Andrew W. Moore

114 papers A* 45A 11B 1Misc 1Journal 26Unranked 29

Year	Rank	Type	Title / Venue / Authors
2016	J	`jnl`	Four Thought Leaders on Where the Industry Is Headed. IEEE Softw. Andrew W. Moore, Tim O'Reilly, Paul D. Nielsen, Kevin Fall
2013	J	`jnl`	Interpolating Conditional Density Trees CoRR Scott Davies, Andrew W. Moore
2013	J	`jnl`	Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed Continuous And Discrete Variables CoRR Scott Davies, Andrew W. Moore
2013	J	`jnl`	Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes CoRR Andrew W. Moore, Jeff G. Schneider
2013	J	`jnl`	The Anchors Hierachy: Using the triangle inequality to survive high dimensional data CoRR Andrew W. Moore
2012	J	`jnl`	A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs CoRR Purnamrita Sarkar, Andrew W. Moore
2011	J	`jnl`	Dual-Tree Fast Gauss Transforms CoRR Dongryeol Lee, Alexander G. Gray, Andrew W. Moore
2011	—	`ch.`	Random Walks in Social Networks and their Applications: A Survey. Social Network Data Analytics Purnamrita Sarkar, Andrew W. Moore
2011	A*	`conf`	Theoretical Justification of Popular Link Prediction Heuristics. IJCAI Purnamrita Sarkar, Deepayan Chakrabarti, Andrew W. Moore
2010	J	`jnl`	Computationally efficient scoring of activity using demographics and connectivity of entities. Inf. Technol. Manag. Artur Dubrawski, John Östlund, Lujie Chen, Andrew W. Moore
2010	A*	`conf`	Fast nearest-neighbor search in disk-resident graphs. KDD Purnamrita Sarkar, Andrew W. Moore
2010	A*	`conf`	Theoretical Justification of Popular Link Prediction Heuristics. COLT Purnamrita Sarkar, Deepayan Chakrabarti, Andrew W. Moore
2009	A*	`conf`	Fast dynamic reranking in large graphs. WWW Purnamrita Sarkar, Andrew W. Moore
2008	A*	`conf`	Fast incremental proximity search in large graphs. ICML Purnamrita Sarkar, Andrew W. Moore, Amit Prakash
2007	A	`conf`	A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs. UAI Purnamrita Sarkar, Andrew W. Moore
2007	A	`conf`	Fast State Discovery for HMM Model Selection and Learning. AISTATS Sajid M. Siddiqi, Geoffrey J. Gordon, Andrew W. Moore
2006	—	`conf`	Autonomous Visualization. PKDD Khalid El-Arini, Andrew W. Moore, Ting Liu
2006	J	`jnl`	Dependency trees in sub-linear time and bounded memory. VLDB J. Dan Pelleg, Andrew W. Moore
2006	A*	`ed.`	Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006 ICML William W. Cohen, Andrew W. Moore
2006	A*	`conf`	Monitoring Food Safety by Detecting Patterns in Consumer Complaints. AAAI Artur Dubrawski, Kimberly Elenberg, Andrew W. Moore, Maheshkumar Sabhnani
2006	J	`jnl`	New Algorithms for Efficient High-Dimensional Nonparametric Classification. J. Mach. Learn. Res. Ting Liu, Andrew W. Moore, Alexander G. Gray
2006	A*	`conf`	New cached-sufficient statistics algorithms for quickly answering statistical questions. KDD Andrew W. Moore
2006	A*	`conf`	Sequential update of ADtrees. ICML Josep Roure, Andrew W. Moore
2005	—	`conf`	A Bayesian Spatial Scan Statistic. NIPS Daniel B. Neill, Andrew W. Moore, Gregory F. Cooper
2005	A*	`conf`	A multiple tree algorithm for the efficient association of asteroid observations. KDD Jeremy Kubica, Andrew W. Moore, Andrew J. Connolly, Robert Jedicke
2005	J	`jnl`	Algorithms for rapid outbreak detection: a research synthesis. J. Biomed. Informatics David L. Buckeridge, Howard S. Burkom, Murray Campbell, William R. Hogan, Andrew W. Moore
2005	—	`conf`	Bayes net graphs to understand co-authorship networks? LinkKDD Anna Goldenberg, Andrew W. Moore
2005	A*	`conf`	Detection of emerging space-time clusters. KDD Daniel B. Neill, Andrew W. Moore, Maheshkumar Sabhnani, Kenny Daniel
2005	—	`conf`	Dual-Tree Fast Gauss Transforms. NIPS Dongryeol Lee, Alexander G. Gray, Andrew W. Moore
2005	—	`conf`	Dynamic Social Network Analysis using Latent Space Models. NIPS Purnamrita Sarkar, Andrew W. Moore
2005	J	`jnl`	Dynamic social network analysis using latent space models. SIGKDD Explor. Purnamrita Sarkar, Andrew W. Moore
2005	—	`conf`	Fast Information Value for Graphical Models. NIPS Brigham S. Anderson, Andrew W. Moore
2005	A*	`conf`	Fast inference and learning in large-state-space HMMs. ICML Sajid M. Siddiqi, Andrew W. Moore
2005	A*	`conf`	Making Logistic Regression a Core Data Mining Tool with TR-IRLS. ICDM Paul Komarek, Andrew W. Moore
2005	—	`conf`	Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery. NIPS Jeremy Kubica, Joseph Masiero, Andrew W. Moore, Robert Jedicke, Andrew J. Connolly
2005	J	`jnl`	What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks. J. Mach. Learn. Res. Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner
2004	—	`conf`	Active Learning for Anomaly and Rare-Category Detection. NIPS Dan Pelleg, Andrew W. Moore
2004	—	`conf`	An Investigation of Practical Approximate Nearest Neighbor Algorithms. NIPS Ting Liu, Andrew W. Moore, Alexander G. Gray, Ke Yang
2004	A*	`conf`	Belief state approaches to signaling alarms in surveillance systems. KDD Kaustav Das, Andrew W. Moore, Jeff G. Schneider
2004	—	`conf`	Detecting Significant Multidimensional Spatial Clusters. NIPS Daniel B. Neill, Andrew W. Moore, Francisco Pereira, Tom M. Mitchell
2004	A*	`conf`	Fast nonlinear regression via eigenimages applied to galactic morphology. KDD Brigham S. Anderson, Andrew W. Moore, Andrew J. Connolly, Robert Nichol
2004	A*	`conf`	Rapid detection of significant spatial clusters. KDD Daniel B. Neill, Andrew W. Moore
2004	A*	`conf`	The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data. KDD Ting Liu, Ke Yang, Andrew W. Moore
2004	A*	`conf`	Tractable learning of large Bayes net structures from sparse data. ICML Anna Goldenberg, Andrew W. Moore
2003	—	`conf`	A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters. NIPS Daniel B. Neill, Andrew W. Moore
2003	J	`jnl`	Application of Information Technology: Automated Syndromic Surveillance for the 2002 Winter Olympics. J. Am. Medical Informatics Assoc. 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
2003	A*	`conf`	Bayesian Network Anomaly Pattern Detection for Disease Outbreaks. ICML Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner
2003	A	`conf`	Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs. AISTATS Paul Komarek, Andrew W. Moore
2003	A*	`conf`	Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries. ICML Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G. Schneider
2003	—	`conf`	New Algorithms for Efficient High Dimensional Non-parametric Classification. NIPS Ting Liu, Andrew W. Moore, Alexander G. Gray
2003	A	`conf`	Nonparametric Density Estimation: Toward Computational Tractability. SDM Alexander G. Gray, Andrew W. Moore
2003	A*	`conf`	Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning. ICML Andrew W. Moore, Weng-Keen Wong
2003	A*	`conf`	Probabilistic Noise Identification and Data Cleaning. ICDM Jeremy Kubica, Andrew W. Moore
2003	A	`conf`	Rapid Evaluation of Multiple Density Models. AISTATS Alexander G. Gray, Andrew W. Moore
2003	A*	`conf`	Tractable Group Detection on Large Link Data Sets. ICDM Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider
2002	Misc	`conf`	Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System. AMIA 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
2002	A	`conf`	Interpolating Conditional Density Trees. UAI Scott Davies, Andrew W. Moore
2002	J	`jnl`	Policy Search using Paired Comparisons. J. Mach. Learn. Res. Malcolm J. A. Strens, Andrew W. Moore
2002	A	`conf`	Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes. UAI Andrew W. Moore, Jeff G. Schneider
2002	—	`conf`	Rule-Based Anomaly Pattern Detection for Detecting Disease Outbreaks. AAAI/IAAI Weng-Keen Wong, Andrew W. Moore, Gregory F. Cooper, Michael M. Wagner
2002	—	`conf`	Stochastic Link and Group Detection. AAAI/IAAI Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider, Yiming Yang
2002	—	`conf`	Using Tarjan's Red Rule for Fast Dependency Tree Construction. NIPS Dan Pelleg, Andrew W. Moore
2002	J	`jnl`	Variable Resolution Discretization in Optimal Control. Mach. Learn. Rémi Munos, Andrew W. Moore
2001	A	`conf`	Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. MICCAI Yanxi Liu, Frank Dellaert, William E. Rothfus, Andrew W. Moore, Jeff G. Schneider, Takeo Kanade
2001	A*	`conf`	Direct Policy Search using Paired Statistical Tests. ICML Malcolm J. A. Strens, Andrew W. Moore
2001	A*	`conf`	Mixtures of Rectangles: Interpretable Soft Clustering. ICML Dan Pelleg, Andrew W. Moore
2001	A*	`conf`	Repairing Faulty Mixture Models using Density Estimation. ICML Peter Sand, Andrew W. Moore
2000	—	`conf`	'N-Body' Problems in Statistical Learning. NIPS Alexander G. Gray, Andrew W. Moore
2000	A*	`conf`	A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets. ICML Paul Komarek, Andrew W. Moore
2000	A*	`conf`	A Nonparametric Approach to Noisy and Costly Optimization. ICML Brigham S. Anderson, Andrew W. Moore, David Cohn
2000	J	`jnl`	Learning Evaluation Functions to Improve Optimization by Local Search. J. Mach. Learn. Res. Justin A. Boyan, Andrew W. Moore
2000	A*	`conf`	Learning Filaments. ICML Geoffrey J. Gordon, Andrew W. Moore
2000	A	`conf`	Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous And Discrete Variables. UAI Scott Davies, Andrew W. Moore
2000	A*	`conf`	Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICRA Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee
2000	A*	`conf`	Rates of Convergence for Variable Resolution Schemes in Optimal Control. ICML Rémi Munos, Andrew W. Moore
2000	—	`conf`	Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids. Balancing Reactivity and Social Deliberation in Multi-Agent Systems Martin A. Riedmiller, Andrew W. Moore, Jeff G. Schneider
2000	A	`conf`	The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data. UAI Andrew W. Moore
2000	A*	`conf`	X-means: Extending K-means with Efficient Estimation of the Number of Clusters. ICML Dan Pelleg, Andrew W. Moore
1999	A*	`conf`	Accelerating Exact KDD Dan Pelleg, Andrew W. Moore
1999	A*	`conf`	Bayesian Networks for Lossless Dataset Compression. KDD Scott Davies, Andrew W. Moore
1999	A*	`conf`	Distributed Value Functions. ICML Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller
1999	B	`conf`	Gradient descent approaches to neural-net-based solutions of the Hamilton-Jacobi-Bellman equation. IJCNN Rémi Munos, Leemon C. Baird III, Andrew W. Moore
1999	A*	`conf`	Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs. IJCAI Andrew W. Moore, Leemon C. Baird III, Leslie Pack Kaelbling
1999	A*	`conf`	Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems. IJCAI Rémi Munos, Andrew W. Moore
1998	A*	`conf`	ADtrees for Fast Counting and for Fast Learning of Association Rules. KDD Brigham S. Anderson, Andrew W. Moore
1998	—	`conf`	Applying Online Search Techniques to Continuous-State Reinforcement Learning. AAAI/IAAI Scott Davies, Andrew Y. Ng, Andrew W. Moore
1998	—	`conf`	Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. NIPS Rémi Munos, Andrew W. Moore
1998	J	`jnl`	Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets CoRR Andrew W. Moore, Mary S. Lee
1998	J	`jnl`	Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets. J. Artif. Intell. Res. Andrew W. Moore, Mary S. Lee
1998	—	`conf`	Gradient Descent for General Reinforcement Learning. NIPS Leemon C. Baird III, Andrew W. Moore
1998	—	`conf`	Learning Evaluation Functions for Global Optimization and Boolean Satisfiability. AAAI/IAAI Justin A. Boyan, Andrew W. Moore
1998	A*	`conf`	Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICML Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee
1998	A*	`conf`	Value Function Based Production Scheduling. ICML Jeff G. Schneider, Justin A. Boyan, Andrew W. Moore
1998	—	`conf`	Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees. NIPS Andrew W. Moore
1997	A*	`conf`	Efficient Locally Weighted Polynomial Regression Predictions. ICML Andrew W. Moore, Jeff G. Schneider, Kan Deng
1997	—	`conf`	Learning to recognize time series: combining ARMA models with memory-based learning. CIRA Kan Deng, Andrew W. Moore, Michael C. Nechyba
1997	J	`jnl`	Locally Weighted Learning for Control. Artif. Intell. Rev. Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
1997	J	`jnl`	Locally Weighted Learning. Artif. Intell. Rev. Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
1997	J	`jnl`	The Racing Algorithm: Model Selection for Lazy Learners. Artif. Intell. Rev. Oded Maron, Andrew W. Moore
1997	A	`conf`	Using Prediction to Improve Combinatorial Optimization Search. AISTATS Justin A. Boyan, Andrew W. Moore
1996	A*	`conf`	Learning Evaluation Functions for Large Acyclic Domains. ICML Justin A. Boyan, Andrew W. Moore
1996	A*	`conf`	Reinforcement Learning in Factories: The Auton Project (Abstract). ICML Andrew W. Moore
1996	J	`jnl`	Reinforcement Learning: A Survey CoRR Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore
1996	J	`jnl`	Reinforcement Learning: A Survey. J. Artif. Intell. Res. Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore
1995	—	`conf`	Memory-based Stochastic Optimization. NIPS Andrew W. Moore, Jeff G. Schneider
1995	A*	`conf`	Multiresolution Instance-Based Learning. IJCAI Kan Deng, Andrew W. Moore
1995	J	`jnl`	The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces. Mach. Learn. Andrew W. Moore, Christopher G. Atkeson
1994	A*	`conf`	Efficient Algorithms for Minimizing Cross Validation Error. ICML Andrew W. Moore, Mary S. Lee
1994	—	`conf`	Generalization in Reinforcement Learning: Safely Approximating the Value Function. NIPS Justin A. Boyan, Andrew W. Moore
1993	—	`conf`	Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation. NIPS Oded Maron, Andrew W. Moore
1993	—	`conf`	Memory-Based Methods for Regression and Classification. NIPS Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore
1993	J	`jnl`	Prioritized Sweeping: Reinforcement Learning With Less Data and Less Time. Mach. Learn. Andrew W. Moore, Christopher G. Atkeson
1993	—	`conf`	The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces. NIPS Andrew W. Moore
1992	—	`conf`	Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping. NIPS Andrew W. Moore, Christopher G. Atkeson