NDLI: Active learning for sampling in time-series experiments with application to gene expression analysis

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Exploration and apprenticeship learning in reinforcement learning

Active learning for Hidden Markov Models: objective functions and algorithms

Tempering for Bayesian C&RT

Fast condensed nearest neighbor rule

Predictive low-rank decomposition for kernel methods

Multi-way distributional clustering via pairwise interactions

Error limiting reductions between classification tasks

Multi-instance tree learning

Action respecting embedding

Clustering through ranking on manifolds

Reducing overfitting in process model induction

Learning to rank using gradient descent

Learning class-discriminative dynamic Bayesian networks

Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM

Predicting probability distributions for surf height using an ensemble of mixture density networks

Hedged learning: regret-minimization with learning experts

Variational Bayesian image modelling

Preference learning with Gaussian processes

New approaches to support vector ordinal regression

A general regression technique for learning transductions

Learning to compete, compromise, and cooperate in repeated general-sum games

Learning as search optimization: approximate large margin methods for structured prediction

Multimodal oriented discriminant analysis

A practical generalization of Fourier-based learning

Combining model-based and instance-based learning for first order regression

Reinforcement learning with Gaussian processes

Experimental comparison between bagging and Monte Carlo ensemble classification

Supervised clustering with support vector machines

Optimal assignment kernels for attributed molecular graphs

Closed-form dual perturb and combine for tree-based models

Hierarchic Bayesian models for kernel learning

Online feature selection for pixel classification

Learning strategies for story comprehension: a reinforcement learning approach

Near-optimal sensor placements in Gaussian processes

Robust one-class clustering using hybrid global and local search

Statistical and computational analysis of locality preserving projection

Intrinsic dimensionality estimation of submanifolds in $R^{d}$

Bayesian hierarchical clustering

Online learning over graphs

Adapting two-class support vector classification methods to many class problems

A martingale framework for concept change detection in time-varying data streams

Multi-class protein fold recognition using adaptive codes

Learning approximate preconditions for methods in hierarchical plans

Evaluating machine learning for information extraction

Learn to weight terms in information retrieval using category information

A smoothed boosting algorithm using probabilistic output codes

Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes

A support vector method for multivariate performance measures

Error bounds for correlation clustering

Interactive learning of mappings from visual percepts to actions

A causal approach to hierarchical decomposition of factored MDPs

A comparison of tight generalization error bounds

Generalized LARS as an effective feature selection tool for text classification with SVMs

Ensembles of biased classifiers

Computational aspects of Bayesian partition models

Learning the structure of Markov logic networks

Using additive expert ensembles to cope with concept drift

Semi-supervised graph clustering: a kernel approach

A brain computer interface with online feedback based on magnetoencephalography

Relating reinforcement learning performance to classification performance

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers

Heteroscedastic Gaussian process regression

Predicting relative performance of classifiers from samples

Logistic regression with an auxiliary data source

Predicting protein folds with structural repeats using a chain graph model

Unsupervised evidence integration

Naive Bayes models for probability estimation

ROC confidence bands: an empirical evaluation

Modeling word burstiness using the Dirichlet distribution

Proto-value functions: developmental reinforcement learning

The cross entropy method for classification

Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees

Comparing clusterings: an axiomatic view

Weighted decomposition kernels

High speed obstacle avoidance using monocular vision and reinforcement learning

Dynamic preferences in multi-criteria reinforcement learning

Learning first-order probabilistic models with combining rules

An efficient method for simplifying support vector machines

Predicting good probabilities with supervised learning

Recycling data for multi-agent learning

A graphical model for chord progressions embedded in a psychoacoustic space

Q-learning of sequential attention for visual object recognition from informative local descriptors

Discriminative versus generative parameter and structure learning of Bayesian network classifiers

Optimizing abstaining classifiers using ROC analysis

Independent subspace analysis using geodesic spanning trees

A model for handling approximate, noisy or incomplete labeling in text classification

Healing the relevance vector machine through augmentation

Supervised versus multiple instance learning: an empirical comparison

Generalized skewing for functions with continuous and nominal attributes

Fast maximum margin matrix factorization for collaborative prediction

Coarticulation: an approach for generating concurrent plans in Markov decision processes

Why skewing works: learning difficult Boolean functions with greedy tree learners

Integer linear programming inference for conditional random fields

Learning hierarchical multi-category text classification models

Expectation maximization algorithms for conditional likelihoods

Estimating and computing density based distance metrics

Supervised dimensionality reduction using mixture models

Object correspondence as a machine learning problem

Analysis and extension of spectral methods for nonlinear dimensionality reduction

Non-negative tensor factorization with applications to statistics and computer vision

Fast inference and learning in large-state-space HMMs

New d-separation identification results for learning continuous latent variable models

Identifying useful subgoals in reinforcement learning by local graph partitioning

Beyond the point cloud: from transductive to semi-supervised learning

Active learning for sampling in time-series experiments with application to gene expression analysis

Compact approximations to Bayesian predictive distributions

Large scale genomic sequence SVM classifiers

A theoretical analysis of Model-Based Interval Estimation

Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning

Unifying the error-correcting and output-code AdaBoost within the margin framework

Finite time bounds for sampling based fitted value iteration

TD(λ) networks: temporal-difference networks with eligibility traces

Learning structured prediction models: a large margin approach

Learning discontinuities with products-of-sigmoids for switching between local models

Core Vector Regression for very large regression problems

Propagating distributions on a hypergraph by dual information regularization

Hierarchical Dirichlet model for document classification

Implicit surface modelling as an eigenvalue problem

New kernels for protein structural motif discovery and function classification

Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields

Bayesian sparse sampling for on-line reward optimization

Learning predictive representations from a history

Incomplete-data classification using logistic regression

Learning predictive state representations in dynamical systems without reset

Linear Asymmetric Classifier for cascade detectors

Building Sparse Large Margin Classifiers

Dirichlet enhanced relational learning

Learning Gaussian processes from multiple tasks

Augmenting naive Bayes for ranking

A new Mallows distance based metric for comparing clusterings

Learning from labeled and unlabeled data on a directed graph

2D Conditional Random Fields for Web information extraction

Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning

Large margin non-linear embedding

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