A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New framework learns interpretable rule ensembles without sacrificing accuracy.
In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…
Ensemble methods for supervised machine learning have become popular due to their ability to accurately predict class labels with groups of simple, lightweight "base learners." While ensembles offer computationally efficient models that have good predictive capability they tend to be large and offer little insight into…
FIRE extracts interpretable rules from tree ensembles.
In this article, the logic rule ensembles approach to supervised learning is applied to the unsupervised or semi-supervised clustering. Logic rules which were obtained by combining simple conjunctive rules are used to partition the input space and an ensemble of these rules is used to define a similarity matrix. Simila…
This paper improves prediction rule ensembles using model-based data generation.
A new ensemble method using random projections for kNN classification.
We propose a method to extract interpretable rules from tree ensembles.
Fed-FEARE model extracts rules from multiple agencies' data securely.
A new ensemble method improves kNN performance by extending the neighborhood rule.
A new stopping rule based on E-values helps efficiently use sampling in Bayesian Deep Ensembles.
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules. LIBRE uses an ensemble of bottom-up weak learners operating on a random subset of features, which allows for the learning of rules that generalize well on unseen data even in imbalanced settings. Weak…
This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set comple…
Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based le…
A new stochastic method handles ensemble creation with cost constraints.
SigD2 reduces noisy rules in rule-based classifiers for better accuracy and readability.
Design rule check is a critical step in the physical design of integrated circuits to ensure manufacturability. However, it can be done only after a time-consuming detailed routing procedure, which adds drastically to the time of design iterations. With advanced technology nodes, the outcomes of global routing and deta…
New method improves model explainability and accuracy with low computational cost.
CRE discovers interpretable subgroups with heterogeneous treatment effects.
Improving Bayesian filtering with strictly proper scoring rules
In this article, we have proposed several approaches for post processing a large ensemble of prediction models or rules. The results from our simulations show that the post processing methods we have considered here are promising. We have used the techniques developed here for estimation of quantitative traits from mar…
The paper develops a stationary-distribution theory for Random Forest ensemble size selection.
Two algorithms for interpreting and boosting tree-based models using rule covering.
We propose a new Bayesian model for flexible nonlinear regression and classification using tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu (2008) where rules from decision trees and linear terms are used in a L1-regularized regression. We modify RuleFit by replacing the L1-regularizat…
A new gradient boosting method improves interpretability of probabilistic models.
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of existing batch and online ensemble classifiers. There are only a limited number of studies on the ense…
Proposes an interpretable machine learning framework for multi-arm HTE estimation.
Oblique BART improves tree-based predictions.
Improved Bayesian inference for neuronal ensemble inference reduces computational cost.
A new method predicts precipitation distributions from ensemble forecasts.
The paper explores dynamic ensembles for multi-step forecasting.
Wisdom of the crowd revealed a striking fact that the majority answer from a crowd is often more accurate than any individual expert. We observed the same story in machine learning--ensemble methods leverage this idea to combine multiple learning algorithms to obtain better classification performance. Among many popula…
Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment…
MOSS optimizes decision rules for accuracy and stability.
We consider using an ensemble of binary classifiers for transductive prediction, when unlabeled test data are known in advance. We derive minimax optimal rules for confidence-rated prediction in this setting. By using PAC-Bayes analysis on these rules, we obtain data-dependent performance guarantees without distributio…
Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In thi…
Repulsive deep ensembles improve diversity and Bayesian inference.
New NIDS uses hypergraphs for real-time detection of evolving port scans.
A new method creates simpler, more interpretable decision trees from complex ensembles.
We consider the problem of learning decision rules for prediction with feature budget constraint. In particular, we are interested in pruning an ensemble of decision trees to reduce expected feature cost while maintaining high prediction accuracy for any test example. We propose a novel 0-1 integer program formulation …
The motivation of this work is to improve the performance of standard stacking approaches or ensembles, which are composed of simple, heterogeneous base models, through the integration of the generation and selection stages for regression problems. We propose two extensions to the standard stacking approach. In the fir…
AEA dynamically aggregates ensemble targets for actor-critic learning.
This paper introduces TNTK to study infinite soft tree ensembles.
We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-conv…
This study examines ensembling of diffusion models for improved generative quality.
High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametr…
This study compares multivariate vs univariate machine learning for multi-output regression.
A new ensemble of Gaussian processes improves active learning efficiency.