Wednesday

Tutorial

9:00 am - 10:30 am	Morning Sessions
	T3: Online Learning of Real-World Problems


2:00 pm - 3:30 pm
T7: Practical Statistical Relational Learning	Austin Auditorium
	Pedro Domingos


Thursday

10:40 am - 11:30 am	ILP Invited Panel	Austin Auditorium
	Structured Machine Learning: The Next 10 Years

2:00 pm - 3:40 pm -- Session 7 - MULTI-TASK AND TRANSFER LEARNING C&E Hall

  Robust Multi-Task Learning with $t$-Processes
  The Matrix Stick-Breaking Process for Flexible Multi-Task Learning
* Self-taught Learning: Transfer Learning from Unlabeled Data
  Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks


4:10 pm - 5:50 pm Session 12 - RELATIONAL LEARNING II	Austin Auditorium

  Parameter Learning for Relational Bayesian Networks
  Relational Clustering by Symmetric Convex Coding
* Bottom-Up Learning of Markov Logic Network Structure
  Fast and Effective Kernels for Relational Learning from Texts

poster session 7pm


+Friday

10:30 am - 12:35 pm	Sessions 13-16
Session 15: INFERENCE, PROBABILISTIC MODELS, AND RANDOM FIELDS - C&E Hall

# 2 What Is Decreased by the Max-sum Arc Consistency Algorithm?

Session 16 - LARGE-SCALE OPTIMIZATION	- Ag Production

#1 Scalable Training of L1-regularized Log-linear Models
#3 Trust Region Newton Methods for Large-Scale Logistic Regression

2:00 pm - 3:40 pm

Session 18 - MULTIPLE-INSTANCE AND SEQUENTIAL LEARNING Ag Production
#3 CarpeDiem: an Algorithm for the Fast Evaluation of SSL Classifiers

Session 20 - CLASSIFICATION II	Ag Leaders
#1 Uncovering Shared Structures in Multiclass Classification


4:10 pm - 5:50 pm
Session 22 - DISCRIMINANT ANALYSIS -- 	Ag Leaders
#3 Least Squares Linear Discriminant Analysis


+Saturday

9:00 am - 11:05 am

Session 25 - CLASSIFICATION III	Ag Leaders
#1 Sparse Probabilistic Classifiers

Session 26 - STRUCTURED PREDICTION	Austin Auditorium

#1 Exponentiated Gradient Algorithms for Log-Linear Structured Prediction
#2 Comparisons of sequence labelling algorithms and extensions
#3  Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields

Important papers:
CarpeDiem: an Algorithm for the Fast Evaluation of SSL Classifiers (Friday/Th)
Least Squares Linear Discriminant Analysis (Friday/Th)
Trust Region Newton Methods for Large-Scale Logistic Regression (Friday/Th)
Exponentiated Gradient Algorithms for Log-Linear Structured Prediction (Sat)
Comparisons of sequence labelling algorithms and extensions (Sat)
Scalable Training of L1-regularized Log-linear Models (Friday/Th)
What Is Decreased by the Max-sum Arc Consistency Algorithm? (Friday)
Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields (Sat)

Bottom-Up Learning of Markov Logic Network Structure

maybe:
Conditional Random Fields for Multi-agent Reinforcement Learning
Hierarchical Maximum Entropy Density Estimation
Bottom-Up Learning of Markov Logic Network Structure
Statistical Predicate Invention
Three New Graphical Models for Statistical Language Modelling
On Learning Linear Ranking Functions for Beam Search
Dynamic Hierarchical Markov Random Fields and their Application to Web Data Extraction

Absolute musts: CarpeDiem, Comparisons of sequence labelling,