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ORCID18th COLT 2005: Bertinoro, Italy
- Peter Auer, Ron Meir:
Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings. Lecture Notes in Computer Science 3559, Springer 2005, ISBN 3-540-26556-2
Learning to Rank
- Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis:
Ranking and Scoring Using Empirical Risk Minimization. 1-15 - Shivani Agarwal, Dan Roth:
Learnability of Bipartite Ranking Functions. 16-31 - Shivani Agarwal, Partha Niyogi:
Stability and Generalization of Bipartite Ranking Algorithms. 32-47 - Koby Crammer, Yoram Singer:
Loss Bounds for Online Category Ranking. 48-62
Boosting
- Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire:
Margin-Based Ranking Meets Boosting in the Middle. 63-78 - Philip M. Long, Rocco A. Servedio:
Martingale Boosting. 79-94 - Boaz Leskes:
The Value of Agreement, a New Boosting Algorithm. 95-110
Unlabeled Data, Multiclass Classification
- Maria-Florina Balcan, Avrim Blum:
A PAC-Style Model for Learning from Labeled and Unlabeled Data. 111-126 - Matti Kääriäinen:
Generalization Error Bounds Using Unlabeled Data. 127-142 - Ambuj Tewari, Peter L. Bartlett
:
On the Consistency of Multiclass Classification Methods. 143-157 - John Langford, Alina Beygelzimer:
Sensitive Error Correcting Output Codes. 158-172
Online Learning I
- Tong Zhang:
Data Dependent Concentration Bounds for Sequential Prediction Algorithms. 173-187 - Yuri Kalnishkan, Michael V. Vyugin:
The Weak Aggregating Algorithm and Weak Mixability. 188-203 - András György, Tamás Linder, Gábor Lugosi:
Tracking the Best of Many Experts. 204-216 - Nicolò Cesa-Bianchi, Yishay Mansour, Gilles Stoltz:
Improved Second-Order Bounds for Prediction with Expert Advice. 217-232
Online Learning II
- Baruch Awerbuch, Robert D. Kleinberg:
Competitive Collaborative Learning. 233-248 - Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni:
Analysis of Perceptron-Based Active Learning. 249-263 - Shai Shalev-Shwartz, Yoram Singer:
A New Perspective on an Old Perceptron Algorithm. 264-278
Support Vector Machines
- Ingo Steinwart, Clint Scovel:
Fast Rates for Support Vector Machines. 279-294 - Vladimir Koltchinskii, Olexandra Beznosova:
Exponential Convergence Rates in Classification. 295-307 - Nikolas List, Hans Ulrich Simon:
General Polynomial Time Decomposition Algorithms. 308-322
Kernels and Embeddings
- Petros Drineas, Michael W. Mahoney:
Approximating a Gram Matrix for Improved Kernel-Based Learning. 323-337 - Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil:
Learning Convex Combinations of Continuously Parameterized Basic Kernels. 338-352 - Shahar Mendelson:
On the Limitations of Embedding Methods. 353-365 - Manfred K. Warmuth, S. V. N. Vishwanathan:
Leaving the Span. 366-381
Inductive Inference
- Lorenzo Carlucci, Sanjay Jain, Efim B. Kinber, Frank Stephan:
Variations on U-Shaped Learning. 382-397 - Wei Luo, Oliver Schulte:
Mind Change Efficient Learning. 398-412 - Eric Martin, Arun Sharma:
On a Syntactic Characterization of Classification with a Mind Change Bound. 413-428
Unsupervised Learning
- Shahar Mendelson, Alain Pajor:
Ellipsoid Approximation Using Random Vectors. 429-443 - Ravindran Kannan, Hadi Salmasian, Santosh S. Vempala:
The Spectral Method for General Mixture Models. 444-457 - Dimitris Achlioptas, Frank McSherry:
On Spectral Learning of Mixtures of Distributions. 458-469 - Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg:
From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians. 470-485 - Mikhail Belkin, Partha Niyogi:
Towards a Theoretical Foundation for Laplacian-Based Manifold Methods. 486-500
Generalization Bounds
- Polina Golland, Feng Liang, Sayan Mukherjee, Dmitry Panchenko:
Permutation Tests for Classification. 501-515 - Tong Zhang:
Localized Upper and Lower Bounds for Some Estimation Problems. 516-530 - András Antos:
Improved Minimax Bounds on the Test and Training Distortion of Empirically Designed Vector Quantizers. 531-544 - Nathan Srebro, Adi Shraibman:
Rank, Trace-Norm and Max-Norm. 545-560
Query Learning, Attribute Efficiency, Compression Schemes
- Dana Angluin, Jiang Chen:
Learning a Hidden Hypergraph. 561-575 - Vitaly Feldman:
On Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions. 576-590 - Dima Kuzmin, Manfred K. Warmuth:
Unlabeled Compression Schemes for Maximum Classes, . 591-605
Economics and Game Theory
- Sham M. Kakade, Michael J. Kearns:
Trading in Markovian Price Models. 606-620 - Avrim Blum, Yishay Mansour:
From External to Internal Regret. 621-636
Separation Results for Learning Models
- Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan:
Separating Models of Learning from Correlated and Uncorrelated Data. 637-651 - Peter Grünwald, Steven de Rooij:
Asymptotic Log-Loss of Prequential Maximum Likelihood Codes. 652-667 - Frank J. Balbach:
Teaching Classes with High Teaching Dimension Using Few Examples. 668-683
Open Problems
- Dima Kuzmin, Manfred K. Warmuth:
Optimum Follow the Leader Algorithm. 684-686 - John Langford:
The Cross Validation Problem. 687-688 - Wei Luo:
Compute Inclusion Depth of a Pattern. 689-690
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