A. Moore

Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, 1998, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

Variable Resolution Discretization in Optimal Control

Andrew W. Moore, Rémi Munos, R. Munos, 2002, Machine Learning.

A fast multi-resolution method for detection of significant spatial overdensities

A. Moore, D. Neill, 2003 .

Forecasting Web Page Views: Methods and Observations

A. Moore, Jia Li, 2008 .

Acquisition of Dynamic Control Knowledge for a Robotic Manipulator

Andrew W. Moore, A. Moore, 1990, ML.

'N-Body' Problems in Statistical Learning

Andrew W. Moore, Alexander G. Gray, A. Moore, 2000, NIPS.

X-means: Extending K-means with Efficient Estimation of the Number of Clusters

Andrew W. Moore, Dan Pelleg, A. Moore, 2000, ICML.

Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation

Andrew W. Moore, Oded Maron, A. Moore, 1993, NIPS.

Knowledge of knowledge and intelligent experimentation for learning control

Andrew W. Moore, A. Moore, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

Efficient Algorithms for Minimizing Cross Validation Error

Andrew W. Moore, Mary S. Lee, A. Moore, 1994, ICML.

The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces

Andrew W. Moore, Christopher G. Atkeson, C. Atkeson, 1993, Machine Learning.

Using Prediction to Improve Combinatorial Optimization Search

Andrew W. Moore, Justin A. Boyan, A. Moore, 2007 .

Direct Policy Search using Paired Statistical Tests

Andrew W. Moore, Malcolm J. A. Strens, M. Strens, 2001, ICML.

Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0012333.

Influence and variance of a Markov chain: application to adaptive discretization in optimal control

R. Munos, A. Moore, 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures

Takeo Kanade, Andrew W. Moore, Yanxi Liu, 2001, MICCAI.

A Locally Weighted Learning Tutorial using Vizier 1.0

A. Moore, 1997 .

The Racing Algorithm: Model Selection for Lazy Learners

Andrew W. Moore, Oded Maron, A. Moore, 1997, Artificial Intelligence Review.

The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data

Andrew W. Moore, Ting Liu, Ke Yang, 2004, KDD.

Fast State Discovery for HMM Model Selection and Learning

Andrew W. Moore, Geoffrey J. Gordon, Sajid M. Siddiqi, 2007, AISTATS.

Accelerating exact k-means algorithms with geometric reasoning

Andrew W. Moore, Dan Pelleg, A. Moore, 1999, KDD '99.

A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters

Andrew W. Moore, Daniel B. Neill, A. Moore, 2003, NIPS.

Detection of spatial and spatio-temporal clusters

Andrew W. Moore, Daniel B. Neill, A. Moore, 2006 .

Monitoring Pharmacy Retail Data for Anomalous Space-Time Clusters

A. Moore, D. Neill, M. Wagner, 2006 .

Wsare: What’s strange about recent events?

A. Moore, G. Cooper, W. Wong, 2003, Journal of Urban Health.

Detecting anomalous patterns in pharmacy retail data

Andrew W. Moore, Daniel B. Neill, Michael M. Wagner, 2005 .

Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006

Andrew W. Moore, William W. Cohen, A. Moore, 2006, ICML.

A multiple tree algorithm for the efficient association of asteroid observations

Andrew W. Moore, Andrew J. Connolly, Robert Jedicke, 2005, KDD '05.

Sequential update of ADtrees

Andrew W. Moore, Josep Roure Alcobé, A. Moore, 2006, ICML.

Algorithms for rapid outbreak detection: a research synthesis

Andrew W. Moore, Howard S. Burkom, David L. Buckeridge, 2005, J. Biomed. Informatics.

Fast nonlinear regression via eigenimages applied to galactic morphology

Andrew W. Moore, Andrew J. Connolly, Brigham Anderson, 2004, KDD.

Learning to recognize time series: combining ARMA models with memory-based learning

Andrew W. Moore, Michael C. Nechyba, Kan Deng, 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

Memory-Based Methods for Regression and Classification

Andrew W. Moore, Thomas G. Dietterich, Christopher G. Atkeson, 1993, NIPS.

Scalable graphical models for social networks

Andrew W. Moore, Anna Goldenberg, A. Moore, 2007 .

Detection of emerging space-time clusters

Andrew W. Moore, Daniel B. Neill, Maheshkumar Sabhnani, 2005, KDD '05.

A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets

Andrew W. Moore, Paul Komarek, A. Moore, 2000, ICML.

Fast Information Value for Graphical Models

Andrew W. Moore, Brigham Anderson, A. Moore, 2005, NIPS.

Fast incremental proximity search in large graphs

Purnamrita Sarkar, Andrew W. Moore, Amit Prakash, 2008, ICML '08.

Computationally efficient scoring of activity using demographics and connectivity of entities

Andrew W. Moore, Artur Dubrawski, John Östlund, 2010, Inf. Technol. Manag..

Monitoring Food Safety by Detecting Patterns in Consumer Complaints

Andrew W. Moore, Artur Dubrawski, Maheshkumar Sabhnani, 2006, AAAI.

Efficient Analytics for Effective Monitoring of Biomedical Security

Daniel B. Neill, A. Moore, W. Wong, 2022 .

Mixtures of Rectangles: Interpretable Soft Clustering

Andrew W. Moore, Dan Pelleg, A. Moore, 2001, ICML.

Detecting Significant Multidimensional Spatial Clusters

Andrew W. Moore, Tom M. Mitchell, Daniel B. Neill, 2004, NIPS.

Dependency trees in sub-linear time and bounded memory

Andrew W. Moore, Dan Pelleg, A. Moore, 2006, The VLDB Journal.

New Algorithms for Efficient High-Dimensional Nonparametric Classification

Andrew W. Moore, Alexander G. Gray, Ting Liu, 2006, J. Mach. Learn. Res..

CHAPTER 16 – Methods for Detecting Spatial and Spatio-Temporal Clusters

Andrew W. Moore, Daniel B. Neill, A. Moore, 2006 .

Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets

Andrew W. Moore, Mary S. Lee, A. Moore, 1998, J. Artif. Intell. Res..

A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs pdf

Purnamrita Sarkar, Andrew W. Moore, A. Moore, 2007, UAI.

Nonparametric Density Estimation: Toward Computational Tractability

Andrew W. Moore, Alexander G. Gray, A. Moore, 2003, SDM.

The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data

Andrew W. Moore, A. Moore, 2000, UAI.

Dual-Tree Fast Gauss Transforms

Andrew W. Moore, Alexander G. Gray, Dongryeol Lee, 2005, NIPS.

Active Learning for Anomaly and Rare-Category Detection

Andrew W. Moore, Dan Pelleg, A. Moore, 2004, NIPS.

An Expectation-Based Scan Statistic for Detection of Space-Time Clusters

Andrew W. Moore, Daniel B. Neill, Maheshkumar Sabhnani, 2006 .

Learning Automated Product Recommendations Without Observable Features: An Initial Investigation.

Andrew W. Moore, Mary S. Lee, A. Moore, 1995 .

Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs

Andrew W. Moore, Paul Komarek, A. Moore, 2003, AISTATS.

Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous And Discrete Variables

Andrew W. Moore, Scott Davies, A. Moore, 2000, UAI.

Rapid detection of significant spatial clusters

Andrew W. Moore, Daniel B. Neill, A. Moore, 2004, KDD.

Scalable and practical probability density estimators for scientific anomaly detection

Andrew W. Moore, Dan Pelleg, A. Moore, 2004 .

Making logistic regression a core data mining tool with TR-IRLS

Andrew W. Moore, Paul Komarek, A. Moore, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Rapid Evaluation of Multiple Density Models

Andrew W. Moore, Alexander G. Gray, A. Moore, 2003, AISTATS.

Multiresolution Instance-Based Learning

Andrew W. Moore, Kan Deng, A. Moore, 1995, IJCAI.

Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees

Andrew W. Moore, A. Moore, 1998, NIPS.

Active learning for Hidden Markov Models: objective functions and algorithms

Brigham Anderson, Andrew Moore, A. Moore, 2005, ICML.

ADtrees for Fast Counting and for Fast Learning of Association Rules

Andrew W. Moore, Brigham S. Anderson, A. Moore, 1998, KDD.

Policy Search using Paired Comparisons

Andrew W. Moore, Malcolm J. A. Strens, M. Strens, 2003, J. Mach. Learn. Res..

Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning

Andrew W. Moore, Weng-Keen Wong, A. Moore, 2003, ICML.

Barycentric Interpolators for Continuous Space & Time Reinforcement Learning Category : Reinforcement Learning and Control

Andrew Moore, A. Moore, 1998 .

The Racing Algorithm : Model Selection for MemoryBased LearnersOded

A. Moore, O. Maron, 1996 .

Combining Variable Resolution Discretization Criteria for High-accuracy Solutions of Continuous Time and Space Mdps

A. Moore, R. Emi Munos, 2022 .

A Nonparametric Approach to Noisy and Costly Optimization

Andrew W. Moore, David Cohn, Brigham S. Anderson, 2000, ICML.

Eecient Locally Weighted Polynomial Regression Predictions 1 Locally Weighted Polynomial Regression

A. Moore, 2007 .

Robust Value Function Approximation by Working Backwards

A. Moore, J. Boyan, 1999 .

Variable KD-Tree Algorithms for Efficient Spatial Pattern Search

A. Moore, A. Connolly, J. Kubica, 2005 .

Tractable algorithms for proximity search on large graphs

Purnamrita Sarkar, Andrew W. Moore, A. Moore, 2010 .

Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources

A. J. Connolly, A. M. Hopkins, G. T. Richards, 2001 .

Efficient intra- and inter-night linking of asteroid detections using kd-trees

Larry Denneau, Robert Jedicke, Jeremy Kubica, 2007, astro-ph/0703475.

The Effect of Large-Scale Structure on the SDSS Galaxy Three-Point Correlation Function

A. J. Connolly, A. Szalay, A. Pope, 2006, astro-ph/0602548.

Efficient discovery of spatial associations and structure with application to asteroid tracking

Andrew W. Moore, Jeremy Kubica, A. Moore, 2005 .

Statistical Machine Learning for Large-Scale Optimization Contributors

Thomas G. Dietterich, Wray L. Buntine, R. Impagliazzo, 2022 .

Variable Resolution Dynamic Programming

A. Moore, 1991, ML.

Data mining for early disease outbreak detection

Andrew W. Moore, A. Moore, W. Wong, 2004 .

Fast Algorithms for Proximity Search on Large Graphs Purnamrita

Geoffrey J. Gordon, A. Moore, Anupam Gupta, 2008 .

T-Cube : Quick Response to Ad-Hoc Time Series Queries against Large Datasets

A. Moore, A. Dubrawski, Maheshkumar Sabhnani, 2007 .

Reinforcement Learning in Factories: The Auton Project (Abstract)

Andrew W. Moore, A. Moore, 1996, ICML.

Watermarking Shape Datasets with Utility and Distance Preservation

C. Atkeson, J. Langford, A. Moore, 2016 .

Some experiments in adaptive state-space robotics

Andrew W. Moore, William F. Clocksin, A. Moore, 1989 .

Controlling the False-Discovery Rate in Astrophysical Data Analysis

L. Wasserman, A. Moore, J. Schneider, 2001, astro-ph/0107034.

Non-Parametric Inference in Astrophysics

A. Moore, C. Genovese, R. Nichol, 2001, astro-ph/0112050.

Fast Algorithms and Efficient Statistics: Density Estimation in Large Astronomical Datasets

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0008187.

Computational AstroStatistics: Fast Algorithms and Efficient Statistics for Density Estimation in Large Astronomical Datasets

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0007404.

Combining Multiple Signals for Biosurveillance

Weng-Keen Wong, Brigham Anderson, Kaustav Das, 2006, Handbook of Biosurveillance.

Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources

L. Wasserman, A. Moore, J. Schneider, 2001, astro-ph/0110230.

Estimation of Correlations in Large Samples

Andrew W. Moore, Alexander S. Szalay, Simon Prunet, 2001 .

Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0012333.

Efficient intra- and inter-night linking of asteroid detections using kd-trees

A. Moore, J. Kubica, R. Wainscoat, 2007, astro-ph/0703475.

Proceedings, Twenty-Third International Conference on Machine Learning

Andrew W. Moore, William W. Cohen, A. Moore, 2006 .

Integrating Legacy Code Into VO's - A Test Case

Andrew W. Moore, Andrew J. Connolly, Jörg M. Colberg, 2005 .

Statistical computations with AstroGrid and the Grid

L. Wasserman, A. Moore, J. Schneider, 2005, astro-ph/0511437.

Network trafﬁc classiﬁcation via neural networks

Natinael Solomon Neggatu, A. Moore, A. Michael, 2022 .

Making Logistic Regression A Core Data Mining Tool A Practical Investigation of Accuracy, Speed, and Simplicity.

A. Moore, Paul Komarek, 2005 .

Stochastic production scheduling to meet demand forecasts

Andrew W. Moore, Jeff Schneider, Justin A. Boyan, 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

Interpolating Conditional Density Trees pdf

Andrew W. Moore, Scott Davies, A. Moore, 2002, UAI.

Random Walks in Social Networks and their Applications: A Survey

Purnamrita Sarkar, Andrew W. Moore, A. Moore, 2011, Social Network Data Analytics.

The Anchors Hierachy: Using the triangle inequality to survive high dimensional data pdf

Andrew W. Moore, A. Moore, 2013, ArXiv.

Efficiently identifying close track/observation pairs in continuous timed data

Andrew J. Connolly, Robert Jedicke, Jeremy Kubica, 2005, SPIE Optics + Photonics.

Efficient Algorithms for the Identification of Potential Track/Observation Associations in Continuous Time Data

Andrew W. Moore, Robert Jedicke, Jeremy Kubica, 2005 .

Spatial Data Structures for Efficient Trajectory-Based Queries

Andrew W. Moore, Andrew J. Connolly, Robert Jedicke, 2004 .

Using Tarjan's Red Rule for Fast Dependency Tree Construction

Andrew W. Moore, Dan Pelleg, A. Moore, 2002, NIPS.

Eight more Classic Machine Learning algorithms

Andrew W. Moore, A. Moore, 2001 .

Fast factored density estimation and compression with bayesian networks

Andrew W. Moore, Scott Tor Davies, A. Moore, 2002 .

Very Fast Outlier Detection in Large Multidimensional Data Sets

Alexander S. Szalay, Andrew W. Moore, Amitabh Chaudhary, 2002, DMKD.

Belief state approaches to signaling alarms in surveillance systems

Andrew W. Moore, Jeff G. Schneider, Kaustav Das, 2004, KDD.

Semantic-based Biomedical Image Indexing and Retrieval

Takeo Kanade, Yanxi Liu, Frank Dellaert, 2003 .

Logistic regression for data mining and high-dimensional classification

Andrew W. Moore, Paul Komarek, A. Moore, 2004 .

Probabilistic noise identification and data cleaning

Andrew W. Moore, Jeremy Kubica, A. Moore, 2003, Third IEEE International Conference on Data Mining.

What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks

Andrew W. Moore, Weng-Keen Wong, Gregory F. Cooper, 2005, J. Mach. Learn. Res..

Bayesian Network Anomaly Pattern Detection for Disease Outbreaks

Andrew W. Moore, Weng-Keen Wong, Gregory F. Cooper, 2003, ICML.

Omega: on-line memory-based general purpose system classifier

Kan Deng, Andrew William Moore, A. Moore, 1999 .

Fast, Robust Adaptive Control by Learning only Forward Models

Andrew W. Moore, A. Moore, 1991, NIPS.

Stochastic link and group detection

Yiming Yang, Andrew W. Moore, Jeff G. Schneider, 2002, AAAI/IAAI.

Bayes net graphs to understand co-authorship networks?

Anna Goldenberg, Andrew W. Moore, A. Moore, 2005, LinkKDD '05.

Tractable learning of large Bayes net structures from sparse data

Anna Goldenberg, Andrew W. Moore, A. Moore, 2004, ICML.

A Bayesian Spatial Scan Statistic

Andrew W. Moore, Gregory F. Cooper, Daniel B. Neill, 2005, NIPS.

Rule-based anomaly pattern detection for detecting disease outbreaks

Andrew W. Moore, Weng-Keen Wong, Gregory F. Cooper, 2002, AAAI/IAAI.

Proceedings of the 23rd international conference on Machine learning

William W. Cohen, Andrew W. Moore, A. Moore, 2006, ICML 2008.

SDSS-RASS: Next generation of cluster - Finding algorithms

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0011557.

Energy implications of photonic networks with speculative transmission

S. W. Moore, P. M. Watts, A. W. Moore, 2012, IEEE/OSA Journal of Optical Communications and Networking.

Efficient Algorithms for Large-Scale Asteroid Discovery

Andrew W. Moore, Andrew J. Connolly, Larry Denneau, 2007 .

The AI stack: a blueprint for developing and deploying artificial intelligence

Martial Hebert, Andrew W. Moore, Shane Shaneman, 2018, Defense + Security.

Nonparametric Density Estimation: Toward Computational Tractability

Alexander G. Gray, A. Moore, 2003, SDM.

Fast nearest-neighbor search in disk-resident graphs

Purnamrita Sarkar, Andrew W. Moore, A. Moore, 2010, KDD.

Scalable and Robust Group Discovery on Large Transactional Data

Jeremy Kubica, Andrew W. Moore, Patrick Pakyan Choi, 2005 .

K-means and Hierarchical Clustering

Andrew W. Moore, A. Moore, 2004 .

Theoretical Justification of Popular Link Prediction Heuristics

A. Moore, Purnamrita Sarkar, Deepayan Chakrabarti, 2010 .

Learning decisions: robustness, uncertainty, and approximation

A. Moore, J. Bagnell, J. Schneider, 2004 .

Variable Resolution Reinforcement Learning.

Andrew W. Moore, A. Moore, 1995 .

Dual-Tree Fast Gauss Transforms

Alexander G. Gray, A. Moore, Dongryeol Lee, 2005, NIPS.

Finding optimal Bayesian networks by dynamic programming

Andrew W. Moore, Ajit P. Singh, A. Moore, 2005 .

Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids

Martin A. Riedmiller, A. Moore, J. Schneider, 2000, Balancing Reactivity and Social Deliberation in Multi-Agent Systems.

Anomalous Spatial Cluster Detection

Andrew W. Moore, Daniel B. Neill, A. Moore, 2005 .

Tractable group detection on large link data sets

Andrew W. Moore, Jeff G. Schneider, Jeremy Kubica, 2003, Third IEEE International Conference on Data Mining.

Generalization in Reinforcement Learning: Safely Approximating the Value Function

A. Moore, J. Boyan, 1994, NIPS.

Prioritized sweeping: Reinforcement learning with less data and less time

C. Atkeson, A. Moore, 2004, Machine Learning.

Dependency trees in sub-linear time and bounded memory

A. Moore, D. Pelleg, 2006, The VLDB Journal.

Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries

Andrew W. Moore, Jeff G. Schneider, Jeremy Kubica, 2003, ICML.

Nonparametric inference in astrophysics

L. Wasserman, A. Moore, J. Schneider, 2001 .

Empirical Bayes Screening for Link Analysis

Andrew W. Moore, Anna Goldenberg, A. Moore, 2003 .

Bayesian networks for lossless dataset compression

Andrew W. Moore, Scott Davies, A. Moore, 1999, KDD '99.