,
Nick Littlestone,
Manfred K. Warmuth:
Equivalence of Models for Polynomial Learnability
Inf. Comput. 95(2): 129-161 (1991) |
| 1990 |
|---|
| 33 | EE | Philip M. Long,
Manfred K. Warmuth:
Composite Geometric Concepts and Polynomial Predictability.
COLT 1990: 273-287 |
| 32 | EE | David P. Helmbold,
Robert H. Sloan,
Manfred K. Warmuth:
Learning Integer Lattices.
COLT 1990: 288-302 |
| 31 | EE | Naoki Abe,
Manfred K. Warmuth:
On the Computational Complexity of Approximating Distributions by Probabilistic Automata.
COLT 1990: 52-66 |
| 30 | | Leonard Pitt,
Manfred K. Warmuth:
Prediction-Preserving Reducibility.
J. Comput. Syst. Sci. 41(3): 430-467 (1990) |
| 29 | | Daniel Ratner,
Manfred K. Warmuth:
NxN Puzzle and Related Relocation Problem.
J. Symb. Comput. 10(2): 111-138 (1990) |
| 28 | | David P. Helmbold,
Robert H. Sloan,
Manfred K. Warmuth:
Learning Nested Differences of Intersection-Closed Concept Classes.
Machine Learning 5: 165-196 (1990) |
| 1989 |
|---|
| 27 | | Manfred K. Warmuth:
Towards Representation Independence in PAC Learning.
AII 1989: 78-103 |
| 26 | EE | David P. Helmbold,
Robert H. Sloan,
Manfred K. Warmuth:
Learning Nested Differences of Intersection-Closed Concept Classes.
COLT 1989: 41-56 |
| 25 | | 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 |
| 24 | | Nick Littlestone,
Manfred K. Warmuth:
The Weighted Majority Algorithm
FOCS 1989: 256-261 |
| 23 | | Leonard Pitt,
Manfred K. Warmuth:
The Minimum Consistent DFA Problem Cannot Be Approximated within any Polynomial
STOC 1989: 421-432 |
| 22 | | Leonard Pitt,
Manfred K. Warmuth:
The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial (abstract).
Structure in Complexity Theory Conference 1989: 230 |
| 21 | | Jakob Gonczarowski,
Manfred K. Warmuth:
Scattered Versus Context-Sensitive Rewriting.
Acta Inf. 27(1): 81-95 (1989) |
| 20 | | Richard J. Anderson,
Ernst W. Mayr,
Manfred K. Warmuth:
Parallel Approximation Algorithms for Bin Packing
Inf. Comput. 82(3): 262-277 (1989) |
| 19 | EE | Anselm Blumer,
Andrzej Ehrenfeucht,
David Haussler,
Manfred K. Warmuth:
Learnability and the Vapnik-Chervonenkis dimension.
J. ACM 36(4): 929-965 (1989) |
| 18 | | Barbara B. Simons,
Manfred K. Warmuth:
A Fast Algorithm for Multiprocessor Scheduling of Unit-Length Jobs.
SIAM J. Comput. 18(4): 690-710 (1989) |
| 1988 |
|---|
| 17 | EE | David Haussler,
Nick Littlestone,
Manfred K. Warmuth:
Predicting {0, 1}-Functions on Randomly Drawn Points.
COLT 1988: 280-296 |
| 16 | EE | David Haussler,
Michael J. Kearns,
Nick Littlestone,
Manfred K. Warmuth:
Equivalence of Models for Polynomial Learnability.
COLT 1988: 42-55 |
| 15 | | David Haussler,
Nick Littlestone,
Manfred K. Warmuth:
Predicting {0,1}-Functions on Randomly Drawn Points (Extended Abstract)
FOCS 1988: 100-109 |
| 14 | EE | Hagit Attiya,
Marc Snir,
Manfred K. Warmuth:
Computing on an anonymous ring.
J. ACM 35(4): 845-875 (1988) |
| 1987 |
|---|
| 13 | | Anselm Blumer,
Andrzej Ehrenfeucht,
David Haussler,
Manfred K. Warmuth:
Occam's Razor.
Inf. Process. Lett. 24(6): 377-380 (1987) |
| 1986 |
|---|
| 12 | | Daniel Ratner,
Manfred K. Warmuth:
Finding a Shortest Solution for the N × N Extension of the 15-PUZZLE Is Intractable.
AAAI 1986: 168-172 |
| 11 | | Elias Dahlhaus,
Manfred K. Warmuth:
Membership for Growing Context Sensitive Grammars is Polynomial.
CAAP 1986: 85-99 |
| 10 | | Shlomo Moran,
Manfred K. Warmuth:
Gap Theorems for Distributed Computation.
PODC 1986: 131-140 |
| 9 | | Anselm Blumer,
Andrzej Ehrenfeucht,
David Haussler,
Manfred K. Warmuth:
Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis Dimension (Extended Abstract)
STOC 1986: 273-282 |
| 8 | | Elias Dahlhaus,
Manfred K. Warmuth:
Membership for Growing Context-Sensitive Grammars is Polynomial.
J. Comput. Syst. Sci. 33(3): 456-472 (1986) |
| 7 | | Danny Dolev,
Eli Upfal,
Manfred K. Warmuth:
The Parallel Complexity of Scheduling with Precedence Constraints.
J. Parallel Distrib. Comput. 3(4): 553-576 (1986) |
| 6 | | Jakob Gonczarowski,
Manfred K. Warmuth:
Manipulating Derivation Forests by Scheduling Techniques.
Theor. Comput. Sci. 45(1): 87-119 (1986) |
| 1985 |
|---|
| 5 | | Chagit Attiya,
Marc Snir,
Manfred K. Warmuth:
Computing on an Anonymous Ring.
PODC 1985: 196-203 |
| 4 | | Danny Dolev,
Manfred K. Warmuth:
Scheduling Flat Graphs.
SIAM J. Comput. 14(3): 638-657 (1985) |
| 3 | | Jakob Gonczarowski,
Manfred K. Warmuth:
Applications of Scheduling Theory to Formal Language Theory.
Theor. Comput. Sci. 37: 217-243 (1985) |
| 1984 |
|---|
| 2 | | Danny Dolev,
Manfred K. Warmuth:
Scheduling Precedence Graphs of Bounded Height.
J. Algorithms 5(1): 48-59 (1984) |
| 1 | | Manfred K. Warmuth,
David Haussler:
On the Complexity of Iterated Shuffle.
J. Comput. Syst. Sci. 28(3): 345-358 (1984) |
