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Machine Learning

121,146 papers (10 classified directly here)

Subtopics

Learning Types (52,936) Core Methods (48,729) Optimization & Theory (36,874) Application Areas (31,658) Bayesian & Probabilistic (3,963) Learning Paradigms (2,054)

Papers per year

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Papers (including subtopics)

Active Boundary Annotation using Random MAP Perturbations AISTATS 2014
Interpretable Sparse High-Order Boltzmann Machines AISTATS 2014
Efficient Lifting of MAP LP Relaxations Using k-Locality AISTATS 2014
A Geometric Algorithm for Scalable Multiple Kernel Learning AISTATS 2014
On the Testability of Models with Missing Data AISTATS 2014
Selective Sampling with Drift AISTATS 2014
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling AISTATS 2014
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence AISTATS 2014
Robust Forward Algorithms via PAC-Bayes and Laplace Distributions AISTATS 2014
Joint Structure Learning of Multiple Non-Exchangeable Networks AISTATS 2014
Scaling Nonparametric Bayesian Inference via Subsample-Annealing AISTATS 2014
Fast Distribution To Real Regression AISTATS 2014
FuSSO: Functional Shrinkage and Selection Operator AISTATS 2014
To go deep or wide in learning? AISTATS 2014
LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series AISTATS 2014
Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming AISTATS 2014
An Efficient Algorithm for Large Scale Compressive Feature Learning AISTATS 2014
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables AISTATS 2014
An inclusion optimal algorithm for chain graph structure learning AISTATS 2014
A Stepwise uncertainty reduction approach to constrained global optimization AISTATS 2014
An Analysis of Active Learning with Uniform Feature Noise AISTATS 2014
Black Box Variational Inference AISTATS 2014
Cluster Canonical Correlation Analysis AISTATS 2014
Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process AISTATS 2014
Learning Structured Models with the AUC Loss and Its Generalizations AISTATS 2014