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ORCID20th COLT 2007: San Diego, CA, USA
- Nader H. Bshouty, Claudio Gentile:
Learning Theory, 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings. Lecture Notes in Computer Science 4539, Springer 2007, ISBN 978-3-540-72925-9
Invited Presentations
- Dana Ron:
Property Testing: A Learning Theory Perspective. 1-2 - Santosh S. Vempala:
Spectral Algorithms for Learning and Clustering. 3-4
Unsupervised, Semisupervised and Active Learning I
- Rui M. Castro, Robert D. Nowak:
Minimax Bounds for Active Learning. 5-19 - Shai Ben-David, Dávid Pál, Hans Ulrich Simon
:
Stability of k -Means Clustering. 20-34 - Maria-Florina Balcan, Andrei Z. Broder, Tong Zhang:
Margin Based Active Learning. 35-50
Unsupervised, Semisupervised and Active Learning II
- Dana Angluin, James Aspnes, Jiang Chen, Lev Reyzin:
Learning Large-Alphabet and Analog Circuits with Value Injection Queries. 51-65 - Steve Hanneke:
Teaching Dimension and the Complexity of Active Learning. 66-81 - Sham M. Kakade, Dean P. Foster:
Multi-view Regression Via Canonical Correlation Analysis. 82-96
Statistical Learning Theory
- Arnak S. Dalalyan, Alexandre B. Tsybakov:
Aggregation by Exponential Weighting and Sharp Oracle Inequalities. 97-111 - Gilles Blanchard, François Fleuret:
Occam's Hammer. 112-126 - Sylvain Arlot, Gilles Blanchard, Étienne Roquain:
Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector. 127-141 - Guillaume Lecué:
Suboptimality of Penalized Empirical Risk Minimization in Classification. 142-156 - Ran El-Yaniv, Dmitry Pechyony:
Transductive Rademacher Complexity and Its Applications. 157-171
Inductive Inference
- John Case, Samuel E. Moelius:
U-Shaped, Iterative, and Iterative-with-Counter Learning. 172-186 - Oliver Schulte, Wei Luo, Russell Greiner:
Mind Change Optimal Learning of Bayes Net Structure. 187-202 - Lorenzo Carlucci, John Case, Sanjay Jain:
Learning Correction Grammars. 203-217 - Sanjay Jain, Frank Stephan:
Mitotic Classes. 218-232
Online and Reinforcement Learning I
- Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman:
Regret to the Best vs. Regret to the Average. 233-247 - Gábor Lugosi, Shie Mannor, Gilles Stoltz:
Strategies for Prediction Under Imperfect Monitoring. 248-262 - Ambuj Tewari, Peter L. Bartlett:
Bounded Parameter Markov Decision Processes with Average Reward Criterion. 263-277
Online and Reinforcement Learning II
- Sanjoy Dasgupta, Daniel J. Hsu:
On-Line Estimation with the Multivariate Gaussian Distribution. 278-292 - Yuri Kalnishkan, Vladimir Vovk, Michael V. Vyugin:
Generalised Entropy and Asymptotic Complexities of Languages. 293-307 - Francisco S. Melo, M. Isabel Ribeiro:
Q -Learning with Linear Function Approximation. 308-322
Regularized Learning, Kernel Methods, SVM
- Nathan Srebro:
How Good Is a Kernel When Used as a Similarity Measure? 323-335 - Nikolas List, Don R. Hush, Clint Scovel, Ingo Steinwart:
Gaps in Support Vector Optimization. 336-348 - Corinna Cortes, Leonid Kontorovich, Mehryar Mohri:
Learning Languages with Rational Kernels. 349-364 - Nikolas List:
Generalized SMO-Style Decomposition Algorithms. 365-377
Learning Algorithms and Limitations on Learning
- Adam Tauman Kalai:
Learning Nested Halfspaces and Uphill Decision Trees. 378-392 - Alexandre Belloni, Robert M. Freund, Santosh S. Vempala:
An Efficient Re-scaled Perceptron Algorithm for Conic Systems. 393-408 - Adam R. Klivans, Alexander A. Sherstov:
A Lower Bound for Agnostically Learning Disjunctions. 409-423 - Sudipto Guha, Piotr Indyk, Andrew McGregor:
Sketching Information Divergences. 424-438
Online and Reinforcement Learning III
- Vladimir Vovk:
Competing with Stationary Prediction Strategies. 439-453 - Peter Auer, Ronald Ortner, Csaba Szepesvári:
Improved Rates for the Stochastic Continuum-Armed Bandit Problem. 454-468 - David P. Helmbold, Manfred K. Warmuth:
Learning Permutations with Exponential Weights. 469-483
Online and Reinforcement Learning IV
- Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin:
Multitask Learning with Expert Advice. 484-498 - Elad Hazan, Nimrod Megiddo:
Online Learning with Prior Knowledge. 499-513
Dimensionality Reduction
- Ping Li, Trevor Hastie, Kenneth Ward Church:
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections. 514-529 - Florentina Bunea, Alexandre B. Tsybakov, Marten H. Wegkamp:
Sparse Density Estimation with l1 Penalties. 530-543 - Saharon Rosset, Grzegorz Swirszcz, Nathan Srebro, Ji Zhu:
l1 Regularization in Infinite Dimensional Feature Spaces. 544-558 - Sivan Sabato, Shai Shalev-Shwartz:
Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking. 559-573
Other Approaches
- Julian Lorenz, Martin Marciniszyn, Angelika Steger:
Observational Learning in Random Networks. 574-588 - Marcus Hutter:
The Loss Rank Principle for Model Selection. 589-603 - Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin:
Robust Reductions from Ranking to Classification. 604-619
Open Problems
- Liwei Wang, Jufu Feng:
Rademacher Margin Complexity. 620-621 - Avrim Blum, Maria-Florina Balcan:
Open Problems in Efficient Semi-supervised PAC Learning. 622-624 - Pallika H. Kanani, Andrew McCallum:
Resource-Bounded Information Gathering for Correlation Clustering. 625-627 - Nathan Srebro:
Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? 628-629 - Manfred K. Warmuth:
When Is There a Free Matrix Lunch? 630-632
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