Markov Properties for Linear Causal Models with Correlated Errors.
Learning Nondeterministic Classifiers.
Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm.
Bounded Kernel-Based Online Learning.
Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning.
Fourier Theoretic Probabilistic Inference over Permutations.
Structure Spaces.
Analysis of Perceptron-Based Active Learning.
A Parameter-Free Classification Method for Large Scale Learning.
Sparse Online Learning via Truncated Gradient.
Multi-task Reinforcement Learning in Partially Observable Stochastic Environments.
Distributed Algorithms for Topic Models.
Efficient Online and Batch Learning Using Forward Backward Splitting.
RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments.
Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions.
On the Consistency of Feature Selection using Greedy Least Squares Regression.
Similarity-based Classification: Concepts and Algorithms.
Bayesian Network Structure Learning by Recursive Autonomy Identification.
Learning Halfspaces with Malicious Noise.
Dlib-ml: A Machine Learning Toolkit.
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods.
Using Local Dependencies within Batches to Improve Large Margin Classifiers.
Online Learning with Sample Path Constraints.
Data-driven Calibration of Penalties for Least-Squares Regression.
Hash Kernels for Structured Data.
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization.
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models.
Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression.
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs.
Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training.
Model Monitor (
Maximum Entropy Discrimination Markov Networks.
Particle Swarm Model Selection.
Nonextensive Information Theoretic Kernels on Measures.
On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality.
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks.
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination.
Cautious Collective Classification.
Properties of Monotonic Effects on Directed Acyclic Graphs.
Online Learning with Samples Drawn from Non-identical Distributions.
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent.
Learning Approximate Sequential Patterns for Classification.
Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification.
Learning Linear Ranking Functions for Beam Search with Application to Planning.
Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques.
Learning Permutations with Exponential Weights.
Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks.
Discriminative Learning Under Covariate Shift.
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost.
DL-Learner: Learning Concepts in Description Logics.
An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems.
Reproducing Kernel Banach Spaces for Machine Learning.
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization.
Polynomial-Delay Enumeration of Monotonic Graph Classes.
Ultrahigh Dimensional Feature Selection: Beyond The Linear Model.
Nonlinear Models Using Dirichlet Process Mixtures.
Java-ML: A Machine Learning Library.
Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data.
Nieme: Large-Scale Energy-Based Models.
Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors.
Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions.
Subgroup Analysis via Recursive Partitioning.
Consistency and Localizability.
A Least-squares Approach to Direct Importance Estimation.
Incorporating Functional Knowledge in Neural Networks.
On Efficient Large Margin Semisupervised Learning: Method and Theory.
Stable and Efficient Gaussian Process Calculations.
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications.
Distance Metric Learning for Large Margin Nearest Neighbor Classification.
Evolutionary Model Type Selection for Global Surrogate Modeling.
Refinement of Reproducing Kernels.
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity.
Robust Process Discovery with Artificial Negative Events.
Adaptive False Discovery Rate Control under Independence and Dependence.
Generalization Bounds for Ranking Algorithms via Algorithmic Stability.
Learning When Concepts Abound.
Reinforcement Learning in Finite MDPs: PAC Analysis.
On The Power of Membership Queries in Agnostic Learning.
Low-Rank Kernel Learning with Bregman Matrix Divergences.
Marginal Likelihood Integrals for Mixtures of Independence Models.
Robustness and Regularization of Support Vector Machines.
Estimating Labels from Label Proportions.
Prediction With Expert Advice For The Brier Game.
Fast Approximate
Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation.
Classification with Gaussians and Convex Loss.
When Is There a Representer Theorem? Vector Versus Matrix Regularizers.
Learning Acyclic Probabilistic Circuits Using Test Paths.
CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning.
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks.
Exploring Strategies for Training Deep Neural Networks.
NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM.
Provably Efficient Learning with Typed Parametric Models.
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List.
Transfer Learning for Reinforcement Learning Domains: A Survey.
Scalable Collaborative Filtering Approaches for Large Recommender Systems.
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining.
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs.
Universal Kernel-Based Learning with Applications to Regular Languages.
Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods.
