2010
59Hedging Structured Concepts. Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen. COLT 2010, 93-105. Web SearchBibTeXDownload
58On-line Variance Minimization in O(n2) per Trial?. Elad Hazan, Satyen Kale, Manfred K. Warmuth. COLT 2010, 314-315. Web SearchBibTeXDownload
57Learning Rotations with Little Regret. Elad Hazan, Satyen Kale, Manfred K. Warmuth. COLT 2010, 144-154. Web SearchBibTeXDownload
2009
56Tutorial summary: Survey of boosting from an optimization perspective. Manfred K. Warmuth, S. V. N. Vishwanathan. ICML 2009, 175. Web SearchBibTeXDownload
2008
55Entropy Regularized LPBoost. Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan. ALT 2008, 256-271. Web SearchBibTeXDownload
2006
54Continuous Experts and the Binning Algorithm. Jacob Abernethy, John Langford, Manfred K. Warmuth. COLT 2006, 544-558. Web SearchBibTeXDownload
53The p-norm generalization of the LMS algorithm for adaptive filtering. Jyrki Kivinen, Manfred K. Warmuth, Babak Hassibi. IEEE Transactions on Signal Processing (54): 1782-1793 (2006). Web SearchBibTeXDownload
2005
52Leaving the Span. Manfred K. Warmuth, S. V. N. Vishwanathan. COLT 2005, 366-381. Web SearchBibTeXDownload
51Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. Koji Tsuda, Gunnar Rštsch, Manfred K. Warmuth. Journal of Machine Learning Research (6): 995-1018 (2005). Web SearchBibTeXDownload
2004
50Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. Koji Tsuda, Gunnar Rštsch, Manfred K. Warmuth. NIPS 2004. Web SearchBibTeXDownload
2002
49Adaptive Caching by Refetching. Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari. NIPS 2002, 1465-1472. Web SearchBibTeXDownload
2001
48Relative Loss Bounds for Multidimensional Regression Problems. Jyrki Kivinen, Manfred K. Warmuth. Machine Learning (45): 301-329 (2001). Web SearchBibTeXDownload
2000
47Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes. Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth. Electronic Colloquium on Computational Complexity (ECCC) (7) (2000). Web SearchBibTeXDownload
1999
46Boosting as Entropy Projection. Jyrki Kivinen, Manfred K. Warmuth. COLT 1999, 134-144. Web SearchBibTeXDownload
45Averaging Expert Predictions. Jyrki Kivinen, Manfred K. Warmuth. EuroCOLT 1999, 153-167. Web SearchBibTeXDownload
44Relative loss bounds for single neurons. David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth. IEEE Transactions on Neural Networks (10): 1291-1304 (1999). Web SearchBibTeXDownload
1998
43Sequential Prediction of Individual Sequences Under General Loss Functions. David Haussler, Jyrki Kivinen, Manfred K. Warmuth. IEEE Transactions on Information Theory (44): 1906-1925 (1998). Web SearchBibTeXDownload
42Efficient Learning With Virtual Threshold Gates. Wolfgang Maass, Manfred K. Warmuth. Inf. Comput. (141): 66-83 (1998). Web SearchBibTeXDownload
1997
41The Perceptron Algorithm Versus Winnow: Linear Versus Logarithmic Mistake Bounds when Few Input Variables are Relevant (Technical Note). Jyrki Kivinen, Manfred K. Warmuth, Peter Auer. Artif. Intell. (97): 325-343 (1997). Web SearchBibTeXDownload
40Exponentiated Gradient Versus Gradient Descent for Linear Predictors. Jyrki Kivinen, Manfred K. Warmuth. Inf. Comput. (132): 1-63 (1997). Web SearchBibTeXDownload
39How to use expert advice. NicolÚ Cesa-Bianchi, Yoav Freund, David Haussler, David Haussler, Robert E. Schapire, Manfred K. Warmuth. J. ACM (44): 427-485 (1997). Web SearchBibTeXDownload
38Relative Loss Bounds for Multidimensional Regression Problems. Jyrki Kivinen, Manfred K. Warmuth. NIPS 1997. Web SearchBibTeX
37Using and Combining Predictors That Specialize. Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth. STOC 1997, 334-343. Web SearchBibTeXDownload
1996
36Learning of Depth Two Neural Networks with Constant Fan-In at the Hidden Nodes (Extended Abstract). Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth. COLT 1996, 333-343. Web SearchBibTeXDownload
35On-line Prediction and Conversion Strategies. NicolÚ Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth. Machine Learning (25): 71-110 (1996). Web SearchBibTeXDownload
1995
34The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant. Jyrki Kivinen, Manfred K. Warmuth. COLT 1995, 289-296. Web SearchBibTeXDownload
33Tight worst-case loss bounds for predicting with expert advice. David Haussler, Jyrki Kivinen, Manfred K. Warmuth. EuroCOLT 1995, 69-83. Web SearchBibTeXDownload
32Efficient Learning with Virtual Threshold Gates. Wolfgang Maass, Manfred K. Warmuth. ICML 1995, 378-386. Web SearchBibTeX
31Learning Binary Relations Using Weighted Majority Voting. Sally A. Goldman, Manfred K. Warmuth. Machine Learning (20): 245-271 (1995). Web SearchBibTeXDownload
30Worst-case Loss Bounds for Single Neurons. David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth. NIPS 1995, 309-315. Web SearchBibTeXDownload
29Additive versus exponentiated gradient updates for linear prediction. Jyrki Kivinen, Manfred K. Warmuth. STOC 1995, 209-218. Web SearchBibTeXDownload
1994
28Predicting \\0,1\\-Functions on Randomly Drawn Points. David Haussler, Nick Littlestone, Manfred K. Warmuth. Inf. Comput. (115): 248-292 (1994). Web SearchBibTeXDownload
27The Distributed Bit Complexity of the Ring: From the Anonymous to the Non-anonymous Case. Hans L. Bodlaender, Shlomo Moran, Manfred K. Warmuth. Inf. Comput. (108): 34-50 (1994). Web SearchBibTeXDownload
1993
26Learning Binary Relations Using Weighted Majority Voting. Sally A. Goldman, Manfred K. Warmuth. COLT 1993, 453-462. Web SearchBibTeXDownload
25The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial. Leonard Pitt, Manfred K. Warmuth. J. ACM (40): 95-142 (1993). Web SearchBibTeXDownload
24Gap Theorems for Distributed Computation. Shlomo Moran, Manfred K. Warmuth. SIAM J. Comput. (22): 379-394 (1993). Web SearchBibTeXDownload
23How to use expert advice. NicolÚ Cesa-Bianchi, Yoav Freund, David Haussler, David Haussler, Robert E. Schapire, Manfred K. Warmuth. STOC 1993, 382-391. Web SearchBibTeXDownload
1992
22On the Computational Complexity of Approximating Distributions by Probabilistic Automata. Naoki Abe, Manfred K. Warmuth. Machine Learning (9): 205-260 (1992). Web SearchBibTeXDownload
1991
21Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence. Naoki Abe, Manfred K. Warmuth, Jun-ichi Takeuchi. COLT 1991, 277-289. Web SearchBibTeXDownload
20Equivalence of Models for Polynomial Learnability. David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth. Inf. Comput. (95): 129-161 (1991). Web SearchBibTeXDownload
1990
19On the Computational Complexity of Approximating Distributions by Probabilistic Automata. Naoki Abe, Manfred K. Warmuth. COLT 1990, 52-66. Web SearchBibTeXDownload
18Prediction-Preserving Reducibility. Leonard Pitt, Manfred K. Warmuth. J. Comput. Syst. Sci. (41): 430-467 (1990). Web SearchBibTeXDownload
1989
17The Distributed Bit Complexity of the Ring: From the Anonymous to the Non-anonymous Case. Hans L. Bodlaender, Shlomo Moran, Manfred K. Warmuth. FCT 1989, 58-67. Web SearchBibTeX
16Learnability and the Vapnik-Chervonenkis dimension. Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth. J. ACM (36): 929-965 (1989). Web SearchBibTeXDownload
15The Minimum Consistent DFA Problem Cannot Be Approximated within any Polynomial. Leonard Pitt, Manfred K. Warmuth. STOC 1989, 421-432. Web SearchBibTeXDownload
14The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial (abstract). Leonard Pitt, Manfred K. Warmuth. Structure in Complexity Theory Conference 1989, 230. Web SearchBibTeXDownload
1988
13Equivalence of Models for Polynomial Learnability. David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth. COLT 1988, 42-55. Web SearchBibTeXDownload
12Predicting {0, 1}-Functions on Randomly Drawn Points. David Haussler, Nick Littlestone, Manfred K. Warmuth. COLT 1988, 280-296. Web SearchBibTeXDownload
11Predicting {0,1}-Functions on Randomly Drawn Points (Extended Abstract). David Haussler, Nick Littlestone, Manfred K. Warmuth. FOCS 1988, 100-109. Web SearchBibTeXDownload
10Computing on an anonymous ring. Hagit Attiya, Marc Snir, Manfred K. Warmuth. J. ACM (35): 845-875 (1988). Web SearchBibTeXDownload
9Reductions among prediction problems: on the difficulty of predicting automata. Leonard Pitt, Manfred K. Warmuth. Structure in Complexity Theory Conference 1988, 60-69. Web SearchBibTeXDownload
1987
8Occam's Razor. Carl Edward Rasmussen, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth. Inf. Process. Lett. (24): 377-380 (1987). Web SearchBibTeXDownload
1986
7The Parallel Complexity of Scheduling with Precedence Constraints. Danny Dolev, Eli Upfal, Manfred K. Warmuth. J. Parallel Distrib. Comput. (3): 553-576 (1986). Web SearchBibTeXDownload
6Gap Theorems for Distributed Computation. Shlomo Moran, Manfred K. Warmuth. PODC 1986, 131-140. Web SearchBibTeXDownload
5Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis Dimension (Extended Abstract). Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth. STOC 1986, 273-282. Web SearchBibTeXDownload
1985
4Computing on an Anonymous Ring. Chagit Attiya, Marc Snir, Manfred K. Warmuth. PODC 1985, 196-203. Web SearchBibTeXDownload
3Scheduling Flat Graphs. Danny Dolev, Manfred K. Warmuth. SIAM J. Comput. (14): 638-657 (1985). Web SearchBibTeXDownload
1984
2Scheduling Precedence Graphs of Bounded Height. Danny Dolev, Manfred K. Warmuth. J. Algorithms (5): 48-59 (1984). Web SearchBibTeXDownload
1On the Complexity of Iterated Shuffle. Manfred K. Warmuth, David Haussler. J. Comput. Syst. Sci. (28): 345-358 (1984). Web SearchBibTeXDownload
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