/Auton Lab

Auton Lab

Portrait of Auton Lab
Lab Homepage
Mailing Address

The Auton Lab, part of Carnegie Mellon University’s School of Computer Science, researches new approaches to Statistical Data Mining. It is directed by Artur Dubrawski, Andrew Moore and Jeff Schneider. We are very interested in the underlying computer science, mathematics, statistics and AI of detection and exploitation of patterns in data.

We build practical large-scale deployments of very highly autonomous self-improving systems. We gratefully acknowledge funding support from NSF, DARPA, NASA, USDA, CDC, FDA, DHS, DoD, the State of Pennsylvania, other agencies, and over a dozen Fortune 500 companies with whom we have collaborated.

Please see our publications page for a complete list of publications.

Displaying 73 Publications
Bayesian Aggregation of Evidence For Detection and Characterization of Patterns in Multiple Noisy Observations
Prateek Tandon

PhD Thesis, Tech. Report, CMU-RI-TR-15-23, Robotics Institute, Carnegie Mellon University, September, 2015
Learning Latent Variable and Predictive Models of Dynamical Systems
Sajid Siddiqi

PhD Thesis, Tech. Report, CMU-RI-TR-09-39, Robotics Institute, Carnegie Mellon University, January, 2010
Learning Outbreak Regions in Bayesian Spatial Scan Statistics
Maxim Makatchev and Daniel Bertrand Neill

Conference Paper, ICML 2008 Workshop on Machine Learning for Health Care Applications, July, 2008
Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances
Maxim Makatchev and Kurt VanLehn

Conference Paper, Proc. Int. Conf. on AI in Education, AIED2007, July, 2007
Efficient Discovery of Spatial Associations and Structure with Application to Asteroid Tracking
Jeremy Martin Kubica

PhD Thesis, Tech. Report, CMU-RI-TR-06-01, Robotics Institute, Carnegie Mellon University, December, 2005
Scalable and robust group discovery on large transactional data
Pak Yan Choi, Andrew Moore and Jeremy Martin Kubica

Tech. Report, CMU-RI-TR-05-60, Robotics Institute, Carnegie Mellon University, December, 2005
Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery
Jeremy Martin Kubica, Joseph Masiero, Andrew Moore, Robert Jedicke and Andrew J. Connolly

Conference Paper, Neural Information Processing Systems, December, 2005
Making Logistic Regression A Core Data Mining Tool: A Practical Investigation of Accuracy, Speed, and Simplicity
Paul Komarek and Andrew Moore

Tech. Report, CMU-RI-TR-05-27, Robotics Institute, Carnegie Mellon University, May, 2005
Efficient Algorithms for the Identification of Potential Track/Observation Associations in Continuous Time Data
Jeremy Martin Kubica, Andrew Moore, Andrew J. Connolly and Robert Jedicke

Tech. Report, CMU-RI-TR-05-10, Robotics Institute, Carnegie Mellon University, February, 2005
Fast and Robust Track Initiation Using Multiple Trees
Jeremy Martin Kubica, Andrew Moore, Andrew J. Connolly and Robert Jedicke

Tech. Report, CMU-RI-TR-04-62, Robotics Institute, Carnegie Mellon University, November, 2004
Spatial Data Structures for Efficient Trajectory-Based Queries
Jeremy Martin Kubica, Andrew Moore, Andrew J. Connolly and Robert Jedicke

Tech. Report, CMU-RI-TR-04-61, Robotics Institute, Carnegie Mellon University, November, 2004
Fast Nonlinear Regression via Eigenimages Applied to Galactic Morphology
Brigham Anderson, Andrew Moore, Andrew J. Connolly and Robert Nichol

Conference Paper, International Conference on Knowledge Discovery and Data Mining, August, 2004
Logistic Regression for Data Mining and High-Dimensional Classification
Paul Komarek

Tech. Report, CMU-RI-TR-04-34, Robotics Institute, Carnegie Mellon University, May, 2004
Alias Detection in Link Data Sets
Paul Hsiung

Master's Thesis, Tech. Report, CMU-RI-TR-04-22, Robotics Institute, Carnegie Mellon University, March, 2004
Policy Search by Dynamic Programming
J. Andrew (Drew) Bagnell, Sham Kakade, Andrew Ng and Jeff Schneider

Conference Paper, Neural Information Processing Systems, Vol. 16, December, 2003
Covariant Policy Search
J. Andrew (Drew) Bagnell and Jeff Schneider

Conference Paper, Proceeding of the International Joint Conference on Artifical Intelligence, August, 2003
Learning with scope; with application to information extraction and classification
David Blei, J. Andrew (Drew) Bagnell and Andrew McCallum

Conference Paper, Uncertainty in Artificial Intelligence, pp. 53-60, June, 2002
Solving Uncertain Markov Decision Problems
J. Andrew (Drew) Bagnell, Andrew Y. Ng and Jeff Schneider

Tech. Report, CMU-RI-TR-01-25, Robotics Institute, Carnegie Mellon University, August, 2001
Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods
J. Andrew (Drew) Bagnell and Jeff Schneider

Conference Paper, Proceedings of the International Conference on Robotics and Automation 2001, May, 2001
Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous and Discrete Variables
Scott Davies and Andrew Moore

Tech. Report, CMU-CS-00-119, Computer Science Department, Carnegie Mellon University, April, 2000
Influence and Variance of a Markov Chain: Application to Adaptive Discretization in Optimal Control
Remi Munos and Andrew Moore

Conference Paper, IEEE Conference on Decision and Control, Vol. 2, pp. 1464 - 1469, December, 1999
Variable resolution discretization for high-accuracy solutions of optimal control problems
Remi Munos and Andrew Moore

Conference Paper, International Joint Conference on Artificial Intelligence, August, 1999
Cached Sufficient Statistics for Automated Mining and Discovery from Massive Data Sources
Andrew Moore, Jeff Schneider, Brigham Anderson, Scott Davies, Paul Komarek, Mary Soon Lee, Marina Meila, Remi Munos, Kary Myers and Dan Pelleg

Miscellaneous, July, 1999
Gradient Descent Approaches to Neural-Net-Based Solutions of the Hamilton-Jacobi-Bellman Equation
Remi Munos, Leemon Baird and Andrew Moore

Conference Paper, International Joint Conference on Neural Networks, July, 1999
Reinforcement Learning Through Gradient Descent
Leemon Baird

PhD Thesis, Tech. Report, CMU-CS-99-132, Computer Science Department, Carnegie Mellon University, May, 1999
A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions
Remi Munos

Journal Article, Machine Learning Journal, January, 1999
Accelerating Exact k-means Algorithms with Geometric Reasoning
Dan Pelleg and Andrew Moore

Conference Paper, Knowledge Discovery from Databases (KDD '99), January, 1999
Approximate Solutions to Markov Decision Processes
Geoffrey Gordon

PhD Thesis, Tech. Report, January, 1999
Bayesian Networks for Lossless Dataset Compression
Scott Davies and Andrew Moore

Conference Paper, 1999 Knowledge Discovery from Databases (KDD '99), January, 1999
Distributed Value Functions
Jeff Schneider, Weng-Keen Wong, Andrew Moore and Martin Riedmiller

Conference Paper, International Conference on Machine Learning, January, 1999
Gradient Descent for General Reinforcement Learning
Leemon Baird and Andrew Moore

Journal Article, Advances in Neural Information Processing Systems 11, January, 1999
Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs
Andrew Moore, Leemon Baird and Leslie Pack Kaelbling

Conference Paper, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '99), January, 1999
Regret bounds for prediction problems
Geoffrey Gordon

Conference Paper, Proceedings of COLT '99, January, 1999
Variable Resolution Discretization in Optimal Control
Remi Munos and Andrew Moore

Journal Article, Machine Learning Journal, January, 1999
Barycentric Interpolator for Continuous Space and Time Reinforcement Learning
Remi Munos and Andrew Moore

Conference Paper, Neural Information Processing Systems, Vol. 11, December, 1998
Very Fast EM-based Mixture Model Clustering using Multiresolution kd-trees
Andrew Moore

Journal Article, Neural Information Systems Processing, December, 1998
ADtrees for Fast Counting and for Fast Learning of Association Rules
Brigham Anderson and Andrew Moore

Journal Article, Knowledge Discovery from Databases '98, August, 1998
Q2: Memory-based active learning for optimizing noisy continuous functions
Andrew Moore, Jeff Schneider, Justin Boyan and Mary Lee

Conference Paper, International Conference of Machine Learning, June, 1998
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets
Andrew Moore and Mary Soon Lee

Journal Article, Journal of Artificial Intelligence Research, Vol. 8, pp. 67- 91, March, 1998
Value Function Based Production Scheduling
Jeff Schneider, Justin Boyan and Andrew Moore

Conference Paper, Machine Learning: Proceedings of the Fifteenth International Conference (ICML '98), March, 1998
A general convergence method for Reinforcement Learning in the continuous case
Remi Munos

Conference Paper, European Conference on Machine Learning, January, 1998
Applying Online Search Techniques to Reinforcement Learning
Scott Davies, A. Y. Ng and Andrew Moore

Conference Paper, Fifteenth National Conference on Artificial Intelligence (AAAI), January, 1998
Learning Evaluation Functions for Global Optimization and Boolean Satisfiability
Justin Boyan and Andrew Moore

Conference Paper, Fifteenth National Conference on Artificial Intelligence, January, 1998
A convergent Reinforcement Learning algorithm in the continuous case based on a Finite Difference method
Remi Munos

Conference Paper, 1997 International Joint Conference on Artificial Intelligence (IJCAI '97), January, 1997
Efficient Locally Weighted Polynomial Regression Predictions
Andrew Moore, Jeff Schneider and Kan Deng

Conference Paper, International Conference on Machine Learning, January, 1997
Finite-Element methods with local triangulation refinement for continuous Reinforcement Learning problems
Remi Munos

Conference Paper, European Conference on Machine Learning 1997, January, 1997
Locally Weighted Learning
C. G. Atkeson, S. A. Schaal and Andrew Moore

Journal Article, AI Review, Vol. 11, January, 1997
Locally Weighted Learning For Control
Andrew Moore, C. G. Atkeson and S. A. Schaal

Journal Article, AI Review, Vol. 11, pp. 75-113, January, 1997
Reinforcement Learning for Continuous Stochastic Control Problems
Remi Munos and Paul Bourgine

Conference Paper, Neural Information Processing Systems, January, 1997
A Convergent Reinforcement Learning algorithm in the continuous case: the Finite-Element Reinforcement Learning
Remi Munos

Conference Paper, International Conference on Machine Learning 1996 (ICML '96), January, 1996
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
Jeff Schneider

Conference Paper, Neural Information Processing Systems 9, January, 1996
Learning Evaluation Functions for Large Acyclic Domains
Justin Boyan and Andrew Moore

Conference Paper, Machine Learning: Proceedings of the Thirteenth International Conference, January, 1996
Reinforcement Learning: A Survey
L.P. Kaelbling, M.L. Littman and Andrew Moore

Journal Article, Journal of Artificial Intelligence Research, Vol. 4, pp. 237-285, January, 1996
Using Finite-Differences methods for approximating the value function of continuous Reinforcement Learning problems
Remi Munos

Conference Paper, International Symposium on Multi-Technology Information Processing 1996, January, 1996
The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces
Andrew Moore and C. G. Atkeson

Journal Article, Machine Learning, Vol. 21, December, 1995
Learning Automated Product Recommendations Without Observable Features: An Initial Investigation
Mary S. Lee and Andrew Moore

Tech. Report, CMU-RI-TR-95-17, Robotics Institute, Carnegie Mellon University, April, 1995
Memory-Based Learning for Control
Andrew Moore, C. G. Atkeson and S. A. Schaal

Tech. Report, CMU-RI-TR-95-18, Robotics Institute, Carnegie Mellon University, April, 1995
Generalization in Reinforcement Learning: Safely Approximating the Value Function
Justin Boyan and Andrew Moore

Conference Paper, Advances in Neural Information Processing Systems 7, January, 1995
Memory-based Stochastic Optimization
Andrew Moore and Jeff Schneider

Conference Paper, Neural Information Processing Systems 8, January, 1995
Multiresolution Instance-Based Learning
Andrew Moore, Jeff Schneider and Kan Deng

Conference Paper, Proceedings of International Joint Conference on Artificial Intelligence, January, 1995
Online Fitted Reinforcement Learning
Geoffrey Gordon

Conference Paper, VFA workshop at ML-95, January, 1995
Stable Function Approximation in Dynamic Programming
Geoffrey Gordon

Tech. Report, CMU-CS-95-103, Computer Science Department, Carnegie Mellon University, January, 1995
Stable Function Approximation in Dynamic Programming
Geoffrey Gordon

Conference Paper, Proceedings of IMCL '95, January, 1995
An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators
Andrew Moore, D. J. Hill and M. P . Johnson

Journal Article, Computational Learning Theory and Natural Learning Systems, Vol. 3, January, 1994
Efficient Algorithms for Minimizing Cross Validation Error
Andrew Moore and M. S. Lee

Conference Paper, Proceedings of the 11th International Conference on Machine Learning, January, 1994
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time
Andrew Moore and C. G. Atkeson

Journal Article, Machine Learning, Vol. 13, October, 1993
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
O. Maron and Andrew Moore

Conference Paper, Advances in Neural Information ProcessingSystems 6, January, 1993
Memory-based Reinforcement Learning: Efficient Computation with Prioritized Sweeping
Andrew Moore and C. G. Atkeson

Conference Paper, Advances in Neural Information Processing Systems 5, January, 1992
Knowledge of Knowledge and Intelligent Experimentation for Learning Control
Andrew Moore

Conference Paper, Proceedings of the 1991 Seattle International Joint Conference on Neural Networks, Vol. 2, pp. 683 - 688, July, 1991
Variable Resolution Dynamic Programming: Efficiently Learning Action Maps in Multivariate Real-valued State-spaces
Andrew Moore

Conference Paper, Proceedings of the Eighth International Conference on Machine Learning, January, 1991
Acquisition of Dynamic Control Knowledge for a Robotic Manipulator
Andrew Moore

Conference Paper, Proceedings of the 7th International Conference on Machine Learning, January, 1990
Some Experiments in Adaptive State Space Robotics
W. F. Clocksin and Andrew Moore

Conference Paper, Proceedings of the 7th AISB Conference, January, 1989
2017-09-13T10:47:32+00:00