| Year |
Citation |
Score |
| 2019 |
Nakamura A, Helmbold DP, Warmuth MK. Mistake bounds on the noise-free multi-armed bandit game Information and Computation. 269: 104453. DOI: 10.1016/J.Ic.2019.104453 |
0.302 |
|
| 2016 |
Hazan E, Kale S, Warmuth MK. Learning rotations with little regret Machine Learning. 1-20. DOI: 10.1007/S10994-016-5548-X |
0.569 |
|
| 2014 |
Warmuth MK, Kotłowski W, Zhou S. Kernelization of matrix updates, when and how? Theoretical Computer Science. 558: 159-178. DOI: 10.1016/J.Tcs.2014.09.031 |
0.566 |
|
| 2014 |
Dereziński M, Warmuth MK. The limits of squared Euclidean distance regularization Advances in Neural Information Processing Systems. 4: 2807-2815. |
0.473 |
|
| 2013 |
Nie J, Kotłowski W, Warmuth MK. Online PCA with optimal regrets Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8139: 98-112. DOI: 10.1007/978-3-642-40935-6_8 |
0.431 |
|
| 2013 |
Nie J, Warmuth MK, Vishwanathan SVN, Zhang X. Open Problem: Lower bounds for Boosting with Hadamard Matrices Journal of Machine Learning Research. 30: 1076-1079. |
0.307 |
|
| 2011 |
Warmuth MK, Koolen WM, Helmbold DP. Combining initial segments of lists Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6925: 219-233. DOI: 10.1016/J.Tcs.2013.09.021 |
0.534 |
|
| 2011 |
Kotłowski W, Warmuth MK. Minimax algorithm for learning rotations Journal of Machine Learning Research. 19: 821-823. |
0.384 |
|
| 2011 |
Koolen WM, Kotłowski W, Warmuth MK. Learning eigenvectors for free Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.33 |
|
| 2010 |
Zhou S, Warmuth MK, Dong Y, Ye F. New combination coefficients for AdaBoost algorithms Proceedings - 2010 6th International Conference On Natural Computation, Icnc 2010. 6: 3194-3198. DOI: 10.1109/ICNC.2010.5584334 |
0.453 |
|
| 2010 |
Warmuth MK, Kuzmin D. Bayesian generalized probability calculus for density matrices Machine Learning. 78: 63-101. DOI: 10.1007/S10994-009-5133-7 |
0.641 |
|
| 2010 |
Koolen WM, Warmuth MK, Kivinen J. Hedging structured concepts Colt 2010 - the 23rd Conference On Learning Theory. 93-105. |
0.4 |
|
| 2009 |
Warmuth MK, Glocer K, Rätsch G. Boosting algorithms for maximizing the soft margin Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.533 |
|
| 2008 |
Warmuth MK, Glocer KA, Vishwanathan SVN. Entropy regularized LPBoost Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5254: 256-271. DOI: 10.1007/978-3-540-87987-9_23 |
0.649 |
|
| 2008 |
Warmuth MK, Kuzmin D. Randomized online PCA algorithms with regret bounds that are logarithmic in the dimension Journal of Machine Learning Research. 9: 2287-2320. |
0.752 |
|
| 2007 |
Liao J, Warmuth MK, Govindarajan S, Ness JE, Wang RP, Gustafsson C, Minshull J. Engineering proteinase K using machine learning and synthetic genes. Bmc Biotechnology. 7: 16. PMID 17386103 DOI: 10.1186/1472-6750-7-16 |
0.48 |
|
| 2007 |
Warmuth MK. Winnowing subspaces Acm International Conference Proceeding Series. 227: 999-1006. DOI: 10.1145/1273496.1273622 |
0.469 |
|
| 2007 |
Kuzmin D, Warmuth MK. Online kernel PCA with entropic matrix updates Acm International Conference Proceeding Series. 227: 465-472. DOI: 10.1145/1273496.1273555 |
0.708 |
|
| 2007 |
Helmbold DP, Warmuth MK. Learning permutations with exponential weights Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4539: 469-483. |
0.441 |
|
| 2007 |
Warmuth MK, Kuzmin D. Randomized PCA algorithms with regret bounds that are logarithmic in the dimension Advances in Neural Information Processing Systems. 1481-1488. |
0.705 |
|
| 2007 |
Kuzmin D, Warmuth MK. Unlabeled compression schemes for maximum classes Journal of Machine Learning Research. 8: 2047-2081. |
0.566 |
|
| 2007 |
Helmbold DP, Warmuth MK. Learning permutations with exponential weights Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4539: 469-483. |
0.441 |
|
| 2006 |
Warmuth MK, Liao J, Rätsch G. Totally corrective boosting algorithms that maximize the margin Acm International Conference Proceeding Series. 148: 1001-1008. DOI: 10.1145/1143844.1143970 |
0.485 |
|
| 2006 |
Kivinen J, Warmuth MK, Hassibi B. The p-norm generalization of the LMS algorithm for adaptive filtering Ieee Transactions On Signal Processing. 54: 1782-1793. DOI: 10.1109/Tsp.2006.872551 |
0.551 |
|
| 2006 |
Warmuth MK, Kuzmin D. Online variance minimization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4005: 514-528. DOI: 10.1007/S10994-011-5269-0 |
0.749 |
|
| 2006 |
Warmuth MK, Kuzmin D. A Bayesian probability calculus for density matrices Proceedings of the 22nd Conference On Uncertainty in Artificial Intelligence, Uai 2006. 503-511. |
0.575 |
|
| 2006 |
Warmuth MK. Can entropic regularization be replaced by squared euclidean distance plus additional linear constraints Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4005: 653-664. |
0.367 |
|
| 2006 |
Abernethy J, Langford J, Warmuth MK. Continuous experts and the binning algorithm Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4005: 544-558. |
0.505 |
|
| 2005 |
Warmuth MK, Vishwanathan SVN. Leaving the Span Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3559: 366-381. |
0.417 |
|
| 2005 |
Kuzmin D, Warmuth MK. Optimum follow the leader algorithm Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3559: 684-686. |
0.719 |
|
| 2005 |
Rätsch G, Warmuth MK. Efficient margin maximizing with boosting Journal of Machine Learning Research. 6: 2131-2152. |
0.513 |
|
| 2004 |
Hatano K, Warmuth MK. Boosting versus covering Advances in Neural Information Processing Systems. |
0.432 |
|
| 2004 |
Warmuth MK. The optimal PAC algorithm Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 3120: 641-642. |
0.456 |
|
| 2003 |
Warmuth MK, Liao J, Rätsch G, Mathieson M, Putta S, Lemmen C. Active learning with support vector machines in the drug discovery process. Journal of Chemical Information and Computer Sciences. 43: 667-73. PMID 12653536 DOI: 10.1021/Ci025620T |
0.473 |
|
| 2003 |
Forster J, Warmuth MK. Relative loss bounds for temporal-difference learning Machine Learning. 51: 23-50. DOI: 10.1023/A:1021825927902 |
0.458 |
|
| 2003 |
Kivinen J, Warmuth MK, Hassibi B. The P-Norn Generalization of the LMS Algorithm for Adaptive Filtering Ifac Proceedings Volumes. 36: 1717-1722. DOI: 10.1016/S1474-6670(17)35008-5 |
0.549 |
|
| 2002 |
Takimoto E, Warmuth MK. Path kernels and multiplicative updates Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2375: 74-89. DOI: 10.1162/1532443041424328 |
0.418 |
|
| 2002 |
Helmbold DP, Panizza S, Warmuth MK. Direct and indirect algorithms for on-line learning of disjunctions Theoretical Computer Science. 284: 109-142. DOI: 10.1016/S0304-3975(01)00081-0 |
0.568 |
|
| 2002 |
Forster J, Warmuth MK. Relative expected instantaneous loss bounds Journal of Computer and System Sciences. 64: 76-102. DOI: 10.1006/Jcss.2001.1798 |
0.53 |
|
| 2002 |
Rätsch G, Warmuth MK. Maximizing the margin with boosting Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2375: 334-350. |
0.45 |
|
| 2001 |
Herbster M, Warmuth MK. Tracking the Best Linear Predictor Journal of Machine Learning Research. 1: 281-309. DOI: 10.1162/153244301753683726 |
0.511 |
|
| 2001 |
Azoury KS, Warmuth MK. Relative loss bounds for on-line density estimation with the exponential family of distributions Machine Learning. 43: 211-246. DOI: 10.1023/A:1010896012157 |
0.45 |
|
| 2000 |
Bousquet O, Warmuth MK. Tracking a small set of experts by mixing past posteriors Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2111: 31-47. DOI: 10.1162/153244303321897654 |
0.423 |
|
| 2000 |
Takimoto E, Warmuth MK. The last-step minimax algorithm Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1968: 279-290. |
0.4 |
|
| 1999 |
Helmbold DP, Kivinen J, Warmuth MK. Relative loss bounds for single neurons. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 10: 1291-304. PMID 18252631 DOI: 10.1109/72.809075 |
0.515 |
|
| 1999 |
Takimoto E, Warmuth MK. Predicting nearly as well as the best pruning of a planar decision graph Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1720: 335-346. DOI: 10.1016/S0304-3975(01)00401-7 |
0.35 |
|
| 1999 |
Kivinen J, Warmuth MK. Averaging expert predictions Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1572: 153-167. |
0.411 |
|
| 1998 |
Helmbold DP, Schapire RE, Singer Y, Warmuth MK. On-line portfolio selection using multiplicative updates Mathematical Finance. 8: 325-347. DOI: 10.1111/1467-9965.00058 |
0.536 |
|
| 1998 |
Haussler D, Kivinen J, Warmuth MK. Sequential prediction of individual sequences under general loss functions Ieee Transactions On Information Theory. 44: 1906-1925. DOI: 10.1109/18.705569 |
0.319 |
|
| 1998 |
Kivinen J, Warmuth MK. Relative loss bounds for multidimensional regression problems Advances in Neural Information Processing Systems. 287-293. DOI: 10.1023/A:1017938623079 |
0.346 |
|
| 1998 |
Auer P, Warmuth MK. Tracking the Best Disjunction Machine Learning. 32: 127-150. DOI: 10.1023/A:1007472513967 |
0.578 |
|
| 1998 |
Herbster M, Warmuth MK. Tracking the Best Expert Machine Learning. 32: 151-178. DOI: 10.1023/A:1007424614876 |
0.493 |
|
| 1998 |
Maass W, Warmuth MK. Efficient Learning with Virtual Threshold Gates Information and Computation. 141: 66-83. DOI: 10.1006/Inco.1997.2686 |
0.554 |
|
| 1997 |
Cesa-Bianchi N, Freund Y, Haussler D, Helmbold DP, Schapire RE, Warmuth MK. How to use expert advice Journal of the Acm. 44: 427-485. DOI: 10.1145/258128.258179 |
0.537 |
|
| 1997 |
Kivinen J, Warmuth MK, Auer P. The Perceptron algorithm versus Winnow: Linear versus logarithmic mistake bounds when few input variables are relevant Artificial Intelligence. 97: 325-343. DOI: 10.1016/S0004-3702(97)00039-8 |
0.576 |
|
| 1997 |
Kivinen J, Warmuth MK. Exponentiated Gradient versus Gradient Descent for Linear Predictors Information and Computation. 132: 1-63. DOI: 10.1006/Inco.1996.2612 |
0.602 |
|
| 1997 |
Helmbold DP, Schapire RE, Singer Y, Warmuth MK. A Comparison of New and Old Algorithms for a Mixture Estimation Problem Machine Learning. 27: 97-119. |
0.386 |
|
| 1997 |
Singer Y, Warmuth MK. Training algorithms for hidden Markov models using entropy based distance functions Advances in Neural Information Processing Systems. 641-647. |
0.33 |
|
| 1996 |
Cesa-Bianchi N, Long PM, Warmuth MK. Worst-case quadratic loss bounds for prediction using linear functions and gradient descent. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 7: 604-19. PMID 18263458 DOI: 10.1109/72.501719 |
0.471 |
|
| 1995 |
Goldman SA, Warmuth MK. Learning Binary Relations Using Weighted Majority Voting Machine Learning. 20: 245-271. DOI: 10.1023/A:1022675327341 |
0.596 |
|
| 1995 |
Littlestone N, Warmuth MK, Long PM. On-line learning of linear functions Computational Complexity. 5: 1-23. DOI: 10.1007/Bf01277953 |
0.508 |
|
| 1995 |
Haussler D, Kivinen J, Warmuth MK. Tight worst-case loss bounds for predicting with expert advice Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 904: 69-83. |
0.391 |
|
| 1994 |
Cesa-Bianchi N, Krogh A, Warmuth MK. Bounds on Approximate Steepest Descent for Likelihood Maximization in Exponential Families Ieee Transactions On Information Theory. 40: 1215-1218. DOI: 10.1109/18.335953 |
0.34 |
|
| 1994 |
Haussler D, Littlestone N, Warmuth MK. Predicting {0, 1}-Functions on Randomly Drawn Points Information and Computation. 115: 248-292. DOI: 10.1006/Inco.1994.1097 |
0.382 |
|
| 1994 |
Long PM, Warmuth MK. Composite Geometric Concepts and Polynomial Predictability Information and Computation. 113: 230-252. DOI: 10.1006/Inco.1994.1071 |
0.35 |
|
| 1994 |
Littlestone N, Warmuth MK. The Weighted Majority Algorithm Information and Computation. 108: 212-261. DOI: 10.1006/Inco.1994.1009 |
0.577 |
|
| 1994 |
Bodlaender HL, Moran S, Warmuth MK. The Distributed Bit Complexity of the Ring: From the Anonymous to the Non-anonymous Case Information and Computation. 108: 34-50. DOI: 10.1006/Inco.1994.1002 |
0.33 |
|
| 1993 |
Pitt L, Warmuth MK. The Minimum Consistent DFA Problem Cannot be Approximated Within any Polynomial Journal of the Acm (Jacm). 40: 95-142. DOI: 10.1145/138027.138042 |
0.47 |
|
| 1993 |
Moran S, Warmuth MK. Gap theorems for distributed computation Siam Journal On Computing. 22: 379-394. DOI: 10.1137/0222028 |
0.319 |
|
| 1992 |
Helmbold D, Sloan R, Warmuth MK. Learning Integer Lattices Siam Journal On Computing. 21: 240-266. DOI: 10.1137/0221019 |
0.442 |
|
| 1991 |
Haussler D, Kearns M, Littlestone N, Warmuth MK. Equivalence of models for polynomial learnability Information and Computation. 95: 129-161. DOI: 10.1016/0890-5401(91)90042-Z |
0.303 |
|
| 1990 |
Helmbold D, Sloan R, Warmuth MK. Learning Nested Differences of Intersection-Closed Concept Classes Machine Learning. 5: 165-196. DOI: 10.1023/A:1022696716689 |
0.51 |
|
| 1990 |
Ratner D, Warmuth M. The (n 2 -1)-puzzle and related relocation problems Journal of Symbolic Computation. 10: 111-137. DOI: 10.1016/S0747-7171(08)80001-6 |
0.401 |
|
| 1990 |
Pitt L, Warmuth MK. Prediction-preserving reducibility Journal of Computer and System Sciences. 41: 430-467. DOI: 10.1016/0022-0000(90)90028-J |
0.35 |
|
| 1989 |
Blumer A, Ehrenfeucht A, Haussler D, Warmuth MK. Learnability and the Vapnik-Chervonenkis dimension Journal of the Acm. 36: 929-965. DOI: 10.1145/76359.76371 |
0.403 |
|
| 1989 |
Simons BB, Warmuth MK. A Fast Algorithm for Multiprocessor Scheduling of Unit-Length Jobs Siam Journal On Computing. 18: 690-710. DOI: 10.1137/0218048 |
0.392 |
|
| 1989 |
Anderson RJ, Mayr EW, Warmuth MK. Parallel approximation algorithms for bin packing Information and Computation. 82: 262-277. DOI: 10.1016/0890-5401(89)90003-5 |
0.587 |
|
| 1988 |
Attiya H, Snir M, Warmuth MK. Computing on an Anonymous Ring Journal of the Acm (Jacm). 35: 845-875. DOI: 10.1145/48014.48247 |
0.318 |
|
| 1987 |
Blumer A, Ehrenfeucht A, Haussler D, Warmuth MK. Occam's Razor Information Processing Letters. 24: 377-380. DOI: 10.1016/0020-0190(87)90114-1 |
0.486 |
|
| 1986 |
Dolev D, Upfal E, Warmuth MK. The parallel complexity of scheduling with precedence constraints Journal of Parallel and Distributed Computing. 3: 553-576. DOI: 10.1016/0743-7315(86)90014-6 |
0.432 |
|
| 1986 |
Gonczarowski J, Warmuth MK. Manipulating derivation forests by scheduling techniques Theoretical Computer Science. 45: 87-119. DOI: 10.1016/0304-3975(86)90042-3 |
0.362 |
|
| 1985 |
Dolev D, Warmuth MK. Profile Scheduling of Opposing Forests and Level Orders Siam Journal On Algebraic and Discrete Methods. 6: 665-687. DOI: 10.1137/0606066 |
0.47 |
|
| 1985 |
Dolev D, Warmuth MK. Scheduling Flat Graphs Siam Journal On Computing. 14: 638-657. DOI: 10.1137/0214047 |
0.461 |
|
| 1985 |
Gonczarowski J, Warmuth MK. Applications of scheduling theory to formal language theory Theoretical Computer Science. 37: 217-243. DOI: 10.1016/0304-3975(85)90092-1 |
0.371 |
|
| 1984 |
Dolev D, Warmuth MK. Scheduling precedence graphs of bounded height Journal of Algorithms. 5: 48-59. DOI: 10.1016/0196-6774(84)90039-7 |
0.371 |
|
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