
- Visualization of Collaborative Data Guobiao Mei
- Journal of Machine Learning Research 11 (2010) 21152140 Submitted 3/09; Revised 6/10; Published 8/10 Importance Sampling for Continuous Time Bayesian Networks
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- Machine Learning, Machine Vision, and the Brain Tomaso Poggio and Christian R. Shelton
- Solving Structured Continuous-Time Markov Decision Processes Kin Fai Kan and Christian R. Shelton
- Learning Continuous Time Bayesian Networks Uri Nodelman
- Modeling Stock Order Flows and Learning Market-Making from Data
- Continuous Time Bayesian Networks Uri Nodelman
- Balancing Multiple Sources of Reward in Reinforcement Learning
- Continuous Time Bayesian Networks for Host Level Network Intrusion Detection
- Chained Boosting Christian R. Shelton
- Auton Agent Multi-Agent Syst DOI 10.1007/s10458-006-0005-z
- A Continuation Method for Nash Equilibria in Structured Games Stanford University
- Chained Boosting Christian R. Shelton
- Fast Time Series Classification Using Numerosity Reduction Xiaopeng Xi XXI@CS.UCR.EDU
- Cobot: A Social Reinforcement Learning Agent Charles Lee Isbell, Jr. Christian R. Shelton
- Simultaneous Learning of Motion and Sensor Model Parameters for Mobile Robots
- Momentum Control for Balance Adriano Macchietto Victor Zordan Christian R. Shelton
- A Perturbation Scheme for Spherical Arrangements with Application to Molecular Modeling \Lambda
- A Particle Filter for Monocular Vision-Aided Odometry Teddy Yap, Jr
- Journal of Machine Learning Research 11 (2010) 2115-2140 Submitted 3/09; Revised 6/10; Published 8/10 Importance Sampling for Continuous Time Bayesian Networks
- ThreeDimensional Correspondence Christian R. Shelton
- Imp rovement for POMDPs Using Normalized
- Learning from Scarce Experience Leonid Peshkin PESHA@EECS.HARVARD.EDU
- Visualization of Collaborative Data Guobiao Mei
- Importance Sampling for Reinforcement Learning with Multiple Objectives
- Learning ContinuousTime Social Network Dynamics University of California, Riverside
- VETERINARY ENTOMOLOGY Evaluation of Surveillance Methods for Monitoring House Fly
- Spatial Vision, Vol. 13, No. 2,3, pp. 287296 (2000) VSP 2000.
- Interactive Event Search Through Transfer Antony Lam,1
- massachusetts institute of technology --artificial intelligence laboratory Importance Sampling for
- Face Recognition and Alignment using Support Vector Machines University of California, Riverside
- Journal of Artificial Intelligence Research 39 (2010) 745774 Submitted 03/10; published 12/10 Intrusion Detection using Continuous Time Bayesian Networks
- Machine Learning, Machine Vision, and the Brain Tomaso Poggio and Christian R. Shelton
- Journal of Artificial Intelligence Research 25 (2006) 457-502 Submitted 11/05; published 4/06 A Continuation Method for Nash Equilibria in Structured
- Simultaneous Learning of Motion and Sensor Model Parameters for Mobile Robots
- Journal of Artificial Intelligence Research 25 (2006) 457502 Submitted 11/05; published 4/06 A Continuation Method for Nash Equilibria in Structured
- Reinforcement Learning with Partially Known World Dynamics Christian R. Shelton
- Face Recognition and Alignment using Support Vector Machines University of California, Riverside
- Unsupervised Image Embedding Using Nonparametric Statistics Guobiao Mei
- Modeling Stock Order Flows and Learning MarketMaking from Data
- Balancing Multiple Sources of Reward in Reinforcement Learning
- Annotating Historical Archives of Images Xiaoyue Wang Lexiang Ye Eamonn Keogh Christian Shelton
- Catenary Support Vector Machines Kin Fai Kan and Christian R. Shelton
- Importance Sampling Estimates for Policies with Memory Christian R. Shelton cshelton@ai.mit.edu
- Unsupervised Image Embedding Using Nonparametric Statistics Guobiao Mei
- Sampling for Approximate Inference in Continuous Time Bayesian Networks
- SLAM in Large Indoor Environments with LowCost, Noisy, and Sparse Sonars
- Morphable Surface Models # Christian R. Shelton
- Policy Improvement for POMDPs using Normalized Importance Sampling
- Learning Continuous Time Bayesian Networks Uri Nodelman
- Journal of Artificial Intelligence Research 39 (2010) 745--774 Submitted 03/10; published 12/10 Intrusion Detection using Continuous Time Bayesian Networks
- ORIGINAL ARTICLE Applying modality and equivalence concepts to pattern finding
- Journal of Machine Learning Research 11 (2010) 1137-1140 Submitted 10/09; Revised 1/10; Published 3/10 Continuous Time Bayesian Network Reasoning and Learning Engine
- Momentum Control for Balance Adriano Macchietto Victor Zordan Christian R. Shelton
- SLAM in Large Indoor Environments with Low-Cost, Noisy, and Sparse Sonars
- Sampling for Approximate Inference in Continuous Time Bayesian Networks
- Book Editors IOS Press, 2003
- Expectation Propagation for Continuous Time Bayesian Networks
- Reinforcement Learning with Partially Known World Dynamics Christian R. Shelton
- Learning from Scarce Experience Leonid Peshkin PESHA@EECS.HARVARD.EDU
- Importance Sampling for Reinforcement Learning with Multiple Objectives
- Policy Improvement for POMDPs using Normalized Importance Sampling
- Importance Sampling Estimates for Policies with Memory Christian R. Shelton cshelton@ai.mit.edu
- Morphable Surface Models Christian R. Shelton
- Three-Dimensional Correspondence Christian R. Shelton
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- Solving Structured ContinuousTime Markov Decision Processes Kin Fai Kan and Christian R. Shelton
- Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
- Continuous Time Bayesian Networks Uri Nodelman
- A Continuation Method for Nash Equilibria in Structured Games Stanford University
- Learning Continuous-Time Social Network Dynamics University of California, Riverside
- Policy Improvement for POMDPs Using Normalized
- Factored Filtering of Continuous-Time Systems E. Busra Celikkaya