List of accepted papers
All papers are available online.
The papers are presented without any special order.
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Ranking with a P-Norm Push
Cynthia Rudin -
Memory-Limited U-Shaped Learning
Lorenzo Carlucci, John Case, Sanjay Jain, Frank Stephan -
On Learning Languages from Positive Data and a Limited Number of Short Counterexamples
Sanjay Jain, Efim Kinber -
The Rademacher Complexity of Linear Transformation Classes
Andreas Maurer -
On Optimal Learning Algorithms for Multiplicity Automata
Laurence Bisht, Nader Bshouty, Hanna Mazzawi -
Optimal oracle inequality for aggregation of classifiers under low noise condition
Guillaume Lecué -
Online Learning with Variable Stage Duration
Shie Mannor, Nahum Shimkin -
Exact Learning Composed Classes With a Small Number of Mistakes
Nader Bshouty, Hanna Mazzawi -
Parent Assignment Is Hard for the MDL, AIC, and NML Costs
Mikko Koivisto -
Active Sampling for Multiple Output Identification
Shai Fine, Yishay Mansour -
Discriminative Learning can Succeed where Generative Learning Fails
Phil Long, Rocco Servedio -
Online Learning meets Optimization in the Dual
Shai Shalev-Shwartz, Yoram Singer -
DNF are Teachable in the Average Case
Homin Lee, Rocco Servedio, Andrew Wan -
Improved Lower Bounds for Learning Intersections of Halfspaces
Adam Klivans, Alexander Sherstov -
A randomized online learning algorithm for better variance control
Jean-Yves Audibert -
Online Tracking of Linear Subspaces
Koby Crammer -
Learning Bounds for Support Vector Machines with Learned Kernels
Nathan Srebro, Shai Ben-David -
Efficient Learning Algorithms Yield Circuit Lower Bounds
Lance Fortnow, Adam Klivans -
Online Multitask Learning
Ofer Dekel, Phil Long, Yoram Singer -
Function classes that approximate the Bayes risk
Ingo Steinwart, Don Hush, Clint Scovel -
Tracking the Best Hyperplane with a Simple Budget Perceptron
Nicolò Cesa-Bianchi, Claudio Gentile -
Subset Ranking using Regression
David Cossock, Tong Zhang -
Logarithmic Regret Algorithms for Online Convex Optimization
Elad Hazan, Adam Kalai, Satyen Kale, Amit Agarwal -
Significance and Recovery of Block Structures in Binary Matrices with Noise
Xing Sun, Andrew Nobel -
Unifying Divergence Minimization and Statistical Inference via Convex Duality
Yasemin Altun, Alex Smola -
Online Variance Minimization
Manfred K. Warmuth, Dima Kuzmin -
Teaching Randomized Learners
Frank Balbach, Thomas Zeugmann -
Functional classification with margin conditions
Magalie Fromont, Christine Tuleau -
Learning rational stochastic languages
François Denis, Yann Esposito, Amaury Habrard -
The Shortest Path Problem Under Partial Monitoring
Andras György, Tamás Linder, György Ottucsák -
Online Learning with Constraints
Shie Mannor, John Tsitsiklis -
Uniform convergence of adaptive graph-based regularization
Matthias Hein -
Improving Random Projections Using Marginal Information
Ping Li, Trevor J. Hastie, Kenneth W. Church -
Competing with wild prediction rules
Vladimir Vovk -
PAC Learning Mixtures of Gaussians with No Separation Assumption
Jon Feldman, Rocco Servedio, Ryan O'Donnell -
Stable Transductive Learning
Ran El-Yaniv, Dmitry Pechyony -
Mercer's Theorem, Feature Maps, and Smoothing
Ha Quang Minh, Partha Niyogi, Yuan Yao -
Aggregation and Sparsity via l_1 Penalized Least Squares
Florentina Bunea, Alexandre Tsybakov, Marten Wegkamp -
Uniform-Distribution Learnability of Noisy Linear Threshold
Functions with Restricted Focus of Attention
Jeffrey C. Jackson -
Maximum Entropy Distribution Estimation with Generalized Regularization
Miroslav Dudík, Robert E. Schapire -
A Sober Look at Clustering Stability
Shai Ben-David, Ulrike von Luxburg, Dávid Pál -
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
András Antos, Csaba Szepesvári, Rémi Munos -
The Binning Algorithm
Jacob Abernethy, John Langford, Manfred K. Warmuth
