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Manfred K. Warmuth Vis

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*2009
140EEManfred K. Warmuth, S. V. N. Vishwanathan: Tutorial summary: Survey of boosting from an optimization perspective. ICML 2009: 175
2008
139EEManfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan: Entropy Regularized LPBoost. ALT 2008: 256-271
138EEJacob Abernethy, Manfred K. Warmuth, Joel Yellin: When Random Play is Optimal Against an Adversary. COLT 2008: 437-446
137EEAdam M. Smith, Manfred K. Warmuth: Learning Rotations. COLT 2008: 517
2007
136EEDavid P. Helmbold, Manfred K. Warmuth: Learning Permutations with Exponential Weights. COLT 2007: 469-483
135EEManfred K. Warmuth: When Is There a Free Matrix Lunch? COLT 2007: 630-632
134EEDima Kuzmin, Manfred K. Warmuth: Online kernel PCA with entropic matrix updates. ICML 2007: 465-472
133EEManfred K. Warmuth: Winnowing subspaces. ICML 2007: 999-1006
132EEManfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch: Boosting Algorithms for Maximizing the Soft Margin. NIPS 2007
2006
131EEManfred K. Warmuth, Dima Kuzmin: Online Variance Minimization. COLT 2006: 514-528
130EEJacob Abernethy, John Langford, Manfred K. Warmuth: Continuous Experts and the Binning Algorithm. COLT 2006: 544-558
129EEManfred K. Warmuth: Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints. COLT 2006: 653-654
128EEManfred K. Warmuth, Jun Liao, Gunnar Rätsch: Totally corrective boosting algorithms that maximize the margin. ICML 2006: 1001-1008
127EEManfred K. Warmuth, Dima Kuzmin: Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension. NIPS 2006: 1481-1488
126EEManfred K. Warmuth, Dima Kuzmin: A Bayesian Probability Calculus for Density Matrices. UAI 2006
125EEManfred K. Warmuth: A Bayesian Probability Calculus for Density Matrices. UAI 2006
124EEJyrki 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
123EEManfred K. Warmuth, S. V. N. Vishwanathan: Leaving the Span. COLT 2005: 366-381
122EEDima Kuzmin, Manfred K. Warmuth: Unlabeled Compression Schemes for Maximum Classes, . COLT 2005: 591-605
121EEDima Kuzmin, Manfred K. Warmuth: Optimum Follow the Leader Algorithm. COLT 2005: 684-686
120EEManfred K. Warmuth: A Bayes Rule for Density Matrices. NIPS 2005
119EEGunnar Rätsch, Manfred K. Warmuth: Efficient Margin Maximizing with Boosting. Journal of Machine Learning Research 6: 2131-2152 (2005)
118EEKoji 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
117EEManfred K. Warmuth: The Optimal PAC Algorithm. COLT 2004: 641-642
116EEKoji 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
114EEManfred K. Warmuth: Compressing to VC Dimension Many Points. COLT 2003: 743-744
113EEKohei Hatano, Manfred K. Warmuth: Boosting versus Covering. NIPS 2003
112EEManfred 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)
111EEEiji 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
109EEGunnar Rätsch, Manfred K. Warmuth: Maximizing the Margin with Boosting. COLT 2002: 334-350
108EEEiji Takimoto, Manfred K. Warmuth: Path Kernels and Multiplicative Updates. COLT 2002: 74-89
107EERobert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari: Adaptive Caching by Refetching. NIPS 2002: 1465-1472
106EEJürgen Forster, Manfred K. Warmuth: Relative Expected Instantaneous Loss Bounds. J. Comput. Syst. Sci. 64(1): 76-102 (2002)
105EEOlivier Bousquet, Manfred K. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. Journal of Machine Learning Research 3: 363-396 (2002)
104EEDavid 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
102EEOlivier Bousquet, Manfred K. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. COLT/EuroCOLT 2001: 31-47
101EEManfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen: Active Learning in the Drug Discovery Process. NIPS 2001: 1449-1456
100EEGunnar Rätsch, Sebastian Mika, Manfred K. Warmuth: On the Convergence of Leveraging. NIPS 2001: 487-494
99EEMark 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
96EEEiji 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
91EEPeter 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)
90EEPeter Auer, Manfred K. Warmuth: Tracking the best disjunction Electronic Colloquium on Computational Complexity (ECCC) 7(70): (2000)
1999
89EEEiji Takimoto, Manfred K. Warmuth: Predicting Nearly as well as the best Pruning of a Planar Decision Graph. ATL 1999: 335-346
88EEJyrki Kivinen, Manfred K. Warmuth: Boosting as Entropy Projection. COLT 1999: 134-144
87EEDavid P. Helmbold, Sandra Panizza, Manfred K. Warmuth: Direct and Indirect Algorithms for On-line Learning of Disjunctions. EuroCOLT 1999: 138-152
86EEJyrki Kivinen, Manfred K. Warmuth: Averaging Expert Predictions EuroCOLT 1999: 153-167
85EEKaty S. Azoury, Manfred K. Warmuth: Relative Loss Bounds for On-line Density Estirnation with the Exponential Family of Distributions. UAI 1999: 31-40
84EEDavid P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth: Relative loss bounds for single neurons. IEEE Transactions on Neural Networks 10(6): 1291-1304 (1999)
1998
83EEMark Herbster, Manfred K. Warmuth: Tracking the Best Regressor. COLT 1998: 24-31
82EEClaudio Gentile, Manfred K. Warmuth: Linear Hinge Loss and Average Margin. NIPS 1998: 225-231
81EEYoram 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
73EEYoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: Using and Combining Predictors That Specialize. STOC 1997: 334-343
72EEJyrki 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)
70EENicolò 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
68EEPeter 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
66EEYoram 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
63EEJyrki Kivinen, Manfred K. Warmuth: The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant. COLT 1995: 289-296
62EEDavid 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
57EEDavid P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth: Worst-case Loss Bounds for Single Neurons. NIPS 1995: 309-315
56EEPeter Auer, Mark Herbster, Manfred K. Warmuth: Exponentially many local minima for single neurons. NIPS 1995: 316-322
55EEJyrki 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
44EENicolò 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
43EESally A. Goldman, Manfred K. Warmuth: Learning Binary Relations Using Weighted Majority Voting. COLT 1993: 453-462
42EENicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth: How to use expert advice. STOC 1993: 382-391
41EELeonard 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
39EEDavid 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
36EENaoki 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
33EEPhilip M. Long, Manfred K. Warmuth: Composite Geometric Concepts and Polynomial Predictability. COLT 1990: 273-287
32EEDavid P. Helmbold, Robert H. Sloan, Manfred K. Warmuth: Learning Integer Lattices. COLT 1990: 288-302
31EENaoki 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
26EEDavid 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)
19EEAnselm 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
17EEDavid Haussler, Nick Littlestone, Manfred K. Warmuth: Predicting {0, 1}-Functions on Randomly Drawn Points. COLT 1988: 280-296
16EEDavid 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
14EEHagit 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
11EEElias 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)

Coauthor Index

1Naoki Abe [31] [36] [38]
2Jacob Abernethy [130] [138]
3Richard J. Anderson [20]
4Ismail Ari [107]
5Hagit Attiya (Chagit Attiya) [5] [14]
6Peter Auer [56] [60] [68] [72] [78] [90] [91]
7Katy S. Azoury [85] [98]
8Anselm Blumer [9] [13] [19]
9Hans L. Bodlaender [25] [48]
10Olivier Bousquet [102] [105]
11Scott A. Brandt [107]
12Nicolò Cesa-Bianchi [42] [44] [49] [64] [70]
13Elias Dahlhaus [8] [11]
14Danny Dolev [2] [4] [7]
15Andrzej Ehrenfeucht [9] [13] [19]
16Sally Floyd [51]
17Jürgen Forster [92] [93] [106] [110]
18Yoav Freund [42] [64] [70] [73]
19Claudio Gentile [82]
20Karen A. Glocer [132] [139]
21Sally A. Goldman [43] [52]
22Jakob Gonczarowski [3] [6] [21]
23Robert B. Gramacy [107]
24Babak Hassibi [124]
25Kohei Hatano [113]
26David Haussler [1] [9] [13] [15] [16] [17] [19] [34] [42] [45] [61] [70] [80]
27David P. Helmbold [26] [28] [32] [37] [39] [42] [53] [57] [62] [64] [67] [69] [70] [84] [87] [104] [136]
28Mark Herbster [56] [59] [77] [83] [99]
29Michael J. Kearns [16] [34]
30Jyrki Kivinen [55] [57] [61] [63] [71] [72] [75] [80] [84] [86] [88] [97] [124]
31Anders Krogh [49]
32Dima Kuzmin [121] [122] [126] [127] [131] [134]
33Stephen Kwek [68] [91]
34John Langford [130]
35Steven Lemm [94]
36Christian Lemmen [101] [112]
37Jun Liao [101] [112] [128]
38Nick Littlestone [15] [16] [17] [24] [34] [35] [45] [47] [54]
39Philip M. Long [33] [35] [44] [46] [54]
40Wolfgang Maass [58] [68] [79] [91]
41Michael Mathieson [101] [112]
42Ernst W. Mayr [20]
43Sebastian Mika [94] [100]
44Shlomo Moran [10] [25] [40] [48]
45Klaus-Robert Müller [94]
46Takashi Onoda [94]
47Sandra Panizza [87] [104]
48Leonard Pitt [22] [23] [30] [41]
49Santosh Putta [112]
50Daniel Ratner [12] [29]
51Gunnar Rätsch [94] [100] [101] [109] [112] [116] [118] [119] [128] [132]
52Robert E. Schapire [42] [50] [62] [65] [67] [69] [70] [73]
53Bernhard Schölkopf [115]
54Barbara B. Simons (Barbara Simons) [18]
55Yoram Singer [62] [66] [67] [69] [73] [81]
56Robert H. Sloan [26] [28] [32] [37]
57Adam M. Smith [137]
58Marc Snir [5] [14]
59Jun-ichi Takeuchi [36]
60Eiji Takimoto [89] [95] [96] [103] [108] [111]
61Koji Tsuda [116] [118]
62Eli Upfal [7]
63S. V. N. Vishwanathan (Vishy Vishwanathan) [123] [139] [140]
64Joel Yellin [138]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)