Manfred K. Warmuth

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)

Manfred K. Warmuth, Dima Kuzmin: Online variance minimization. Machine Learning 87(1): 1-32 (2012)

Manfred K. Warmuth, Wojciech Kotlowski, Shuisheng Zhou: Kernelization of Matrix Updates, When and How? ALT 2012: 350-364

Wouter M. Koolen, Dmitry Adamskiy, Manfred K. Warmuth: Putting Bayes to sleep. NIPS 2012: 135-143

Wojciech Kotlowski, Manfred K. Warmuth: Minimax Algorithm for Learning Rotations. Journal of Machine Learning Research - Proceedings Track 19: 821-824 (2011)

Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold: Combining Initial Segments of Lists. ALT 2011: 219-233

Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth: Learning Eigenvectors for Free. NIPS 2011: 945-953

Manfred K. Warmuth, Dima Kuzmin: Bayesian generalized probability calculus for density matrices. Machine Learning 78(1-2): 63-101 (2010)

Manfred K. Warmuth: The Blessing and the Curse of the Multiplicative Updates. ALT 2010: 31

Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen: Hedging Structured Concepts. COLT 2010: 93-105

Elad Hazan, Satyen Kale, Manfred K. Warmuth: Learning Rotations with Little Regret. COLT 2010: 144-154

Elad Hazan, Satyen Kale, Manfred K. Warmuth: On-line Variance Minimization in O(n2) per Trial? COLT 2010: 314-315

Manfred K. Warmuth: The Blessing and the Curse of the Multiplicative Updates. Discovery Science 2010: 382

Shuisheng Zhou, Manfred K. Warmuth, Yinli Dong, Feng Ye: New combination coefficients for AdaBoost algorithms. ICNC 2010: 3194-3198

Jacob Abernethy, Manfred K. Warmuth: Repeated Games against Budgeted Adversaries. NIPS 2010: 1-9

David P. Helmbold, Manfred K. Warmuth: Learning Permutations with Exponential Weights. Journal of Machine Learning Research 10: 1705-1736 (2009)

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

Manfred K. Warmuth, S. V. N. Vishwanathan: Tutorial summary: Survey of boosting from an optimization perspective. ICML 2009: 175

Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan: Entropy Regularized LPBoost. ALT 2008: 256-271

Jacob Abernethy, Manfred K. Warmuth, Joel Yellin: When Random Play is Optimal Against an Adversary. COLT 2008: 437-446

Adam M. Smith, Manfred K. Warmuth: Learning Rotations. COLT 2008: 517

Dima Kuzmin, Manfred K. Warmuth: Unlabeled Compression Schemes for Maximum Classes. Journal of Machine Learning Research 8: 2047-2081 (2007)

David P. Helmbold, Manfred K. Warmuth: Learning Permutations with Exponential Weights. COLT 2007: 469-483

Manfred K. Warmuth: When Is There a Free Matrix Lunch? COLT 2007: 630-632

Dima Kuzmin, Manfred K. Warmuth: Online kernel PCA with entropic matrix updates. ICML 2007: 465-472

Manfred K. Warmuth: Winnowing subspaces. ICML 2007: 999-1006

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

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)

Manfred K. Warmuth, Dima Kuzmin: Online Variance Minimization. COLT 2006: 514-528

Jacob Abernethy, John Langford, Manfred K. Warmuth: Continuous Experts and the Binning Algorithm. COLT 2006: 544-558

Manfred K. Warmuth: Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints. COLT 2006: 653-654

Manfred K. Warmuth, Jun Liao, Gunnar Rätsch: Totally corrective boosting algorithms that maximize the margin. ICML 2006: 1001-1008

Manfred K. Warmuth, Dima Kuzmin: Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension. NIPS 2006: 1481-1488

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

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)

Gunnar Rätsch, Manfred K. Warmuth: Efficient Margin Maximizing with Boosting. Journal of Machine Learning Research 6: 2131-2152 (2005)

Manfred K. Warmuth, S. V. N. Vishwanathan: Leaving the Span. COLT 2005: 366-381

Dima Kuzmin, Manfred K. Warmuth: Unlabeled Compression Schemes for Maximum Classes, . COLT 2005: 591-605

Dima Kuzmin, Manfred K. Warmuth: Optimum Follow the Leader Algorithm. COLT 2005: 684-686

Manfred K. Warmuth: A Bayes Rule for Density Matrices. NIPS 2005

Manfred K. Warmuth: The Optimal PAC Algorithm. COLT 2004: 641-642

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

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)

Eiji Takimoto, Manfred K. Warmuth: Path Kernels and Multiplicative Updates. Journal of Machine Learning Research 4: 773-818 (2003)

Jürgen Forster, Manfred K. Warmuth: Relative Loss Bounds for Temporal-Difference Learning. Machine Learning 51(1): 23-50 (2003)

Manfred K. Warmuth: Compressing to VC Dimension Many Points. COLT 2003: 743-744

Ashutosh Garg, Manfred K. Warmuth: Inline updates for HMMs. INTERSPEECH 2003

Rita Singh, Manfred K. Warmuth, Bhiksha Raj, Paul Lamere: Classification with free energy at raised temperatures. INTERSPEECH 2003

Kohei Hatano, Manfred K. Warmuth: Boosting versus Covering. NIPS 2003

Bernhard Schölkopf, Manfred K. Warmuth (Eds.): 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

Jürgen Forster, Manfred K. Warmuth: Relative Expected Instantaneous Loss Bounds. J. Comput. Syst. Sci. 64(1): 76-102 (2002)

Olivier Bousquet, Manfred K. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. Journal of Machine Learning Research 3: 363-396 (2002)

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)

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)

Eiji Takimoto, Manfred K. Warmuth: Path Kernels and Multiplicative Updates. COLT 2002: 74-89

Gunnar Rätsch, Manfred K. Warmuth: Maximizing the Margin with Boosting. COLT 2002: 334-350

Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari: Adaptive Caching by Refetching. NIPS 2002: 1465-1472

Mark Herbster, Manfred K. Warmuth: Tracking the Best Linear Predictor. Journal of Machine Learning Research 1: 281-309 (2001)

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)

Jyrki Kivinen, Manfred K. Warmuth: Relative Loss Bounds for Multidimensional Regression Problems. Machine Learning 45(3): 301-329 (2001)

Olivier Bousquet, Manfred K. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. COLT/EuroCOLT 2001: 31-47

Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth: On the Convergence of Leveraging. NIPS 2001: 487-494

Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen: Active Learning in the Drug Discovery Process. NIPS 2001: 1449-1456

Eiji Takimoto, Manfred K. Warmuth: The Last-Step Minimax Algorithm. ALT 2000: 279-290

Jürgen Forster, Manfred K. Warmuth: Relative Expected Instantaneous Loss Bounds. COLT 2000: 90-99

Eiji Takimoto, Manfred K. Warmuth: The Minimax Strategy for Gaussian Density Estimation. pp. COLT 2000: 100-106

Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller: Barrier Boosting. COLT 2000: 170-179

Jürgen Forster, Manfred K. Warmuth: Relative Loss Bounds for Temporal-Difference Learning. ICML 2000: 295-302

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)

Peter Auer, Manfred K. Warmuth: Tracking the best disjunction. Electronic Colloquium on Computational Complexity (ECCC) 7(70) (2000)

David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth: Relative loss bounds for single neurons. IEEE Transactions on Neural Networks 10(6): 1291-1304 (1999)

Eiji Takimoto, Manfred K. Warmuth: Predicting Nearly as well as the best Pruning of a Planar Decision Graph. ALT 1999: 335-346

Jyrki Kivinen, Manfred K. Warmuth: Boosting as Entropy Projection. COLT 1999: 134-144

David P. Helmbold, Sandra Panizza, Manfred K. Warmuth: Direct and Indirect Algorithms for On-line Learning of Disjunctions. EuroCOLT 1999: 138-152

Jyrki Kivinen, Manfred K. Warmuth: Averaging Expert Predictions. EuroCOLT 1999: 153-167

Katy S. Azoury, Manfred K. Warmuth: Relative Loss Bounds for On-line Density Estirnation with the Exponential Family of Distributions. UAI 1999: 31-40

Wolfgang Maass, Manfred K. Warmuth: Efficient Learning With Virtual Threshold Gates. Inf. Comput. 141(1): 66-83 (1998)

Peter Auer, Manfred K. Warmuth: Tracking the Best Disjunction. Machine Learning 32(2): 127-150 (1998)

Mark Herbster, Manfred K. Warmuth: Tracking the Best Expert. Machine Learning 32(2): 151-178 (1998)

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)

Mark Herbster, Manfred K. Warmuth: Tracking the Best Regressor. COLT 1998: 24-31

Claudio Gentile, Manfred K. Warmuth: Linear Hinge Loss and Average Margin. NIPS 1998: 225-231

Yoram Singer, Manfred K. Warmuth: Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. NIPS 1998: 578-584

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)

Jyrki Kivinen, Manfred K. Warmuth: Exponentiated Gradient Versus Gradient Descent for Linear Predictors. Inf. Comput. 132(1): 1-63 (1997)

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)

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)

Manfred K. Warmuth: Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. EuroCOLT 1997: 1-2

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

Manfred K. Warmuth: Relative Loss Bounds, the Minimum Relative Entropy Principle, and EM. NIPS 1997

Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: Using and Combining Predictors That Specialize. STOC 1997: 334-343

Robert E. Schapire, Manfred K. Warmuth: On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. Machine Learning 22(1-3): 95-121 (1996)

Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth: On-line Prediction and Conversion Strategies. Machine Learning 25(1): 71-110 (1996)

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 Netw. Learning Syst. 7(3): 604-619 (1996)

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

David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: On-Line Portfolio Selection Using Multiplicative Updates. ICML 1996: 243-251

Yoram Singer, Manfred K. Warmuth: Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions. NIPS 1996: 641-647

Nick Littlestone, Philip M. Long, Manfred K. Warmuth: On-line Learning of Linear Functions. Computational Complexity 5(1): 1-23 (1995)

David P. Helmbold, Manfred K. Warmuth: On Weak Learning. J. Comput. Syst. Sci. 50(3): 551-573 (1995)

Sally A. Goldman, Manfred K. Warmuth: Learning Binary Relations Using Weighted Majority Voting. Machine Learning 20(3): 245-271 (1995)

Sally Floyd, Manfred K. Warmuth: Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. Machine Learning 21(3): 269-304 (1995)

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

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

David Haussler, Jyrki Kivinen, Manfred K. Warmuth: Tight worst-case loss bounds for predicting with expert advice. EuroCOLT 1995: 69-83

Peter Auer, Manfred K. Warmuth: Tracking the Best Disjunction. FOCS 1995: 312-321

Mark Herbster, Manfred K. Warmuth: Tracking the Best Expert. ICML 1995: 286-294

Wolfgang Maass, Manfred K. Warmuth: Efficient Learning with Virtual Threshold Gates. ICML 1995: 378-386

David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth: Worst-case Loss Bounds for Single Neurons. NIPS 1995: 309-315

Peter Auer, Mark Herbster, Manfred K. Warmuth: Exponentially many local minima for single neurons. NIPS 1995: 316-322

Jyrki Kivinen, Manfred K. Warmuth: Additive versus exponentiated gradient updates for linear prediction. STOC 1995: 209-218

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)

Nick Littlestone, Manfred K. Warmuth: The Weighted Majority Algorithm. Inf. Comput. 108(2): 212-261 (1994)

Philip M. Long, Manfred K. Warmuth: Composite Geometric Concepts and Polynomial Predictability. Inf. Comput. 113(2): 230-252 (1994)

David Haussler, Nick Littlestone, Manfred K. Warmuth: Predicting \0,1\-Functions on Randomly Drawn Points. Inf. Comput. 115(2): 248-292 (1994)

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): 1215-1218 (1994)

Robert E. Schapire, Manfred K. Warmuth: On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. ICML 1994: 266-274

Manfred K. Warmuth (Ed.): 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

Leonard Pitt, Manfred K. Warmuth: The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial. J. ACM 40(1): 95-142 (1993)

Shlomo Moran, Manfred K. Warmuth: Gap Theorems for Distributed Computation. SIAM J. Comput. 22(2): 379-394 (1993)

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

Sally A. Goldman, Manfred K. Warmuth: Learning Binary Relations Using Weighted Majority Voting. COLT 1993: 453-462

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

Naoki Abe, Manfred K. Warmuth: On the Computational Complexity of Approximating Distributions by Probabilistic Automata. Machine Learning 9: 205-260 (1992)

David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth: Learning Integer Lattices. SIAM J. Comput. 21(2): 240-266 (1992)

David P. Helmbold, Manfred K. Warmuth: Some Weak Learning Results. COLT 1992: 399-412

David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth: Equivalence of Models for Polynomial Learnability. Inf. Comput. 95(2): 129-161 (1991)

Naoki Abe, Manfred K. Warmuth, Jun-ichi Takeuchi: Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence. COLT 1991: 277-289

Nick Littlestone, Philip M. Long, Manfred K. Warmuth: On-Line Learning of Linear Functions. STOC 1991: 465-475

Manfred K. Warmuth, Leslie G. Valiant (Eds.): 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

Leonard Pitt, Manfred K. Warmuth: Prediction-Preserving Reducibility. J. Comput. Syst. Sci. 41(3): 430-467 (1990)

Daniel Ratner, Manfred K. Warmuth: NxN Puzzle and Related Relocation Problem. J. Symb. Comput. 10(2): 111-138 (1990)

David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth: Learning Nested Differences of Intersection-Closed Concept Classes. Machine Learning 5: 165-196 (1990)

Naoki Abe, Manfred K. Warmuth: On the Computational Complexity of Approximating Distributions by Probabilistic Automata. COLT 1990: 52-66

Philip M. Long, Manfred K. Warmuth: Composite Geometric Concepts and Polynomial Predictability. COLT 1990: 273-287

David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth: Learning Integer Lattices. COLT 1990: 288-302

Jakob Gonczarowski, Manfred K. Warmuth: Scattered Versus Context-Sensitive Rewriting. Acta Inf. 27(1): 81-95 (1989)

Richard J. Anderson, Ernst W. Mayr, Manfred K. Warmuth: Parallel Approximation Algorithms for Bin Packing. Inf. Comput. 82(3): 262-277 (1989)

Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth: Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36(4): 929-965 (1989)

Barbara B. Simons, Manfred K. Warmuth: A Fast Algorithm for Multiprocessor Scheduling of Unit-Length Jobs. SIAM J. Comput. 18(4): 690-710 (1989)

Manfred K. Warmuth: Towards Representation Independence in PAC Learning. AII 1989: 78-103

Leonard Pitt, Manfred K. Warmuth: The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial (abstract). Structure in Complexity Theory Conference 1989: 230

David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth: Learning Nested Differences of Intersection-Closed Concept Classes. COLT 1989: 41-56

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

Nick Littlestone, Manfred K. Warmuth: The Weighted Majority Algorithm. FOCS 1989: 256-261

Leonard Pitt, Manfred K. Warmuth: The Minimum Consistent DFA Problem Cannot Be Approximated within any Polynomial. STOC 1989: 421-432

Ronald L. Rivest, David Haussler, Manfred K. Warmuth (Eds.): 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

Hagit Attiya, Marc Snir, Manfred K. Warmuth: Computing on an anonymous ring. J. ACM 35(4): 845-875 (1988)

Leonard Pitt, Manfred K. Warmuth: Reductions among prediction problems: on the difficulty of predicting automata. Structure in Complexity Theory Conference 1988: 60-69

David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth: Equivalence of Models for Polynomial Learnability. COLT 1988: 42-55

David Haussler, Nick Littlestone, Manfred K. Warmuth: Predicting {0, 1}-Functions on Randomly Drawn Points. COLT 1988: 280-296

David Haussler, Nick Littlestone, Manfred K. Warmuth: Predicting {0,1}-Functions on Randomly Drawn Points (Extended Abstract). FOCS 1988: 100-109

Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth: Occam's Razor. Inf. Process. Lett. 24(6): 377-380 (1987)

Elias Dahlhaus, Manfred K. Warmuth: Membership for Growing Context-Sensitive Grammars is Polynomial. J. Comput. Syst. Sci. 33(3): 456-472 (1986)

Danny Dolev, Eli Upfal, Manfred K. Warmuth: The Parallel Complexity of Scheduling with Precedence Constraints. J. Parallel Distrib. Comput. 3(4): 553-576 (1986)

Jakob Gonczarowski, Manfred K. Warmuth: Manipulating Derivation Forests by Scheduling Techniques. Theor. Comput. Sci. 45(1): 87-119 (1986)

Daniel Ratner, Manfred K. Warmuth: Finding a Shortest Solution for the N × N Extension of the 15-PUZZLE Is Intractable. AAAI 1986: 168-172

Elias Dahlhaus, Manfred K. Warmuth: Membership for Growing Context Sensitive Grammars is Polynomial. CAAP 1986: 85-99

Shlomo Moran, Manfred K. Warmuth: Gap Theorems for Distributed Computation. PODC 1986: 131-140

Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth: Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis Dimension (Extended Abstract). STOC 1986: 273-282

Danny Dolev, Manfred K. Warmuth: Scheduling Flat Graphs. SIAM J. Comput. 14(3): 638-657 (1985)

Jakob Gonczarowski, Manfred K. Warmuth: Applications of Scheduling Theory to Formal Language Theory. Theor. Comput. Sci. 37: 217-243 (1985)

Chagit Attiya, Marc Snir, Manfred K. Warmuth: Computing on an Anonymous Ring. PODC 1985: 196-203

Danny Dolev, Manfred K. Warmuth: Scheduling Precedence Graphs of Bounded Height. J. Algorithms 5(1): 48-59 (1984)

Manfred K. Warmuth, David Haussler: On the Complexity of Iterated Shuffle. J. Comput. Syst. Sci. 28(3): 345-358 (1984)