Friday
Christos Papadimitriou
Saso Dzeroski
Sebastian Thrun
Morning Tea
Morning Tea
ENSEMBLE
Is Combining Classifiers Better than Selecting the Best One?
Saso Dzeroski
Bernard Zenko
HRL
Discovering Hierarchy in Reinforcement Learning with HEXQ
Bernhard Hengst
TEXT
Learning word normalization using word suffix and context from unlabeled data
Dunja Mladenic
BC/DISC
Reinforcement Learning and Shaping: Encouraging Intended Behaviors
Adam Laud
Gerald DeJong
SVM
Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry
Dennis DeCoste
COLT
Sufficient Dimensionality Reduction - A novel Analysis Principle
Amir Globerson
Naftali Tishby
ENSEMBLE
Incorporating Prior Knowledge into Boosting
Robert Schapire
Marie Rochery
Mazin Rahim
Narendra Gupta
FEATURE
Refining the Wrapper Approach - Smoothed Error Estimates for Feature Selection
Loo-Nin Teow
Hwee Tou Ng
Haifeng Liu
Eric Yap
ILP
Feature Subset Selection and Inductive Logic Programming
Erick Alphonse
Stan Matwin
ENSEMBLE
A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance
Michael Goebel
Pat Riddle
Mike Barley
HRL
Automatic Creation of Useful Macro-Actions in Reinforcement Learning
Marc Pickett
Andrew Barto
TEXT
A New Statistical Approach on Personal Name Extraction
Zheng Chen
Feng Zhang
BC/DISC
Separating Skills from Preference: Using Learning to Program by Reward
Daniel Shapiro
Pat Langley
Multi-Instance Kernels
Thomas Gaertner
Peter Flach
Adam Kowalczyk
Alex Smola
Robert Williamson
COLT
Combining Training Set and Test Set Bounds
John Langford
ENSEMBLE
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
Robert Schapire
Peter Stone
David McAllester
Michael Littman
Janos Csirik
FEATURE
Feature Selection with Active Learning
Huan Liu
Hiroshi Motoda
Lei Yu
ILP
Inductive Logic Programming out of Phase Transition: A bottom-up constraint-based approach
Jacques Ales Bianchetti
Celine Rouveirol
Michele Sebag
ENSEMBLE
Cranking: An Ensemble Method for Combining Rankers using Conditional Probability Models on Permutations
Guy Lebanon
John Lafferty
HRL
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies
Malcolm Ryan
TEXT
IEMS - The Intelligent Email Sorter
Elisabeth Crawford
Judy Kay
Eric McCreath
BC/DISC
Learning to Fly by Controlling Dynamic Instabilities
David Stirling
SVM
Kernels for Semi-Structured Data
Hisashi Kashima
Teruo Koyanagi
COLT
Learning k-Reversible Context-Free Grammars from Positive Structural Examples
Tim Oates
Devina Desai
Vinay Bhat
ENSEMBLE
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness
Alexander K. Seewald
FEATURE
Randomized Variable Elimination
David Stracuzzi
Paul Utgoff
ILP
Graph-Based Relational Concept Learning
Jesus Gonzalez
Lawrence Holder
Diane Cook
ENSEMBLE
Active + Semi-supervised Learning = Robust Multi-View Learning
Ion Muslea
Steven Minton
Craig Knoblock
HRL
Model-based Hierarchical Average-reward Reinforcement Learning
Sandeep Seri
Prasad Tadepalli
TEXT
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
Rayid Ghani
BC/DISC
Qualitative reverse engineering
Dorian Suc
Ivan Bratko
SVM
A Fast Dual Algorithm for Kernel Logistic Regression
Sathiya Keerthi
Kaibo Duan
Shirish Shevade
Aun Poo
COLT
On generalization bounds, projection profile, and margin distribution
Ashutosh Garg
Sariel Har-Peled
Dan Roth
ENSEMBLE
Towards "Large Margin" Speech Recognizers by Boosting and Discriminative Training
Carsten Meyer
Peter Beyerlein
FEATURE
Discriminative Feature Selection via Multiclass Variable Memory Markov Model
Noam Slonim
Gill Bejerano
Shai Fine
Naftali Tishby
RULE
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain
Dragan Gamberger
Nada Lavrac
Lunch
Lunch
Lunch
TREES
Fast Minimum Training Error Discretization
Tapio Elomaa
Juhu Rousu
HRL
Hierarchically Optimal Average Reward Reinforcement Learning
Mohammad Ghavamzadeh
Sridhar Mahadevan
TEXT
Partially Supervised Classification of Text Documents
Bing Liu
Wee Sun Lee
Philip S. Yu
Xiaoli Li
BC/DISC
Inducing Process Models from Continuous Data
Pat Langley
Javier Sanchez
Ljupco Todorovski
Saso Dzeroski
COST
An Alternate Objective Function for Markovian Fields
Sham Kakade
Yee Whye Teh
Sam Roweis
BAYES
Non-Disjoint Discretization for Naive-Bayes Classifiers
Ying Yang
Geoffrey I. Webb
SVM
Statistic Behavior and Consistency of Support Vector Machines, Boosting, and Beyond
Tong Zhang
BAYES
Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo
Shien-Shin Tham
Arnaud Doucet
Ramamohanarao Kotagiri
FEATURE
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
David Jensen
Jennifer Neville
TREES
Learning Decision Trees Using the Area Under the ROC Curve
Cesar Ferri
Peter Flach
Jose Hernandez-Orallo
RL
Action Refinement in Reinforcement Learning by Probability Smoothing
Thomas Dietterich
Didac Busquets
Ramon Lopez de Mantaras
Carles Sierra
TEXT
Syllables and other String Kernel Extensions
Craig Saunders
Hauke Tschach
John Shawe-Taylor
RL
Integrating Experimentation and Guidance in Relational Reinforcement Learning
Kurt Driessens
Saso Dzeroski
COST
Issues in Classifier Evaluation using Optimal Cost Curves
Kai Ming Ting
BAYES
Numerical Minimum Message Length Inference of Univariate Polynomials
Leigh Fitzgibbon
David Dowe
Lloyd Allison
SVM
The Perceptron Algorithm with Uneven Margins
Yaoyong Li
Hugo Zaragoza
Ralf Herbrich
John Shawe-Taylor
Jaz Kandola
BAYES
Modeling for Optimal Probability Prediction
Yong Wang
Ian H. Witten
RL
Algorithm-Directed Exploration for Model-Based Reinforcement Learning
Carlos Guestrin
Relu Patrascu
Dale Schuurmans
TREES
An Analysis of Functional Trees
Joao Gama
BC/DISC
Learning Spatial and Temporal Correlation for Navigation in a 2-Dimensional Continuous World
Anand Panangadan
Michael Dyer
TEXT
A Boosted Maximum Entropy Model for Learning Text Chunking
Seong-Bae Park
Byoung-Tak Zhang
RL
Approximately Optimal Approximate Reinforcement Learning
Sham Kakade
John Langford
COST
Pruning Improves Heuristic Search for Cost-Sensitive Learning
Valentina Bayer Zubek
Thomas Dietterich
BAYES
Learning to Share Distributed Probabilistic Beliefs
Christopher Leckie
Ramamohanarao Kotagiri
SVM
Learning the Kernel Matrix with Semi-Definite Programming
Gert Lanckriet
Nello Christianini
Peter Bartlett
Laurent El Ghaoui
Michael Jordan
BAYES
Representational Upper Bounds of Bayesian Networks
Huajie Zhang
Charles Ling
RL
A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation
Artur Merke
Ralf Schoknecht
Afternoon Tea
Afternoon Tea
Afternoon Tea
TREES
Classification Value Grouping
Colin Ho
RL
Scalable Internal-State Policy-Gradient Methods for POMDPs
Douglas Aberdeen
Jonathan Baxter
TEXT
Using Unlabelled Data for Text Classification through Addition of Cluster Parameters
Bhavani Raskutti
Adam Kowalczyk
Herman Ferra
RL
Competitive Analysis of the Explore/Exploit Tradeoff
John Langford
Martin Zinkevich
Sham Kakade
UNSUP
Semi-supervised Clustering by Seeding
Sugato Basu
Arindam Banerjee
Raymond Mooney
BAYES
Markov Chain Monte Carlo Sampling using Direct Search Optimization
Malcolm Strens
Mark Bernhardt
Nicholas Everett
SVM
Diffusion Kernels on Graphs and Other Discrete Structures
Risi Kondor
John Lafferty
RULE
Learning Decision Rules by Randomized Iterative Local Search
Michael Chisholm
Prasad Tadepalli
RL
Stock Trading System Using Reinforcement Learning with Cooperative Agents
Jangmin O
Jae Won Lee
Byoung-Tak Zhang
TREES
Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction
Fumio Takechi
Einoshin Suzuki
RL
An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes
Blai Bonet
UNSUP
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering
Dan Klein
Sepandar Kamvar
Christopher Manning
RL
Investigating the Maximum Likelihood Alternative to TD(lambda)
Fletcher Lu
Relu Patrascu
Dale Schuurmans
UNSUP
Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data
Joseph Bockhorst
Mark Craven
BAYES
Exact model averaging with naive Bayesian classifiers
Denver Dash
Gregory Cooper
RL
Learning from Scarce Experience
Leonid Peshkin
Christian Shelton
RULE
Transformation-Based Regression
Bjorn Bringmann
Stefan Kramer
Friedrich Neubarth
Hannes Pirker
Gerhard Widmer
MULT
Content-Based Image Retrieval Using Multiple-Instance Learning
Qi Zhang
Wei Yu
Sally Goldman
Jason Fritts
TREES
Adaptive View Validation: A First Step Towards Automatic View Detection
Ion Muslea
Steven Minton
Craig Knoblock
RL
On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains
Theodore Perkins
Mark Pendrith
RULE
Mining Both Positive and Negative Association Rules
Xindong Wu
Shichao Zhang
RL
Coordinated Reinforcement Learning
Carlos Guestrin
Michail Lagoudakis
Ronald Parr
UNSUP
Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach
Sepandar Kamvar
Dan Klein
Christopher Manning
BAYES
MMIHMM: Maximum Mutual Information Hidden Markov Models
Nuria Oliver
Ashutosh Garg