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- Valuedirected Compression of POMDPs Pascal Poupart
- VDCBPI: an Approximate Scalable Algorithm for Large POMDPs
- Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes
- Approximate Value-Directed Belief State Monitoring for Partially Observable Markov Decision Processes
- Model-based Bayesian Reinforcement Learning in Partially Observable Domains
- Piecewise Linear Value Function Approximation for Factored MDPs Pascal Poupart and Craig Boutilier
- Explaining Recommendations Generated by Omar Zia Khan, Pascal Poupart, and James P. Black
- An Analytic Solution to Discrete Bayesian Reinforcement Learning Pascal Poupart ppoupart@cs.uwaterloo.ca
- Vectorspace Analysis of Beliefstate Approximation for POMDPs Pascal Poupart
- Towards Bayesian Reinforcement Learning
- Solving POMDPs with Continuous or Large Discrete Observation Spaces Department of Computer Science
- Greedy linear valueapproximation for factored Markov decision processes Relu Patrascu
- AUTOMATIC SPEECH FEATURE EXTRACTION FOR COGNITIVE LOAD CLASSIFICATION
- Probabilistic 3D Tracking: Rollator Users' Leg Pose from Coronal Images Samantha Ng Adel Fakih Adam Fourney Pascal Poupart
- Regretbased Utility Elicitation in Constraintbased Decision Problems Craig Boutilier
- ValueDirected Sampling Methods for Monitoring POMDPs Pascal Poupart
- Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker
- The Advisor-POMDP: A Principled Approach to Trust through Reputation in Electronic Markets
- Active Learning with Semi-Supervised Support Vector Machines
- Constraintbased Optimization with the Minimax Decision Criterion
- "Is the Sky Pure Today?" AwkChecker: An Assistive Tool for Detecting and
- Performing Incremental Bayesian Inference by Dynamic Model Counting Wei Li and Peter van Beek and Pascal Poupart
- A Smart Walker to Understand Walking Abilities
- Hierarchical POMDP Controller Optimization by Likelihood Maximization
- Automated Hierarchy Discovery for Planning in Partially Observable Environments
- Solving POMDPs with Continuous or Large Discrete Observation Spaces Department of Computer Science
- Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes
- Value-directed Compression of POMDPs Pascal Poupart
- Value-Directed Sampling Methods for Monitoring POMDPs Pascal Poupart
- Value-Directed Belief State Approximation for POMDPs Pascal Poupart
- Bayesian Reputation Modeling in E-Marketplaces Sensitive to Subjectivity, Deception and Change
- Artificial Intelligence 170 (2006) 686713 www.elsevier.com/locate/artint
- Regret-based Utility Elicitation in Constraint-based Decision Problems Craig Boutilier
- Refining Diagnostic POMDPs with User Feedback Omar Zia Khan and Pascal Poupart
- Minimal Sufficient Explanations for Factored Markov Decision Processes Omar Zia Khan, Pascal Poupart and James P. Black
- Towards Global Reinforcement Learning Milen Pavlov
- Generating Lexical Analogies Using Dependency Relations Andy Chiu, Pascal Poupart, and Chrysanne DiMarco
- Greedy linear value-approximation for factored Markov decision processes Relu Patrascu
- February 16, 2010 University of Kentucky, Lexington
- SymbolicSymbolic PerseusPerseus
- Towards Global Reinforcement Milen Pavlov
- Dynamic Factored Particle Filtering for Context-Specific Correlations
- Curriculum Vitae Pascal Poupart
- Automated Hierarchy Discovery for Planning in Partially Observable Environments
- From Atoms to the Solar System: Generating Lexical Analogies from Text
- Learning Lexical Semantic Relations using Lexical Analogies --Extended Abstract
- ValueDirected Belief State Approximation for POMDPs Pascal Poupart
- Assisting Persons with Dementia during Handwashing Using a Partially Observable Markov
- An Analytic Solution to Discrete Bayesian
- Vector-space Analysis of Belief-state Approximation for POMDPs Pascal Poupart
- Bounded Finite State Controllers Pascal Poupart
- Piecewise Linear Value Function Approximation for Factored MDPs Pascal Poupart and Craig Boutilier
- Bounded Finite State Controllers Pascal Poupart
- VDCBPI: an Approximate Scalable Algorithm for Large POMDPs
- Automated Hierarchy Discovery for Planning in Partially Observable Domains
- Towards a Mobility Diagnostic Tool: Tracking Rollator Users' Leg Pose With a Monocular Vision System
- Continuous Correlated Beta Processes Robby Goetschalckx Pascal Poupart, Jesse Hoey
- AMBULATORY MEASUREMENT OF DUAL-TASKING BEHAVIOUR: METHOD AND PRELIMINARY EVALUATION IN OLDER ADULTS
- 3D Pose Tracking of Walker Users' Lower Limb with a Structured-Light Camera on a Moving Platform
- Asymptotic Theory for Linear-Chain Conditional Random Fields Mathieu Sinn Pascal Poupart
- Activity Recognition for Users of Rolling Walker Mobility Aids
- Analyzing and Escaping Local Optima in Planning as Inference for
- Asymptotic Theory for Linear-Chain Conditional Random Fields -Supplementary Material -
- Evaluation Results for a Query-Based Diagnostics Application John Mark Agosta, john.m.agosta@intel.com
- Point-Based Value Iteration for Constrained POMDPs Dongho Kim Jaesong Lee Kee-Eung Kim
- Ambulatory Assessment of Lifestyle Factors for Alzheimer's Disease and Related Dementias
- VALMA: VOICE, ACTIVITY, AND LOCATION MONITORING FOR ALZHEIMER'S DISEASE AND RELATED DEMENTIAS
- Closing the Gap: Improved Bounds on Optimal POMDP Solutions Pascal Poupart
- Vision-Based Observation Models for Lower Limb 3D Tracking with a
- Bayesian Unsupervised Labeling of Web Document Clusters
- Smart Walkers! Enhancing the Mobility of the Elderly (Extended Abstract)
- Error Bounds for Online Predictions of Linear-Chain Conditional Random Fields.
- Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints