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← Core Methods
Machine Learning
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Core Methods
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Feature Selection
462 directly classified papers
Papers per year
2001: 1
2003: 11
2004: 4
2005: 1
2006: 6
2007: 13
2008: 8
2009: 11
2010: 17
2011: 14
2012: 18
2013: 26
2014: 20
2015: 14
2016: 23
2017: 27
2018: 24
2019: 28
2020: 35
2021: 29
2022: 34
2023: 36
2024: 32
2025: 26
2026: 4
Papers
Noising versus Smoothing for Vertex Identification in Unknown Shapes
CVPR 2014
Submodular Attribute Selection for Action Recognition in Video
NIPS 2014
Parallel Feature Selection Inspired by Group Testing
NIPS 2014
Median Selection Subset Aggregation for Parallel Inference
NIPS 2014
Sparse Bayesian structure learning with “dependent relevance determination” priors
NIPS 2014
Local Decorrelation For Improved Pedestrian Detection
NIPS 2014
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
NIPS 2014
Learning Feature Selection Dependencies in Multi-task Learning
NIPS 2013
Estimating LASSO Risk and Noise Level
NIPS 2013
Ultrahigh Dimensional Feature Screening via RKHS Embeddings
AISTATS 2013
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
AISTATS 2013
Summary Statistics for Partitionings and Feature Allocations
NIPS 2013
Least Informative Dimensions
NIPS 2013
On the Complexity and Approximation of Binary Evidence in Lifted Inference
NIPS 2013
Supervised Sparse Analysis and Synthesis Operators
NIPS 2013
Scale Invariant Conditional Dependence Measures
ICML 2013
Hash Bit Selection: A Unified Solution for Selection Problems in Hashing
CVPR 2013
A Unified Robust Regression Model for Lasso-like Algorithms
ICML 2013
Understanding variable importances in forests of randomized trees
NIPS 2013
On model selection consistency of penalized M-estimators: a geometric theory
NIPS 2013
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
NIPS 2013
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
NIPS 2013
Feature Multi-Selection among Subjective Features
ICML 2013
Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model
ICCV 2013
Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery
ICML 2013
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