Peter Sand, Andrew Moore
Repairing Faulty Mixture Models using Density Estimation
International Conference on Machine Learning, 2001
(ICML2001).
J. Andrew Bagnell, Jeff Schneider
Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods
International Conference on Robotics and Automation, 2001.
Alexander Gray and Andrew Moore,
'N-Body' Problems in Statistical Learning, Advances in Neural
Information Processing Systems 13 (Submitted May 2000, Proceedings published
May 2001).
Remi Munos and Andrew Moore,
Rates of Convergence for Variable Resolution Schemes in Optimal Control
, International Conference on Machine Learning, 2000
(ICML2000).
Paul Komarek and Andrew Moore, A
Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large
Data Sets, International Conference on Machine Learning, 2000
(ICML2000).
PDF
version of article
Geoff Gordon and Andrew Moore, Learning
Filaments, International Conference on Machine Learning, 2000
(ICML2000)
Dan Pelleg and Andrew Moore,
X-means: Extending K-means with Efficient Estimation of the Number of
Clusters, International Conference on Machine Learning, 2000 (ICML2000)
Scott Davies and Andrew Moore, Mix-nets: Factored
Mixtures of Gaussians in Bayesian Networks with Mixed Continuous and
Discrete Variables, In proceedings of UAI-2000:
The Sixteenth Conference on Uncertainty in Artificial Intelligence
Brigham Anderson, Andrew Moore, and David Cohn A Nonparametric Approach to Noisy and Costly Optimization,
International Conference on Machine Learning, 2000 (ICML2000)
Andrew W. Moore et al,
Cached Sufficient Statistics: What are they? A short white paper.
(PDF Version)
Andrew W. Moore,
The Anchors Hierarchy: Using the Triangle Inequality to Survive High
Dimensional Data,
In proceedings of UAI-2000:
The Sixteenth Conference on Uncertainty in Artificial Intelligence
Andrew W. Moore, Leemon Baird and Leslie Pack Kaelbling,
Multi-Value-Functions: Efficient Automatic Action Hierarchies
for Multiple Goal MDPs,
International Joint Conference on Artificial Intelligence, 1999 (IJCAI99).
Scott Davies and Andrew Moore.
Bayesian Networks for Lossless Dataset Compression,
Conference on Knowledge Discovery in Databases 1999, (KDD99)
Remi Munos and Andrew Moore.
Influence and Variance of a Markov Chain : Application to Adaptive
Discretization in Optimal Control,
Conference on Decision and Control 1999, (CDC99)
Dan Pelleg and Andrew Moore.
Accelerating Exact
k-means Algorithms with
Geometric Reasoning
Conference on Knowledge Discovery in Databases 1999, (KDD99)
Remi Munos and Andrew Moore.
Variable resolution discretization for high-accuracy
solutions of optimal control problems.,
International Joint Conference on Artificial Intelligence, 1999 (IJCAI99).
Remi Munos, Leemon Baird, and Andrew Moore.
Gradient Descent Approaches to Neural-Net-Based
Solutions of the Hamilton-Jacobi-Bellman Equation.
IJCNN99.
Jeff Schneider, Weng-Keen Wong, Andrew Moore, Martin Riedmiller
Distributed Value Functions,
International Conference on Machine Learning, 1999
Andrew W. Moore, Very
Fast EM-based Mixture Model Clustering using Multiresolution
kd-trees, Advances in Neural Information Processing Systems
11, (Submitted May 1998, Proceedings published May 1999).
Leemon C. Baird and Andrew W. Moore
Gradient descent for general reinforcement learning,
Advances in Neural Information Processing Systems 11, May 1999.
Remi Munos and Andrew W. Moore,
Barycentric Interpolators for Continuous Space and Time
Reinforcement Learning
, Advances in Neural Information Processing Systems
11, (Submitted May 1998, Proceedings published May 1999).
Andrew W. Moore and Jeff Schneider and Justin Boyan and
Mary Soon Lee, Q2:
Memory-based active learning for optimizing noisy continuous
functions, To be presented at the International Conference of
Machine Learning, Madison, June/July 1998. (Zipped
Version)
Andrew W. Moore and Mary Soon Lee,
Cached
Sufficient Statistics for Efficient Machine Learning with Large Datasets,
(This paper introduces AD-Trees).
(In Volume 8 of Journal
of Artificial Intelligence Research). Revised version
of CMU Robotics Institute Technical
Report CMU-RI-TR-97-27, July 1997, (24 pages)
Brigham S. Anderson and Andrew W. Moore ADtrees
for Fast Counting and for Fast Learning of Association Rules,
To Appear in KDD98 (Knowledge Discovery from Databases, New York, August 1998)
Boyan, J. A. and A. W. Moore. "Learning Evaluation Functions for Global
Optimization and Boolean Satisfiability." Fifteenth National Conference
on Artificial Intelligence (AAAI), 1998 (to appear) (Outstanding
Paper Award).
postscript
(8 pages, 420K)
Jeff Schneider and Justin Boyan and Andrew W. Moore, Value
Function Based Production Scheduling,
To be presented at the International Conference of
Machine Learning, Madison, June/July 1998
S. Davies and A. Y. Ng and A. W. Moore, Applying
Online Search Techniques to Reinforcement Learning Fifteenth National
Conference on Artificial Intelligence (AAAI), 1998 (to appear).
A. W. Moore, An
introductory tutorial on kd-trees Extract from A. W. Moore's
Phd. thesis: Efficient Memory-based Learning for Robot Control, Computer
Laboratory, University of Cambridge, Technical Report No. 209, 1991.
A. W. Moore and J. Schneider and K. Deng, Efficient
Locally Weighted Polynomial Regression Predictions, Proceedings
of the 1997 International Machine Learning Conference, Morgan Kaufmann
Publishers.
C. G. Atkeson, S. A. Schaal and Andrew W, Moore, Locally
Weighted Learning, AI Review,Volume 11, Pages 11-73 (Kluwer
Publishers) 1997
Andrew W, Moore, C. G. Atkeson, S. A. Schaal, Locally
Weighted Learning For Control, AI Review, Volume 11, Pages 75-113
(Kluwer Publishers) 1997
S. Davies, Multidimensional
Interpolation and Triangulation for Reinforcement Learning, NIPS-96,
1996 (8 pages)
Jeff G. Schneider, Exploiting
Model Uncertainty Estimates for Safe Dynamic Control Learning,
Neural Information Processing Systems 9, 1996
Kaelbling, L.P., Littman, M.L., and Moore, A.W. (1996) Reinforcement
Learning: A Survey, Journal of Artificial Intelligence Research
Volume 4, pages 237-285. PostScript
article version
Boyan, J. A. and A. W. Moore. "Learning Evaluation Functions for Large
Acyclic Domains." In L. Saitta (ed.), Machine Learning: Proceedings
of the Thirteenth International Conference. Morgan Kaufmann, 1996.
postscript
(8 pages, 147K)
A. W. Moore and J. Schneider, Memory-based
Stochastic Optimization, NIPS-95, 1995 (8 pages)
A. W. Moore and C. G. Atkeson, The
Parti-game Algorithm for Variable Resolution Reinforcement Learning in
Multidimensional State-spaces, Machine Learning, Volume 21,
December 1995 (36 pages)
K. Deng and A. W. Moore, Multiresolution
Instance-Based Learning, Proceedings of the International Joint
Conference on Artificial Intelligence (IJCAI), 1995 (7 pages)
A. W. Moore, C. G. Atkeson, and S. Schaal, Memory-based
Learning for Control, CMU Robotics Institute Technical Report CMU-RI-TR-95-18,
April 1995 (39 pages)
Mary Soon Lee and Andrew Moore, Learning
Automated Product Recommendations Without Observable Features: An Initial
Investigation, CMU Robotics Institute Technical Report CMU-RI-TR-95-17,
April 1995 (35 pages)
Justin Boyan and Andrew Moore, Generalization
in Reinforcement Learning: Safely Approximating the Value Function,
Proceedings of Neural Information Processings Systems 7, Morgan Kaufmann,
January 1995 (8 pages)
A. W. Moore and M. S. Lee, Efficient
Algorithms for Minimizing Cross Validation Error, Proceedings of
the 11th International Conference on Machine Learning, Morgan Kaufmann,
1994 (9 pages)
A. W. Moore, Variable Resolution Reinforcement Learning, Proceedings
of the Eighth Yale Workshop on Adaptive and Learning Systems, 1994
A. W. Moore and C. G. Atkeson, Prioritized
Sweeping: Reinforcement Learning with Less Data and Less Real Time,
Machine Learning, Volume 13, October 1993
A. W. Moore, The Parti-game Algorithm for Variable Resolution Reinforcement
Learning in Multidimensional State-spaces, Advances in Neural Information
Processing Systems 6, Morgan Kaufmann, 1993
O. Maron and A. W. Moore, Hoeffding
Races: Accelerating Model Selection Search for Classification and Function
Approximation, Advances in Neural Information Processing Systems 6, Morgan Kaufmann, 1993 (8 pages)
A. W. Moore and C. G. Atkeson,Memory-based Reinforcement Learning: Converging with Less Data andLess Real Time, Robot Learning, editors J. Connell and S. Mahadevan,Kluwer Academic Publishers, 1993
A. W. Moore, D. J. Hill, and M. P
. Johnson,An
Empirical Investigation of Brute Force to choose Features, Smoothers and
Function Approximators, Computational Learning Theory and Natural
Learning Systems, Volume 3, editors S. Hanson, S. Judd, and T. Petsche,
MIT Press, 1994 (20 pages)
A. W. Moore and C. G. Atkeson, Memory-based Reinforcement Learning:
Efficient Computation with Prioritized Sweeping, Advances in Neural
Information Processing Systems 5, editors S. J. Hanson, J. D Cowan, and
C. L. Giles, Morgan Kaufmann, 1992
A. W. Moore, Fast, Robust Adaptive Control by Learning only Forward
Models, Advances in Neural Information Processing Systems 4, editors
J. E. Moody, S. J. Hanson, and R. P. Lippman, Morgan Kaufmann, 1991
A. W. Moore, Knowledge of Knowledge and Intelligent Experimentation
for Learning Control, Proceedings of the 1991 Seattle International
Joint Conference on Neural Networks, July 1991
A. W. Moore, Variable Resolution Dynamic Programming: Efficiently Learning
Action Maps in Multivariate Real-valued State-spaces, Proceedings of
the Eighth International Conference on Machine Learning, editors L. Birnbaum
and G. Collins, Morgan Kaufman, June 1991
A. W. Moore, Efficient Memory-based Learning for Robot Control,
PhD. Thesis: University of Cambridge, Computer Science, Technical Report
209, March 1991
A. W. Moore, Acquisition of Dynamic Control Knowledge for a Robotic
Manipulator, Proceedings of the 7th International Conference on Machine
Learning, Morgan Kaufman, June 1990
W. F. Clocksin and A. W. Moore Some Experiments in Adaptive State Space
Robotics, Proceedings of the 7th AISB Conference, Brighton, Morgan
Kaufman, April 1989
Andrew Moore's 1991 PhD Thesis: Efficient Memory Based Robot
Learning (Technical Report 209, University of Cambridge).
Part 1,
Part 2,
Part 3,
Part 4