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ORCIDAndrew W. Moore 0001
Person information
- affiliation: Carnegie Mellon University, PA, USA
- affiliation (former): Google Pittsburgh, USA
Other persons with the same name
- Andrew W. Moore — disambiguation page
- Andrew W. Moore 0002
![[0000-0002-5494-9305]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-5494-9305")
(aka: Andrew William Moore) — University of Cambridge, UK
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2010 – 2019
- 2016
[j18]Andrew W. Moore, Tim O'Reilly, Paul D. Nielsen, Kevin Fall:
Four Thought Leaders on Where the Industry Is Headed. IEEE Softw. 33(1): 36-39 (2016)- 2013
- [i8]Scott Davies, Andrew W. Moore:
Interpolating Conditional Density Trees. CoRR abs/1301.0563 (2013) - [i7]Andrew W. Moore, Jeff G. Schneider:
Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes. CoRR abs/1301.0589 (2013) - [i6]Scott Davies, Andrew W. Moore:
Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed Continuous And Discrete Variables. CoRR abs/1301.3852 (2013) - [i5]Andrew W. Moore:
The Anchors Hierachy: Using the triangle inequality to survive high dimensional data. CoRR abs/1301.3877 (2013) - 2012
- [i4]Purnamrita Sarkar, Andrew W. Moore:
A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs. CoRR abs/1206.5259 (2012) - 2011
- [c85]Purnamrita Sarkar, Deepayan Chakrabarti, Andrew W. Moore:
Theoretical Justification of Popular Link Prediction Heuristics. IJCAI 2011: 2722-2727 - [p1]Purnamrita Sarkar, Andrew W. Moore:
Random Walks in Social Networks and their Applications: A Survey. Social Network Data Analytics 2011: 43-77 - [i3]Dongryeol Lee, Alexander G. Gray, Andrew W. Moore:
Dual-Tree Fast Gauss Transforms. CoRR abs/1102.2878 (2011) - 2010
- [j17]Artur Dubrawski, John Östlund, Lujie Chen, Andrew W. Moore:
Computationally efficient scoring of activity using demographics and connectivity of entities. Inf. Technol. Manag. 11(2): 77-89 (2010) - [c84]Purnamrita Sarkar, Deepayan Chakrabarti, Andrew W. Moore:
Theoretical Justification of Popular Link Prediction Heuristics. COLT 2010: 295-307 - [c83]Purnamrita Sarkar, Andrew W. Moore:
Fast nearest-neighbor search in disk-resident graphs. KDD 2010: 513-522
2000 – 2009
- 2009
- [c82]Purnamrita Sarkar, Andrew W. Moore:
Fast dynamic reranking in large graphs. WWW 2009: 31-40 - 2008
- [c81]Purnamrita Sarkar, Andrew W. Moore, Amit Prakash:
Fast incremental proximity search in large graphs. ICML 2008: 896-903 - 2007
- [c80]Purnamrita Sarkar, Andrew W. Moore:
A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs. UAI 2007: 335-343 - [c79]Sajid M. Siddiqi, Geoffrey J. Gordon, Andrew W. Moore:
Fast State Discovery for HMM Model Selection and Learning. AISTATS 2007: 492-499 - 2006
- [j16]Ting Liu, Andrew W. Moore, Alexander G. Gray:
New Algorithms for Efficient High-Dimensional Nonparametric Classification. J. Mach. Learn. Res. 7: 1135-1158 (2006) - [j15]Dan Pelleg, Andrew W. Moore:
Dependency trees in sub-linear time and bounded memory. VLDB J. 15(3): 250-262 (2006) - [c78]Artur Dubrawski, Kimberly Elenberg, Andrew W. Moore, Maheshkumar Sabhnani:
Monitoring Food Safety by Detecting Patterns in Consumer Complaints. AAAI 2006: 1782-1788 - [c77]Josep Roure, Andrew W. Moore:
Sequential update of ADtrees. ICML 2006: 769-776 - [c76]Andrew W. Moore:
New cached-sufficient statistics algorithms for quickly answering statistical questions. KDD 2006: 2 - [c75]Khalid El-Arini, Andrew W. Moore, Ting Liu:
Autonomous Visualization. PKDD 2006: 495-502 - [e1]William W. Cohen, Andrew W. Moore:
Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006. ACM International Conference Proceeding Series 148, ACM 2006, ISBN 1-59593-383-2 [contents] - 2005
- [j14]David L. Buckeridge, Howard S. Burkom, Murray Campbell, William R. Hogan, Andrew W. Moore:
Algorithms for rapid outbreak detection: a research synthesis. J. Biomed. Informatics 38(2): 99-113 (2005) - [j13]Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner:
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks. J. Mach. Learn. Res. 6: 1961-1998 (2005) - [j12]Purnamrita Sarkar, Andrew W. Moore:
Dynamic social network analysis using latent space models. SIGKDD Explor. 7(2): 31-40 (2005) - [c74]Paul Komarek, Andrew W. Moore:
Making Logistic Regression a Core Data Mining Tool with TR-IRLS. ICDM 2005: 685-688 - [c73]Sajid M. Siddiqi, Andrew W. Moore:
Fast inference and learning in large-state-space HMMs. ICML 2005: 800-807 - [c72]Anna Goldenberg, Andrew W. Moore:
Bayes net graphs to understand co-authorship networks? LinkKDD 2005: 1-8 - [c71]Jeremy Kubica, Andrew W. Moore, Andrew J. Connolly, Robert Jedicke:
A multiple tree algorithm for the efficient association of asteroid observations. KDD 2005: 138-146 - [c70]Daniel B. Neill, Andrew W. Moore, Maheshkumar Sabhnani, Kenny Daniel:
Detection of emerging space-time clusters. KDD 2005: 218-227 - [c69]Brigham S. Anderson, Andrew W. Moore:
Fast Information Value for Graphical Models. NIPS 2005: 51-58 - [c68]Jeremy Kubica, Joseph Masiero, Andrew W. Moore, Robert Jedicke, Andrew J. Connolly:
Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery. NIPS 2005: 691-698 - [c67]Dongryeol Lee, Alexander G. Gray, Andrew W. Moore:
Dual-Tree Fast Gauss Transforms. NIPS 2005: 747-754 - [c66]Daniel B. Neill, Andrew W. Moore, Gregory F. Cooper:
A Bayesian Spatial Scan Statistic. NIPS 2005: 1003-1010 - [c65]Purnamrita Sarkar, Andrew W. Moore:
Dynamic Social Network Analysis using Latent Space Models. NIPS 2005: 1145-1152 - 2004
- [c64]Anna Goldenberg, Andrew W. Moore:
Tractable learning of large Bayes net structures from sparse data. ICML 2004 - [c63]Brigham S. Anderson, Andrew W. Moore, Andrew J. Connolly, Robert Nichol:
Fast nonlinear regression via eigenimages applied to galactic morphology. KDD 2004: 40-48 - [c62]Daniel B. Neill, Andrew W. Moore:
Rapid detection of significant spatial clusters. KDD 2004: 256-265 - [c61]Kaustav Das, Andrew W. Moore, Jeff G. Schneider:
Belief state approaches to signaling alarms in surveillance systems. KDD 2004: 539-544 - [c60]Ting Liu, Ke Yang, Andrew W. Moore:
The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data. KDD 2004: 629-634 - [c59]Ting Liu, Andrew W. Moore, Alexander G. Gray, Ke Yang:
An Investigation of Practical Approximate Nearest Neighbor Algorithms. NIPS 2004: 825-832 - [c58]Daniel B. Neill, Andrew W. Moore, Francisco Pereira, Tom M. Mitchell:
Detecting Significant Multidimensional Spatial Clusters. NIPS 2004: 969-976 - [c57]Dan Pelleg, Andrew W. Moore:
Active Learning for Anomaly and Rare-Category Detection. NIPS 2004: 1073-1080 - 2003
- [j11]Per H. Gesteland, Reed M. Gardner, Fu-Chiang Tsui, Jeremy U. Espino, Robert T. Rolfs, Brent C. James, Wendy Webber Chapman, Andrew W. Moore, Michael M. Wagner:
Application of Information Technology: Automated Syndromic Surveillance for the 2002 Winter Olympics. J. Am. Medical Informatics Assoc. 10(6): 547-554 (2003) - [c56]Alexander G. Gray, Andrew W. Moore:
Rapid Evaluation of Multiple Density Models. AISTATS 2003 - [c55]Paul Komarek, Andrew W. Moore:
Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs. AISTATS 2003 - [c54]Jeremy Kubica, Andrew W. Moore:
Probabilistic Noise Identification and Data Cleaning. ICDM 2003: 131-138 - [c53]Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider:
Tractable Group Detection on Large Link Data Sets. ICDM 2003: 573-576 - [c52]Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G. Schneider:
Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries. ICML 2003: 392-399 - [c51]Andrew W. Moore, Weng-Keen Wong:
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning. ICML 2003: 552-559 - [c50]Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner:
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks. ICML 2003: 808-815 - [c49]Ting Liu, Andrew W. Moore, Alexander G. Gray:
New Algorithms for Efficient High Dimensional Non-parametric Classification. NIPS 2003: 265-272 - [c48]Daniel B. Neill, Andrew W. Moore:
A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters. NIPS 2003: 651-658 - [c47]Alexander G. Gray, Andrew W. Moore:
Nonparametric Density Estimation: Toward Computational Tractability. SDM 2003: 203-211 - 2002
- [j10]Malcolm J. A. Strens, Andrew W. Moore:
Policy Search using Paired Comparisons. J. Mach. Learn. Res. 3: 921-950 (2002) - [j9]Rémi Munos, Andrew W. Moore:
Variable Resolution Discretization in Optimal Control. Mach. Learn. 49(2-3): 291-323 (2002) - [c46]Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner:
Rule-Based Anomaly Pattern Detection for Detecting Disease Outbreaks. AAAI/IAAI 2002: 217-223 - [c45]Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider, Yiming Yang:
Stochastic Link and Group Detection. AAAI/IAAI 2002: 798-806 - [c44]Fu-Chiang Tsui, Jeremy U. Espino, Michael M. Wagner, Per H. Gesteland, Oleg Ivanov, Robert T. Olszewski, Zhen Liu, Xiaoming Zeng, Wendy W. Chapman, Weng-Keen Wong, Andrew W. Moore:
Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System. AMIA 2002 - [c43]Dan Pelleg, Andrew W. Moore:
Using Tarjan's Red Rule for Fast Dependency Tree Construction. NIPS 2002: 801-808 - [c42]Scott Davies, Andrew W. Moore:
Interpolating Conditional Density Trees. UAI 2002: 119-127 - [c41]Andrew W. Moore, Jeff G. Schneider:
Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes. UAI 2002: 360-369 - 2001
- [c40]Dan Pelleg, Andrew W. Moore:
Mixtures of Rectangles: Interpretable Soft Clustering. ICML 2001: 401-408 - [c39]Peter Sand, Andrew W. Moore:
Repairing Faulty Mixture Models using Density Estimation. ICML 2001: 457-464 - [c38]Malcolm J. A. Strens, Andrew W. Moore:
Direct Policy Search using Paired Statistical Tests. ICML 2001: 545-552 - [c37]Yanxi Liu, Frank Dellaert, William E. Rothfus, Andrew W. Moore, Jeff G. Schneider, Takeo Kanade:
Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. MICCAI 2001: 655-665 - 2000
- [j8]Justin A. Boyan, Andrew W. Moore:
Learning Evaluation Functions to Improve Optimization by Local Search. J. Mach. Learn. Res. 1: 77-112 (2000) - [c36]Martin A. Riedmiller, Andrew W. Moore, Jeff G. Schneider:
Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids. Balancing Reactivity and Social Deliberation in Multi-Agent Systems 2000: 137-149 - [c35]Brigham S. Anderson, Andrew W. Moore, David Cohn:
A Nonparametric Approach to Noisy and Costly Optimization. ICML 2000: 17-24 - [c34]Geoffrey J. Gordon, Andrew W. Moore:
Learning Filaments. ICML 2000: 335-342 - [c33]Paul Komarek, Andrew W. Moore:
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets. ICML 2000: 495-502 - [c32]Rémi Munos, Andrew W. Moore:
Rates of Convergence for Variable Resolution Schemes in Optimal Control. ICML 2000: 647-654 - [c31]Dan Pelleg, Andrew W. Moore:
X-means: Extending K-means with Efficient Estimation of the Number of Clusters. ICML 2000: 727-734 - [c30]Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee:
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICRA 2000: 4096- - [c29]Alexander G. Gray, Andrew W. Moore:
'N-Body' Problems in Statistical Learning. NIPS 2000: 521-527 - [c28]Scott Davies, Andrew W. Moore:
Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous And Discrete Variables. UAI 2000: 168-175 - [c27]Andrew W. Moore:
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data. UAI 2000: 397-405
1990 – 1999
- 1999
- [c26]Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller:
Distributed Value Functions. ICML 1999: 371-378 - [c25]Andrew W. Moore, Leemon C. Baird III, Leslie Pack Kaelbling:
Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs. IJCAI 1999: 1316-1323 - [c24]Rémi Munos, Andrew W. Moore:
Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems. IJCAI 1999: 1348-1355 - [c23]Rémi Munos, Leemon C. Baird III, Andrew W. Moore:
Gradient descent approaches to neural-net-based solutions of the Hamilton-Jacobi-Bellman equation. IJCNN 1999: 2152-2157 - [c22]Dan Pelleg, Andrew W. Moore:
Accelerating Exact k-means Algorithms with Geometric Reasoning. KDD 1999: 277-281 - [c21]Scott Davies, Andrew W. Moore:
Bayesian Networks for Lossless Dataset Compression. KDD 1999: 387-391 - 1998
- [j7]Andrew W. Moore, Mary S. Lee:
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets. J. Artif. Intell. Res. 8: 67-91 (1998) - [c20]Justin A. Boyan, Andrew W. Moore:
Learning Evaluation Functions for Global Optimization and Boolean Satisfiability. AAAI/IAAI 1998: 3-10 - [c19]Scott Davies, Andrew Y. Ng, Andrew W. Moore:
Applying Online Search Techniques to Continuous-State Reinforcement Learning. AAAI/IAAI 1998: 753-760 - [c18]Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee:
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICML 1998: 386-394 - [c17]Jeff G. Schneider, Justin A. Boyan, Andrew W. Moore:
Value Function Based Production Scheduling. ICML 1998: 522-530 - [c16]Brigham S. Anderson, Andrew W. Moore:
ADtrees for Fast Counting and for Fast Learning of Association Rules. KDD 1998: 134-138 - [c15]Andrew W. Moore:
Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees. NIPS 1998: 543-549 - [c14]Leemon C. Baird III, Andrew W. Moore:
Gradient Descent for General Reinforcement Learning. NIPS 1998: 968-974 - [c13]Rémi Munos, Andrew W. Moore:
Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. NIPS 1998: 1024-1030 - [i2]Andrew W. Moore, Mary S. Lee:
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets. CoRR cs.AI/9803102 (1998) - 1997
- [j6]Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal:
Locally Weighted Learning. Artif. Intell. Rev. 11(1-5): 11-73 (1997) - [j5]Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal:
Locally Weighted Learning for Control. Artif. Intell. Rev. 11(1-5): 75-113 (1997) - [j4]Oded Maron, Andrew W. Moore:
The Racing Algorithm: Model Selection for Lazy Learners. Artif. Intell. Rev. 11(1-5): 193-225 (1997) - [c12]Kan Deng, Andrew W. Moore, Michael C. Nechyba:
Learning to recognize time series: combining ARMA models with memory-based learning. CIRA 1997: 246-251 - [c11]Andrew W. Moore, Jeff G. Schneider, Kan Deng:
Efficient Locally Weighted Polynomial Regression Predictions. ICML 1997: 236-244 - 1996
- [j3]Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore:
Reinforcement Learning: A Survey. J. Artif. Intell. Res. 4: 237-285 (1996) - [c10]Justin A. Boyan, Andrew W. Moore:
Learning Evaluation Functions for Large Acyclic Domains. ICML 1996: 63-70 - [c9]Andrew W. Moore:
Reinforcement Learning in Factories: The Auton Project (Abstract). ICML 1996: 556 - [i1]Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore:
Reinforcement Learning: A Survey. CoRR cs.AI/9605103 (1996) - 1995
- [j2]Andrew W. Moore, Christopher G. Atkeson:
The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces. Mach. Learn. 21(3): 199-233 (1995) - [c8]Kan Deng, Andrew W. Moore:
Multiresolution Instance-Based Learning. IJCAI 1995: 1233-1242 - [c7]Andrew W. Moore, Jeff G. Schneider:
Memory-based Stochastic Optimization. NIPS 1995: 1066-1072 - 1994
- [c6]Andrew W. Moore, Mary S. Lee:
Efficient Algorithms for Minimizing Cross Validation Error. ICML 1994: 190-198 - [c5]Justin A. Boyan, Andrew W. Moore:
Generalization in Reinforcement Learning: Safely Approximating the Value Function. NIPS 1994: 369-376 - 1993
- [j1]Andrew W. Moore, Christopher G. Atkeson:
Prioritized Sweeping: Reinforcement Learning With Less Data and Less Time. Mach. Learn. 13: 103-130 (1993) - [c4]Oded Maron, Andrew W. Moore:
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation. NIPS 1993: 59-66 - [c3]Andrew W. Moore:
The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces. NIPS 1993: 711-718 - [c2]Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore:
Memory-Based Methods for Regression and Classification. NIPS 1993: 1165-1166 - 1992
- [c1]Andrew W. Moore, Christopher G. Atkeson:
Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping. NIPS 1992: 263-270
Coauthor Index
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