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Policy-Gradients for PSRs and POMDPs
Generalized Non-metric Multidimensional Scaling
A Boosting Algorithm for Label Covering in Multilabel Problems
Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
Kernel Multi-task Learning using Task-specific Features
A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data
The Laplacian Eigenmaps Latent Variable Model
Visualizing Similarity Data with a Mixture of Maps
Solving Markov Random Fields with Spectral Relaxation
Fast search for Dirichlet process mixture models
Large-Margin Classification in Banach Spaces
Learning A* underestimates : Using inference to guide inference
Exact Bayesian structure learning from uncertain interventions
Online Learning of Search Heuristics
Deterministic Annealing for Multiple-Instance Learning
Approximate inference using conditional entropy decompositions
Visualizing pairwise similarity via semidefinite programming
SampleSearch: A Scheme that Searches for Consistent Samples
Dissimilarity in Graph-Based Semi-Supervised Classification
Information Retrieval by Inferring Implicit Queries from Eye Movements
A Nonparametric Bayesian Approach to Modeling Overlapping Clusters
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
Learning Markov Structure by Maximum Entropy Relaxation
Multi-object tracking with representations of the symmetric group
MDL Histogram Density Estimation
Incorporating Prior Knowledge on Features into Learning
Fast Low-Rank Semidefinite Programming for Embedding and Clustering
Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization
Treelets | A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data
Efficient active learning with generalized linear models
A Bayesian Divergence Prior for Classiffier Adaptation
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
Fisher Consistency of Multicategory Support Vector Machines
Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach
Recall Systems: Effcient Learning and Use of Category Indices
AClass: A simple, online, parallelizable algorithm for probabilistic classification
A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games
Loop Corrected Belief Propagation
Inductive Transfer for Bayesian Network Structure Learning
Maximum Entropy Correlated Equilibria
Approximate Counting of Graphical Models Via MCMC
Margin based Transductive Graph Cuts using Linear Programming
A Unified Energy-Based Framework for Unsupervised Learning
(Approximate) Subgradient Methods for Structured Prediction
A fast algorithm for learning large scale preference relations
The Rademacher Complexity of Co-Regularized Kernel Classes
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data
A Stochastic Quasi-Newton Method for Online Convex Optimization
Bayesian Inference and Optimal Design in the Sparse Linear Model
A Unified Algorithmic Approach for Efficient Online Label Ranking
A Framework for Probability Density Estimation
Fast Kernel ICA using an Approximate Newton Method
Fast State Discovery for HMM Model Selection and Learning
Analogical Reasoning with Relational Bayesian Sets
Dynamic Factorization Tests: Applications to Multi-modal Data Association
Generalized Darting Monte Carlo
Local and global sparse Gaussian process approximations
Predictive Discretization during Model Selection
Emerge and spread models and word burstiness
Learning Multilevel Distributed Representations for High-Dimensional Sequences
Stick-breaking Construction for the Indian Buffet Process
Hierarchical Beta Processes and the Indian Buffet Process
Nonlinear Dimensionality Reduction as Information Retrieval
The Kernel Path in Kernelized LASSO
Efficient large margin semisupervised learning
Fast Mean Shift with Accurate and Stable Convergence
Metric Learning for Kernel Regression
Performance Guarantees for Information Theoretic Active Inference
Transductive Classification via Local Learning Regularization
How Powerful Can Any Regression Learning Procedure Be?
Importance Sampling for General Hybrid Bayesian Networks
Nonnegative Garrote Component Selection in Functional ANOVA models
Generalized Do-Calculus with Testable Causal Assumptions
An Improved 1-norm SVM for Simultaneous Classification and Variable Selection