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ORCID19th COLT 2006: Pittsburgh, PA, USA
- Gábor Lugosi, Hans Ulrich Simon
:
Learning Theory, 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings. Lecture Notes in Computer Science 4005, Springer 2006, ISBN 3-540-35294-5
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 D. 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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