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Note: Links lead to the DBLP on the Web. Avrim Blum Maria-Florina Balcan , Avrim Blum: A PAC-Style Model for Learning from Labeled and Unlabeled Data. COLT 2005 : 111-126 Avrim Blum, Yishay Mansour : From External to Internal Regret. COLT 2005 : 621-636 Maria-Florina Balcan , Avrim Blum: Mechanism Design via Machine Learning. FOCS 2005 : 605-614 Shobha Venkataraman , Dawn Xiaodong Song , Phillip B. Gibbons , Avrim Blum: New Streaming Algorithms for Fast Detection of Superspreaders. NDSS 2005 Avrim Blum, Cynthia Dwork , Frank McSherry , Kobbi Nissim : Practical privacy: the SuLQ framework. PODS 2005 : 128-138 Avrim Blum: Random Projection, Margins, Kernels, and Feature-Selection. SLSFS 2005 : 52-68 Avrim Blum, Jason D. Hartline : Near-optimal online auctions. SODA 2005 : 1156-1163 Maria-Florina Balcan , Avrim Blum, Santosh Vempala : On Kernels, Margins, and Low-Dimensional Mappings. ALT 2004 : 194-205 H. Brendan McMahan , Avrim Blum: Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary. COLT 2004 : 109-123 Avrim Blum, John D. Lafferty , Mugizi Robert Rwebangira , Rajashekar Reddy : Semi-supervised learning using randomized mincuts. ICML 2004 Maria-Florina Balcan , Avrim Blum, Ke Yang : Co-Training and Expansion: Towards Bridging Theory and Practice. NIPS 2004 Avrim Blum, Dawn Xiaodong Song , Shobha Venkataraman : Detection of Interactive Stepping Stones: Algorithms and Confidence Bounds. RAID 2004 : 258-277 Nikhil Bansal , Avrim Blum, Shuchi Chawla , Adam Meyerson : Approximation algorithms for deadline-TSP and vehicle routing with time-windows. STOC 2004 : 166-174 Avrim Blum, Jeffrey C. Jackson , Tuomas Sandholm , Martin Zinkevich : Preference Elicitation and Query Learning. Journal of Machine Learning Research 5 : 649-667 (2004) Nikhil Bansal , Avrim Blum, Shuchi Chawla : Correlation Clustering. Machine Learning 56 (1-3): 89-113 (2004) Avrim Blum, Vijay Kumar , Atri Rudra , Felix Wu : Online learning in online auctions. Theor. Comput. Sci. 324 (2-3): 137-146 (2004) Martin Zinkevich , Avrim Blum, Tuomas Sandholm : On polynomial-time preference elicitation with value queries. ACM Conference on Electronic Commerce 2003 : 176-185 Avrim Blum, Jeffrey C. Jackson , Tuomas Sandholm , Martin Zinkevich : Preference Elicitation and Query Learning. COLT 2003 : 13-25 Avrim Blum, John Langford : PAC-MDL Bounds. COLT 2003 : 344-357 Avrim Blum: Learning a Function of r Relevant Variables. COLT 2003 : 731-733 Nikhil Bansal , Avrim Blum, Shuchi Chawla , Kedar Dhamdhere : Scheduling for Flow-Time with Admission Control. ESA 2003 : 43-54 Avrim Blum: Machine Learning: My Favorite Results, Directions, and Open Problems. FOCS 2003 : 2- Avrim Blum, Shuchi Chawla , David R. Karger , Terran Lane , Adam Meyerson , Maria Minkoff : Approximation Algorithms for Orienteering and Discounted-Reward TSP. FOCS 2003 : 46-55 H. Brendan McMahan , Geoffrey J. Gordon , Avrim Blum: Planning in the Presence of Cost Functions Controlled by an Adversary. ICML 2003 : 536-543 Ke Yang , Avrim Blum: On Statistical Query Sampling and NMR Quantum Computing. IEEE Conference on Computational Complexity 2003 : 194- Avrim Blum, Vijay Kumar , Atri Rudra , Felix Wu : Online learning in online auctions. SODA 2003 : 202-204 Yossi Azar , Avrim Blum, Yishay Mansour : Combining online algorithms for rejection and acceptance. SPAA 2003 : 159-163 Nikhil Bansal , Avrim Blum, Shuchi Chawla , Adam Meyerson : Online oblivious routing. SPAA 2003 : 44-49 Avrim Blum, Shuchi Chawla , Adam Kalai : Static Optimality and Dynamic Search-Optimality in Lists and Trees. Algorithmica 36 (3): 249-260 (2003) Avrim Blum, Ke Yang : On Statistical Query Sampling and NMR Quantum Computing Electronic Colloquium on Computational Complexity (ECCC) 10 (014): (2003) Avrim Blum, Adam Kalai , Hal Wasserman : Noise-tolerant learning, the parity problem, and the statistical query model. J. ACM 50 (4): 506-519 (2003) John Langford , Avrim Blum: Microchoice Bounds and Self Bounding Learning Algorithms. Machine Learning 51 (2): 165-179 (2003) Nikhil Bansal , Avrim Blum, Shuchi Chawla : Correlation Clustering. FOCS 2002 : 238- Avrim Blum, Shuchi Chawla , Adam Kalai : Static optimality and dynamic search-optimality in lists and trees. SODA 2002 : 1-8 Avrim Blum, John Dunagan : Smoothed analysis of the perceptron algorithm for linear programming. SODA 2002 : 905-914 Avrim Blum, Tuomas Sandholm , Martin Zinkevich : Online algorithms for market clearing. SODA 2002 : 971-980 Avrim Blum, Shuchi Chawla : Learning from Labeled and Unlabeled Data using Graph Mincuts. ICML 2001 : 19-26 Avrim Blum, Adam Kalai , Jon M. Kleinberg : Admission Control to Minimize Rejections. WADS 2001 : 155-164 Joseph O'Sullivan , John Langford , Rich Caruana , Avrim Blum: FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML 2000 : 703-710 Avrim Blum, Adam Kalai , Hal Wasserman : Noise-tolerant learning, the parity problem, and the statistical query model. STOC 2000 : 435-440 Avrim Blum, Adam Kalai , Hal Wasserman : Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model CoRR cs.LG/0010022 : (2000) Avrim Blum, Carl Burch : On-line Learning and the Metrical Task System Problem. Machine Learning 39 (1): 35-58 (2000) Avrim Blum, Prasad Chalasani : An Online Algorithm for Improving Performance in Navigation. SIAM J. Comput. 29 (6): 1907-1938 (2000) Avrim Blum, Howard J. Karloff , Yuval Rabani , Michael E. Saks : A Decomposition Theorem for Task Systems and Bounds for Randomized Server Problems. SIAM J. Comput. 30 (5): 1624-1661 (2000) Avrim Blum, Goran Konjevod , R. Ravi , Santosh Vempala : Semi-definite relaxations for minimum bandwidth and other vertex-ordering problems. Theor. Comput. Sci. 235 (1): 25-42 (2000) Avrim Blum, Adam Kalai , John Langford : Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT 1999 : 203-208 John Langford , Avrim Blum: Microchoice Bounds and Self Bounding Learning Algorithms. COLT 1999 : 209-214 Avrim Blum, John Langford : Probabilistic Planning in the Graphplan Framework. ECP 1999 : 319-332 Avrim Blum, Carl Burch , Adam Kalai : Finely-Competitive Paging. FOCS 1999 : 450-458 Avrim Blum, R. Ravi , Santosh Vempala : A Constant-Factor Approximation Algorithm for the k -MST Problem. J. Comput. Syst. Sci. 58 (1): 101-108 (1999) Avrim Blum, Adam Kalai : Universal Portfolios With and Without Transaction Costs. Machine Learning 35 (3): 193-205 (1999) Avrim Blum, Tom M. Mitchell : Combining Labeled and Unlabeled Sata with Co-Training. COLT 1998 : 92-100 Avrim Blum, Carl Burch , John Langford : On Learning Monotone Boolean Functions. FOCS 1998 : 408-415 Avrim Blum, Goran Konjevod , R. Ravi , Santosh Vempala : Semi-Definite Relaxations for Minimum Bandwidth and other Vertex-Ordering Problems. STOC 1998 : 100-105 Avrim Blum, Alan M. Frieze , Ravi Kannan , Santosh Vempala : A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions. Algorithmica 22 (1/2): 35-52 (1998) Avrim Blum, Prasad Chalasani , Sally A. Goldman , Donna K. Slonim : Learning with Unreliable Boundary Queries. J. Comput. Syst. Sci. 56 (2): 209-222 (1998) Avrim Blum, Adam Kalai : A Note on Learning from Multiple-Instance Examples. Machine Learning 30 (1): 23-29 (1998) Howard Aizenstein , Avrim Blum, Roni Khardon , Eyal Kushilevitz , Leonard Pitt , Dan Roth : On Learning Read-k-Satisfy-j DNF. SIAM J. Comput. 27 (6): 1515-1530 (1998) Baruch Awerbuch , Yossi Azar , Avrim Blum, Santosh Vempala : New Approximation Guarantees for Minimum-Weight k-Trees and Prize-Collecting Salesmen. SIAM J. Comput. 28 (1): 254-262 (1998) Joseph S. B. Mitchell , Avrim Blum, Prasad Chalasani , Santosh Vempala : A Constant-Factor Approximation Algorithm for the Geometric k-MST Problem in the Plane. SIAM J. Comput. 28 (3): 771-781 (1998) Avrim Blum, Adam Kalai : Universal Portfolios With and Without Transaction Costs. COLT 1997 : 309-313 Avrim Blum, Carl Burch : On-line Learning and the Metrical Task System Problem. COLT 1997 : 45-53 Yair Bartal , Avrim Blum, Carl Burch , Andrew Tomkins : A polylog( n )-Competitive Algorithm for Metrical Task Systems. STOC 1997 : 711-719 Avrim Blum, Merrick L. Furst : Fast Planning Through Planning Graph Analysis. Artif. Intell. 90 (1-2): 281-300 (1997) Avrim Blum, Pat Langley : Selection of Relevant Features and Examples in Machine Learning. Artif. Intell. 97 (1-2): 245-271 (1997) Avrim Blum, David R. Karger : An Õ(n^{3/14})-Coloring Algorithm for 3-Colorable Graphs. Inf. Process. Lett. 61 (1): 49-53 (1997) Avrim Blum, Ravindran Kannan : Learning an Intersection of a Constant Number of Halfspaces over a Uniform Distribution. J. Comput. Syst. Sci. 54 (2): 371-380 (1997) Avrim Blum: Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain. Machine Learning 26 (1): 5-23 (1997) Avrim Blum, Prabhakar Raghavan , Baruch Schieber : Navigating in Unfamiliar Geometric Terrain. SIAM J. Comput. 26 (1): 110-137 (1997) Avrim Blum, Alan M. Frieze , Ravi Kannan , Santosh Vempala : A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions. FOCS 1996 : 330-338 Avrim Blum: On-line Algorithms in Machine Learning. Online Algorithms 1996 : 306-325 Piotr Berman , Avrim Blum, Amos Fiat , Howard J. Karloff , Adi Rosén , Michael E. Saks : Randomized Robot Navigation Algorithms. SODA 1996 : 75-84 Avrim Blum, R. Ravi , Santosh Vempala : A Constant-factor Approximation Algorithm for the k MST Problem (Extended Abstract). STOC 1996 : 442-448 Avrim Blum, Prasad Chalasani , Sally A. Goldman , Donna K. Slonim : Learning with Unreliable Boundary Queries. COLT 1995 : 98-107 Avrim Blum: Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain. ICML 1995 : 64-72 Avrim Blum, Merrick L. Furst : Fast Planning Through Planning Graph Analysis. IJCAI 1995 : 1636-1642 Baruch Awerbuch , Yossi Azar , Avrim Blum, Santosh Vempala : Improved approximation guarantees for minimum-weight k -trees and prize-collecting salesmen. STOC 1995 : 277-283 Avrim Blum, Prasad Chalasani , Santosh Vempala : A constant-factor approximation for the k -MST problem in the plane. STOC 1995 : 294-302 Avrim Blum, Joel Spencer : Coloring Random and Semi-Random k-Colorable Graphs. J. Algorithms 19 (2): 204-234 (1995) Avrim Blum, Lisa Hellerstein , Nick Littlestone : Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes. J. Comput. Syst. Sci. 50 (1): 32-40 (1995) Avrim Blum, Steven Rudich : Fast Learning of k-Term DNF Formulas with Queries. J. Comput. Syst. Sci. 51 (3): 367-373 (1995) Avrim Blum, Roni Khardon , Eyal Kushilevitz , Leonard Pitt , Dan Roth : On Learning Read- k -Satisfy- j DNF. COLT 1994 : 110-117 Avrim Blum, Prasad Chalasani , Don Coppersmith , William R. Pulleyblank , Prabhakar Raghavan , Madhu Sudan : The minimum latency problem. STOC 1994 : 163-171 Avrim Blum, Merrick L. Furst , Jeffrey C. Jackson , Michael J. Kearns , Yishay Mansour , Steven Rudich : Weakly learning DNF and characterizing statistical query learning using Fourier analysis. STOC 1994 : 253-262 Avrim Blum: New Approximation Algorithms for Graph Coloring. J. ACM 41 (3): 470-516 (1994) Avrim Blum, Ming Li , John Tromp , Mihalis Yannakakis : Linear Approximation of Shortest Superstrings. J. ACM 41 (4): 630-647 (1994) Avrim Blum: Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain. SIAM J. Comput. 23 (5): 990-1000 (1994) Avrim Blum, Prasad Chalasani , Jeffrey Jackson : On Learning Embedded Symmetric Concepts. COLT 1993 : 337-346 Avrim Blum, Merrick L. Furst , Michael J. Kearns , Richard J. Lipton : Cryptographic Primitives Based on Hard Learning Problems. CRYPTO 1993 : 278-291 Avrim Blum, Prasad Chalasani : An On-Line Algorithm for Improving Performance in Navigation FOCS 1993 : 2-11 Avrim Blum, Ravi Kannan : Learning an Intersection of k Halfspaces over a Uniform Distribution FOCS 1993 : 312-320 Avrim Blum, Ronald L. Rivest : Training a 3-Node Neural Network is NP-Complete. Machine Learning: From Theory to Applications 1993 : 9-28 Avrim Blum, Prasad Chalasani : Learning Switching Concepts. COLT 1992 : 231-242 Avrim Blum, Howard J. Karloff , Yuval Rabani , Michael E. Saks : A Decomposition Theorem and Bounds for Randomized Server Problems FOCS 1992 : 197-207 Avrim Blum, Steven Rudich : Fast Learning of k-Term DNF Formulas with Queries STOC 1992 : 382-389 Avrim Blum: Rank-r Decision Trees are a Subclass of r-Decision Lists. Inf. Process. Lett. 42 (4): 183-185 (1992) Avrim Blum: Learning Boolean Functions in an Infinite Attribute Space. Machine Learning 9 : 373-386 (1992) Avrim Blum, Ronald L. Rivest : Training a 3-node neural network is NP-complete. Neural Networks 5 (1): 117-127 (1992) Avrim Blum, Lisa Hellerstein , Nick Littlestone : Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes. COLT 1991 : 157-166 Avrim Blum, Tao Jiang , Ming Li , John Tromp , Mihalis Yannakakis : Linear Approximation of Shortest Superstrings STOC 1991 : 328-336 Avrim Blum, Prabhakar Raghavan , Baruch Schieber : Navigating in Unfamiliar Geometric Terrain (Preliminary Version) STOC 1991 : 494-504 Avrim Blum, Mona Singh : Learning Functions of k Terms. COLT 1990 : 144-153 Avrim Blum: Separating PAC and Mistake-Bound Learning Models Over the Boolean Domain (Abstract). COLT 1990 : 393 Avrim Blum: Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain FOCS 1990 : 211-218 Avrim Blum: Some Tools for Approximate 3-Coloring (Extended Abstract) FOCS 1990 : 554-562 Avrim Blum: Learning Boolean Functions in an Infinite Atribute Space (Extended Abstract) STOC 1990 : 64-72 Avrim Blum: An \tildeO(n^0.4)-Approximation Algorithm for 3-Coloring (and Improved Approximation Algorithm for k-Coloring) STOC 1989 : 535-542 Avrim Blum, Ronald L. Rivest : Training a 3-Node Neural Network is NP-Complete. COLT 1988 : 9-18 Avrim Blum, Ronald L. Rivest : Training a 3-Node Neural Network is NP-Complete. NIPS 1988 : 494-501 1 [ 52 ] 2 [ 33 ] [ 51 ] 3 [ 33 ] [ 51 ] [ 83 ] 4 [ 99 ] [ 102 ] [ 107 ] [ 109 ] 5 [ 77 ] [ 82 ] [ 89 ] [ 95 ] [ 97 ] 6 [ 47 ] 7 [ 38 ] 8 [ 47 ] [ 48 ] [ 57 ] [ 61 ] [ 68 ] 9 [ 71 ] 10 [ 17 ] [ 20 ] [ 22 ] [ 27 ] [ 32 ] [ 36 ] [ 50 ] [ 54 ] [ 67 ] 11 [ 73 ] [ 76 ] [ 77 ] [ 81 ] [ 82 ] [ 87 ] [ 89 ] [ 95 ] [ 97 ] 12 [ 27 ] 13 [ 89 ] 14 [ 75 ] 15 [ 105 ] 16 [ 38 ] 17 [ 40 ] [ 55 ] 18 [ 21 ] [ 26 ] [ 34 ] [ 46 ] 19 [ 106 ] 20 [ 36 ] [ 54 ] 21 [ 86 ] 22 [ 103 ] 23 [ 11 ] [ 30 ] 24 [ 22 ] 25 [ 26 ] [ 92 ] [ 96 ] 26 [ 10 ] 27 [ 49 ] [ 53 ] [ 59 ] [ 61 ] [ 64 ] [ 69 ] [ 70 ] [ 72 ] [ 76 ] [ 79 ] [ 81 ] 28 [ 19 ] [ 40 ] [ 43 ] [ 55 ] 29 [ 44 ] [ 87 ] 30 [ 16 ] [ 38 ] [ 66 ] 31 [ 21 ] [ 26 ] 32 [ 28 ] [ 52 ] 33 [ 72 ] 34 [ 56 ] [ 65 ] 35 [ 84 ] [ 94 ] 36 [ 28 ] [ 52 ] 37 [ 100 ] 38 [ 87 ] 39 [ 57 ] [ 62 ] [ 63 ] [ 64 ] [ 71 ] [ 78 ] [ 91 ] 40 [ 45 ] 41 [ 10 ] [ 24 ] 42 [ 21 ] 43 [ 11 ] [ 30 ] 44 [ 26 ] [ 83 ] [ 108 ] 45 [ 86 ] [ 101 ] 46 [ 105 ] 47 [ 82 ] [ 87 ] [ 97 ] 48 [ 87 ] 49 [ 50 ] 50 [ 58 ] 51 [ 105 ] 52 [ 71 ] 53 [ 28 ] [ 52 ] 54 [ 27 ] 55 [ 16 ] [ 66 ] 56 [ 9 ] [ 27 ] [ 41 ] 57 [ 37 ] [ 56 ] [ 60 ] [ 65 ] 58 [ 100 ] 59 [ 1 ] [ 2 ] [ 12 ] [ 18 ] 60 [ 38 ] 61 [ 28 ] [ 52 ] 62 [ 15 ] [ 26 ] [ 29 ] 63 [ 84 ] [ 94 ] 64 [ 100 ] 65 [ 16 ] [ 38 ] [ 66 ] 66 [ 74 ] [ 92 ] [ 93 ] [ 96 ] 67 [ 9 ] [ 41 ] 68 [ 8 ] 69 [ 36 ] [ 54 ] 70 [ 98 ] [ 106 ] 71 [ 31 ] 72 [ 27 ] 73 [ 47 ] 74 [ 10 ] [ 24 ] 75 [ 32 ] [ 33 ] [ 37 ] [ 40 ] [ 50 ] [ 51 ] [ 55 ] [ 56 ] [ 60 ] [ 65 ] [ 102 ] 76 [ 98 ] [ 106 ] 77 [ 69 ] [ 70 ] [ 79 ] 78 [ 84 ] [ 94 ] 79 [ 80 ] [ 85 ] [ 99 ] 80 [ 10 ] [ 24 ] 81 [ 74 ] [ 92 ] [ 93 ] [ 96 ]  ©2006 Association for Computing Machinery |
