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Greatest papers with code

BART: Bayesian additive regression trees

19 Jun 2008JakeColtman/bartpy

We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior.

DRUG DISCOVERY VARIABLE SELECTION

Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

17 Jan 2020hazimehh/L0Learn

We consider a discrete optimization based approach for learning sparse classifiers, where the outcome depends upon a linear combination of a small subset of features.

VARIABLE SELECTION

Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms

5 Mar 2018hazimehh/L0Learn

In spite of the usefulness of $L_0$-based estimators and generic MIO solvers, there is a steep computational price to pay when compared to popular sparse learning algorithms (e. g., based on $L_1$ regularization).

COMBINATORIAL OPTIMIZATION FEATURE SELECTION SPARSE LEARNING VARIABLE SELECTION

metboost: Exploratory regression analysis with hierarchically clustered data

13 Feb 2017patr1ckm/mvtboost

A machine learning method called boosted decision trees (Friedman, 2001) is a good approach for exploratory regression analysis in real data sets because it can detect predictors with nonlinear and interaction effects while also accounting for missing data.

MODEL SELECTION VARIABLE SELECTION

Combinatorial Bayesian Optimization using the Graph Cartesian Product

NeurIPS 2019 QUVA-Lab/COMBO

On this combinatorial graph, we propose an ARD diffusion kernel with which the GP is able to model high-order interactions between variables leading to better performance.

NEURAL ARCHITECTURE SEARCH VARIABLE SELECTION

Invariant Causal Prediction for Block MDPs

ICML 2020 facebookresearch/icp-block-mdp

Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges.

CAUSAL INFERENCE VARIABLE SELECTION

Deep Knockoffs

16 Nov 2018msesia/deepknockoffs

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.

VARIABLE SELECTION

Learning Local Search Heuristics for Boolean Satisfiability

NeurIPS 2019 emreyolcu/sat

We present an approach to learn SAT solver heuristics from scratch through deep reinforcement learning with a curriculum.

VARIABLE SELECTION

A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

16 Aug 2018FWen/ncreg

In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.

COMPRESSIVE SENSING MATRIX COMPLETION VARIABLE SELECTION