- Graphical Models MLSS 2010, Canberra
- Probabilistic Latent Maximal Marginal Relevance Shengbo Guo
- Learning CRFs with Hierarchical Features: An Application to Go Scott Sanner ssanner@cs.toronto.edu
- Relational and First-Order Decision-Theoretic Planning: Foundations and Future Directions
- Approximate Solution Techniques for Factored Firstorder MDPs Scott Sanner
- Towards Practical Taxonomic Classi cation for Description Logics on the Semantic Web
- Simultaneous Learning of Structure and Value in Relational Reinforcement Learning
- Bayesian Real-time Dynamic Programming Scott Sanner
- Affine Algebraic Decision Diagrams (AADDs) and their Application to Structured Probabilistic Inference
- Approximate Dynamic Programming with Affine ADDs Scott Sanner
- Natural Language Question Answering Over Triple Knowledge Bases
- Cost-sensitive Parsimonious Linear Regression Robby Goetschalckx Kurt Driessens
- Learning CRFs with Hierarchical Features: An Application to Go Scott Sanner ssanner@cs.toronto.edu
- Symbolic Dynamic Programming Scott Sanner
- Simultaneous Learning of Structure and Value in Relational Reinforcement Learning
- First-order MDPs Scott Sanner
- Theorem Proving Scott Sanner, Guest Lecture
- Future Directions for First-Order Decision-Theoretic Planning
- Refutation-Complete Binary Decision Diagrams Scott P. Sanner
- Scott P. Sanner Statistical Machine Learning and AI Groups Home: +61 (2) 6257-0772
- International Probabilistic Planning Competition 2011 Rules Agreement
- ICAPS 2010 Tutorial Scott Sanner
- The Joy of Description Logics
- CS224N, Spring Final Project: Scott Sanner Natural Language Processing HMM-Based Text Extractor ssanner@cs.stanford.edu
- Automated Text Classification in the DMOZ Lachlan Henderson
- An Ordered Theory Resolution Calculus for Hybrid Reasoning in Firstorder Extensions of Description Logic
- FIRSTORDER DECISIONTHEORETIC PLANNING IN STRUCTURED RELATIONAL ENVIRONMENTS
- In Proceedings of the Seventeenth International Conference on Machine Learning (ICML2000), pages 823830, Stanford, California, July 2000
- AutomatedAutomated Theorem ProvingTheorem Proving
- Future Directions for First-Order Decision-Theoretic Planning
- Relational and First-Order Decision-Theoretic Planning: Deterministic Planning Supplement
- Practical Linear Valueapproximation Techniques for Firstorder MDPs Scott Sanner
- Online Feature Discovery in Relational Reinforcement Learning Scott Sanner SSANNER@CS.TORONTO.EDU
- Instructions for MIE457F Project 2: A Data Mining System
- AutomatedAutomated Theorem ProvingTheorem Proving
- CS224N, Spring Final Project: Scott Sanner Natural Language Processing HMMBased Text Extractor ssanner@cs.stanford.edu
- Probabilistic Latent Maximal Marginal Relevance Shengbo Guo, Scott Sanner (Statistical Machine Learning Group, NICTA)
- Approximate Linear Programming for Firstorder MDPs Scott Sanner
- In Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), pages 823-830, Stanford, California, July 2000
- Sentiment Mining for Natural Language Documents Alexander O'Neill
- Future Directions for First-Order Decision-
- FIRST-ORDER DECISION-THEORETIC PLANNING IN STRUCTURED RELATIONAL ENVIRONMENTS
- Reinforcement Learning with the Use of Costly Features Robby Goetschalckx2, Scott Sanner1 and Kurt Driessens2
- Instructions for MIE457F Project 1: An Information Retrieval System
- Towards practical taxonomic classification for description logics on the Semantic Web
- Relational Dynamic Influence Diagram Language (RDDL): Language Description
- Introduction Reinforcement Learning
- Planning in Continuous Domains with MDPs Neil Bacon, Supervisor: Scott Sanner
- A#ne Algebraic Decision Diagrams (AADDs) and their Application to Structured Probabilistic Inference
- RESEARCH STATEMENT The Intelligence Layer: Robust Decision-making via AI and Machine Learning
- Efficient Solutions to Factored MDPs with Imprecise Transition Probabilities Karina Valdivia Delgado
- Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries
- Description Sanner (ssanner@cs.toronto.edu)
- ICAPS IPPC 2011 Amazon EC2 Setup Instructions, v3-9-11
- Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
- Instructions for MIE457F Project 3: Semantic Web Modelling and Inference
- Online Feature Discovery in Relational Reinforcement Learning Scott Sanner SSANNER@CS.TORONTO.EDU
- RefutationComplete Binary Decision Diagrams Scott P. Sanner
- Future DirectionsFuture Directions for First-Order Decision-for First-Order Decision-
- Towards practical taxonomic classification for description logics on the Semantic Web
- Reinforcement Learning with the Use of Costly Features Robby Goetschalckx1
- POND-Hindsight: Applying Hindsight Optimization to POMDPs Alan Olsen and Daniel Bryce
- Symbolic Dynamic Programming for Discrete and Continuous State MDPs Scott Sanner
- Sparse Kernel-SARSA() with an Eligibility Trace Matthew Robards1,2
- ICAPS 2011 Tutorial Scott Sanner
- Scott Sanner IPPC Results Presentation
- ICAPS 2011 Tutorial Scott Sanner
- Diverse Retrieval via Greedy Optimization of Expected 1-call@k in a Latent Subtopic Relevance Model
- Bidirectional Online Probabilistic Planning Aswin Nadamuni Raghavan and Saket Joshi and Alan Fern and Prasad Tadepalli
- A FLEXIBLE MODEL FOR TRAFFIC SIMULATION AND
- Automatic Parody Detection in Sentiment December 16, 2010
