Papers with Code 아카이브

Variable Selection

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tkdmah/trlasso

공식 코드

Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective

https://github.com/tmichoel/bayonet

공식 코드

Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net

https://github.com/tobias-freidling/hsic-lasso-psi

공식 코드

Post-selection inference with HSIC-Lasso

https://github.com/tobiasoh/master_thesis

공식 코드

Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification

https://github.com/topipa/GP_varsel_KL_VAR

공식 코드

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

https://github.com/topipa/gp-varsel-kl-var

공식 코드

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

https://github.com/vineet1992/Gene-Selection

공식 코드

A Pipeline for Integrated Theory and Data-Driven Modeling of Genomic and Clinical Data

https://github.com/vishalned/aircast

공식 코드

AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment

https://github.com/weichangyu10/gpdapublic

공식 코드

Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data

https://github.com/wenshaz/elpaso

공식 코드

Evolutionary shift detection with ensemble variable selection

https://github.com/wenshaz/shiva

공식 코드

Detection of evolutionary shifts in variance under an Ornsten-Uhlenbeck model

https://github.com/wmyw96/EILLS

공식 코드

Environment Invariant Linear Least Squares

https://github.com/woodyx218/SLOPE_AMP

공식 코드

Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit

https://github.com/xmengju/RRBoost

공식 코드

Robust Boosting for Regression Problems

https://github.com/y3nk0/OMP-for-Text-Classification

공식 코드

Orthogonal Matching Pursuit for Text Classification

https://github.com/yao-yl/Multimodal-stacking-code

공식 코드

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors

https://github.com/ydecastro/lar_testing

공식 코드

Multiple Testing and Variable Selection along the path of the Least Angle Regression

https://github.com/ynlin11/filter

공식 코드

Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression

https://github.com/yoshikawa-kohei/RVSManOpt

공식 코드

Sparse Reduced-Rank Regression for Simultaneous Rank and Variable Selection via Manifold Optimization

https://github.com/zankin/sovbr

공식 코드

Sparse online variational Bayesian regression

https://github.com/zuowx/deeplink-t

공식 코드

DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM

https://gitlab.com/AnonPaperCode/sss

공식 코드

Scalable Spike-and-Slab

https://github.com/JakeColtman/bartpy

BART: Bayesian additive regression trees

https://github.com/KDiazOrdaz/Machine-learning-in-policy-evaluation-new-tools-for-causal-inference

Machine learning in policy evaluation: new tools for causal inference

https://github.com/KurochkinAlexey/SpatioTemporalMomentum

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

https://github.com/Marga8/HDGCvar

Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure

https://github.com/Peyman-HK/De-randomized-HiDe-MK

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants

https://github.com/Snowball119/Mixed_Data_PCA

Covariance and PCA for Categorical Variables

https://github.com/XiaoouPan/tfHuber

Iteratively Reweighted $\ell_1$-Penalized Robust Regression

https://github.com/abess-team/abess

abess: A Fast Best Subset Selection Library in Python and R

https://github.com/alec-flowers/machine-learning-cs433-p2

Deep Knockoffs

https://github.com/alexmclain/probe

Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression

https://github.com/audreyanneguindon/NeurIPS_2019

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

https://github.com/broadinstitute/millipede

Fast Bayesian Variable Selection in Binomial and Negative Binomial Regression

https://github.com/dmolitor/bolasso

Bolasso: model consistent Lasso estimation through the bootstrap

https://github.com/ds4dm/branch-search-trees

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.