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Core Methods

48,729 papers (8 classified directly here)

Subtopics

Representation Learning (20,822) Classification (15,805) Regression (5,004) Embedding Learning (3,753) Clustering (3,638) Metric Learning (2,854) Graph Neural Networks (1,141) Graphical Models (985) Feature Learning (860) Dimensionality Reduction (707) Kernel Methods (642) Interpretability (499) Feature Selection (479) Matrix Factorization (383) Ranking (283) Ensemble Methods (267) Anomaly Detection (267) Support Vector Machine (254) Sequence Modeling (237) Structured Prediction (205) Sequence Labeling (166) Topic Modeling (123) Sparse Coding (107) Matrix Completion (103) Retrieval (99) Neural Networks (79) Inference (21) Model Selection (19) Decision Tree (16) Prediction (15) Density Estimation (9) Estimation (8) Feature Extraction (7) Information Extraction (7)

Papers per year

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Papers (including subtopics)

Efficiently Enforcing Diversity in Multi-Output Structured Prediction AISTATS 2014
Analytic Long-Term Forecasting with Periodic Gaussian Processes AISTATS 2014
Non-Asymptotic Analysis of Relational Learning with One Network AISTATS 2014
Exploiting the Limits of Structure Learning via Inherent Symmetry AISTATS 2014
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees AISTATS 2014
Latent Gaussian Models for Topic Modeling AISTATS 2014
A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models AISTATS 2014
Global Optimization Methods for Extended Fisher Discriminant Analysis AISTATS 2014
High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation AISTATS 2014
New Bounds on Compressive Linear Least Squares Regression AISTATS 2014
Collaborative Ranking for Local Preferences AISTATS 2014
Scalable Collaborative Bayesian Preference Learning AISTATS 2014
A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data AISTATS 2014
Scalable Variational Bayesian Matrix Factorization with Side Information AISTATS 2014
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data AISTATS 2014
Computational Education using Latent Structured Prediction AISTATS 2014
Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014
Jointly Informative Feature Selection AISTATS 2014
Learning Heterogeneous Hidden Markov Random Fields AISTATS 2014
Interpretable Sparse High-Order Boltzmann Machines 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
Scaling Nonparametric Bayesian Inference via Subsample-Annealing AISTATS 2014
Fast Distribution To Real Regression AISTATS 2014