19. COLT 2006: Pittsburgh, PA, USA

Invited Presentations

• Luc Devroye:
  Random Multivariate Search Trees. 1
  
• György Turán:
  On Learning and Logic. 2-3
  
• Vladimir Vovk:
  Predictions as Statements and Decisions. 4
  

Clustering, Un-, and Semisupervised Learning

• Shai Ben-David, Ulrike von Luxburg, Dávid Pál:
  A Sober Look at Clustering Stability. 5-19
  
• Jon Feldman, Rocco A. Servedio, Ryan O'Donnell:
  PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption. 20-34
  
• Ran El-Yaniv, Dmitry Pechyony:
  Stable Transductive Learning. 35-49
  
• Matthias Hein:
  Uniform Convergence of Adaptive Graph-Based Regularization. 50-64
  

Statistical Learning Theory

• Andreas Maurer:
  The Rademacher Complexity of Linear Transformation Classes. 65-78
  
• Ingo Steinwart, Don R. Hush, Clint Scovel:
  Function Classes That Approximate the Bayes Risk. 79-93
  
• Magalie Fromont, Christine Tuleau:
  Functional Classification with Margin Conditions. 94-108
  
• Xing Sun, Andrew B. Nobel:
  Significance and Recovery of Block Structures in Binary Matrices with Noise. 109-122
  

Regularized Learning and Kernel Methods

• Miroslav Dudík, Robert E. Schapire:
  Maximum Entropy Distribution Estimation with Generalized Regularization. 123-138
  
• Yasemin Altun, Alexander J. Smola:
  Unifying Divergence Minimization and Statistical Inference Via Convex Duality. 139-153
  
• Ha Quang Minh, Partha Niyogi, Yuan Yao:
  Mercer's Theorem, Feature Maps, and Smoothing. 154-168
  
• Nathan Srebro, Shai Ben-David:
  Learning Bounds for Support Vector Machines with Learned Kernels. 169-183
  

Query Learning and Teaching

• Laurence Bisht, Nader H. Bshouty, Hanna Mazzawi:
  On Optimal Learning Algorithms for Multiplicity Automata. 184-198
  
• Nader H. Bshouty, Hanna Mazzawi:
  Exact Learning Composed Classes with a Small Number of Mistakes. 199-213
  
• Homin K. Lee, Rocco A. Servedio, Andrew Wan:
  DNF Are Teachable in the Average Case. 214-228
  
• Frank J. Balbach, Thomas Zeugmann:
  Teaching Randomized Learners. 229-243
  

Inductive Inference

• Lorenzo Carlucci, John Case, Sanjay Jain, Frank Stephan:
  Memory-Limited U-Shaped Learning. 244-258
  
• Sanjay Jain, Efim B. Kinber:
  On Learning Languages from Positive Data and a Limited Number of Short Counterexamples. 259-273
  
• François Denis, Yann Esposito, Amaury Habrard:
  Learning Rational Stochastic Languages. 274-288
  
• Mikko Koivisto:
  Parent Assignment Is Hard for the MDL, AIC, and NML Costs. 289-303
  

Learning Algorithms and Limitations on Learning

• Jeffrey C. Jackson:
  Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention. 304-318
  
• Philip M. Long, Rocco A. Servedio:
  Discriminative Learning Can Succeed Where Generative Learning Fails. 319-334
  
• Adam R. Klivans, Alexander A. Sherstov:
  Improved Lower Bounds for Learning Intersections of Halfspaces. 335-349
  
• Lance Fortnow, Adam R. Klivans:
  Efficient Learning Algorithms Yield Circuit Lower Bounds. 350-363
  

Online Aggregation

• Guillaume Lecué:
  Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition. 364-378
  
• Florentina Bunea, Alexandre B. Tsybakov, Marten H. Wegkamp:
  Aggregation and Sparsity Via l₁ Penalized Least Squares. 379-391
  
• Jean-Yves Audibert:
  A Randomized Online Learning Algorithm for Better Variance Control. 392-407
  

Online Prediction and Reinforcement Learning I

• Shie Mannor, Nahum Shimkin:
  Online Learning with Variable Stage Duration. 408-422
  
• Shai Shalev-Shwartz, Yoram Singer:
  Online Learning Meets Optimization in the Dual. 423-437
  
• Koby Crammer:
  Online Tracking of Linear Subspaces. 438-452
  
• Ofer Dekel, Philip M. Long, Yoram Singer:
  Online Multitask Learning. 453-467
  

Online Prediction and Reinforcement Learning II

• András György, Tamás Linder, György Ottucsák:
  The Shortest Path Problem Under Partial Monitoring. 468-482
  
• Nicolò Cesa-Bianchi, Claudio Gentile:
  Tracking the Best Hyperplane with a Simple Budget Perceptron. 483-498
  
• Elad Hazan, Adam Kalai, Satyen Kale, Amit Agarwal:
  Logarithmic Regret Algorithms for Online Convex Optimization. 499-513
  
• Manfred K. Warmuth, Dima Kuzmin:
  Online Variance Minimization. 514-528
  

Online Prediction and Reinforcement Learning III

• Shie Mannor, John N. Tsitsiklis:
  Online Learning with Constraints. 529-543
  
• Jacob Abernethy, John Langford, Manfred K. Warmuth:
  Continuous Experts and the Binning Algorithm. 544-558
  
• Vladimir Vovk:
  Competing with Wild Prediction Rules. 559-573
  
• András Antos, Csaba Szepesvári, Rémi Munos:
  Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path. 574-588
  

Other Approaches

• Cynthia Rudin:
  Ranking with a P-Norm Push. 589-604
  
• David Cossock, Tong Zhang:
  Subset Ranking Using Regression. 605-619
  
• Shai Fine, Yishay Mansour:
  Active Sampling for Multiple Output Identification. 620-634
  
• Ping Li, Trevor Hastie, Kenneth Ward Church:
  Improving Random Projections Using Marginal Information. 635-649
  

Open Problems

• Claire Monteleoni:
  Efficient Algorithms for General Active Learning. 650-652
  
• Manfred K. Warmuth:
  Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints. 653-654
  

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