On Online Learning of Decision Lists.
A Robust Minimax Approach to Classification.
Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling.
Multiple-Instance Learning of Real-Valued Data.
MDPs: Learning in Varying Environments.
Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem.
The Set Covering Machine.
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions.
Tracking a Small Set of Experts by Mixing Past Posteriors.
Algorithmic Luckiness.
Learning to Construct Fast Signal Processing Implementations.
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results.
Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels.
Lyapunov Design for Safe Reinforcement Learning.
Coupled Clustering: A Method for Detecting Structural Correspondence.
Learning Probabilistic Models of Link Structure.
Using Confidence Bounds for Exploitation-Exploration Trade-offs.
Efficient Algorithms for Decision Tree Cross-validation.
Kernel Independent Component Analysis.
Learning Precise Timing with LSTM Recurrent Networks.
Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries.
Limitations of Learning Via Embeddings in Euclidean Half Spaces.
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification.
On Boosting with Polynomially Bounded Distributions.
The Representational Power of Discrete Bayesian Networks.
Data-dependent margin-based generalization bounds for classification.
Optimal Structure Identification With Greedy Search.
Policy Search using Paired Comparisons.
Efficient Algorithms for Universal Portfolios.
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces.
Minimal Kernel Classifiers.
On the Convergence of Optimistic Policy Iteration.
Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components.
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning.
