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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)

Bethe Learning of Graphical Models via MAP Decoding AISTATS 2016
Scalable and Sound Low-Rank Tensor Learning AISTATS 2016
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information AISTATS 2016
Topic-Based Embeddings for Learning from Large Knowledge Graphs AISTATS 2016
Improper Deep Kernels AISTATS 2016
Non-Gaussian Component Analysis with Log-Density Gradient Estimation AISTATS 2016
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models AISTATS 2016
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation AISTATS 2016
Robust Covariate Shift Regression AISTATS 2016
On Lloyd’s Algorithm: New Theoretical Insights for Clustering in Practice AISTATS 2016
Parallel Markov Chain Monte Carlo via Spectral Clustering AISTATS 2016
Active Learning Algorithms for Graphical Model Selection AISTATS 2016
Streaming Kernel Principal Component Analysis AISTATS 2016
Back to the Future: Radial Basis Function Networks Revisited AISTATS 2016
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions AISTATS 2016
NYTRO: When Subsampling Meets Early Stopping AISTATS 2016
Spectral M-estimation with Applications to Hidden Markov Models AISTATS 2016
Chained Gaussian Processes AISTATS 2016
Multiresolution Matrix Compression AISTATS 2016
Supervised Neighborhoods for Distributed Nonparametric Regression AISTATS 2016
Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation AISTATS 2016
Mondrian Forests for Large-Scale Regression when Uncertainty Matters AISTATS 2016
Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization AISTATS 2016
Discriminative Structure Learning of Arithmetic Circuits AISTATS 2016
One Scan 1-Bit Compressed Sensing AISTATS 2016