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.
Enhances random forest consistency and introduces DMRF for improved performance.
We improve random forest consistency and performance with DMRF, a new variant.
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze the \emph{consistency} and \emph{privacy-preservation}. Instead of deterministic greedy split rule…
Approximates large Random Forest models to save space.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
Problem definition. In retailing, discrete choice models (DCMs) are commonly used to capture the choice behavior of customers when offered an assortment of products. When estimating DCMs using transaction data, flexible models (such as machine learning models or nonparametric models) are typically not interpretable and…
The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Surprisingly, there is no prior work developing tree-induction algorithms for building fair decision trees or fair random forests. These metho…
New method tests risk measures for various distortions.
We investigate concentration inequalities for Dirichlet and Multinomial random variables.
A new method selects representative subdata without assuming underlying models.
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r…
New random forest method provides optimal rates and confidence bands.
Improved random forest proximities capture data geometry.
We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized end-to-end with stochastic gradient descent or variants thereof. We derive random hinge…
Random forests reduce bias and variance, especially in low SNR settings.
Improved random forest models enhance machine learning predictions.
New random forest variants achieve optimal performance in high dimensions.
This paper is a comment on the survey paper by Biau and Scornet (2016) about random forests. We focus on the problem of quantifying the impact of each ingredient of random forests on their performance. We show that such a quantification is possible for a simple pure forest , leading to conclusions that could apply more…
RFpredInterval package builds prediction intervals for random forests and boosted forests.
Enhances random forest performance with exogenous randomness.
Online random forests improve Q-learning performance in specific gym environments.
Paper explores grafting consistent estimators to improve Random Forest consistency.
Random forests have proven to be reliable predictive algorithms in many application areas. Not much is known, however, about the statistical properties of random forests. Several authors have established conditions under which their predictions are consistent, but these results do not provide practical estimates of ran…
Theoretical study of random forests for nonlinear time series.
To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binar…
We propose an algorithm named best-scored random forest for binary classification problems. The terminology "best-scored" means to select the one with the best empirical performance out of a certain number of purely random tree candidates as each single tree in the forest. In this way, the resulting forest can be more …
In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a \textit{one-step boosted …
Alpha-trimming prunes trees in random forests to improve predictive performance.
New methods improve prediction performance and reduce computation time in boosting and random forest models.
Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory. Banzhaf power index …
A random forest is a popular tool for estimating probabilities in machine learning classification tasks. However, the means by which this is accomplished is unprincipled: one simply counts the fraction of trees in a forest that vote for a certain class. In this paper, we forge a connection between random forests and ke…
Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also prov…
Paper proposes a new method combining random forests and Lasso selection.
Develops a new random forest method for clustered data with improved prediction and inference.
Proves bounds on spanning two-forests and random cut sizes.
Shapelet is a discriminative subsequence of time series. An advanced shapelet-based method is to embed shapelet into accurate and fast random forest. However, it shows several limitations. First, random shapelet forest requires a large training cost for split threshold searching. Second, a single shapelet provides limi…
This paper explains CART random forests using stochastic control theory.
Forest-guided smoothing uses random forest outputs for interpretable local smoothers.
Paper assesses error estimates of Random Forests classification.
Random forests classify Pokemon names based on evolutionary status.
As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.
Random forest is widely exploited as an ensemble learning method. In many practical applications, however, there is still a significant challenge to learn from imbalanced data. To alleviate this limitation, we propose a deep dynamic boosted forest (DDBF), a novel ensemble algorithm that incorporates the notion of hard …
A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted av…
Random forests are stable and provide reliable prediction intervals.
Random forests with attention and self-attention improve regression performance.
New method improves feature importance assessment in random forests.
Mondrian random forests improve statistical inference for regression.
Paper proposes a new financial fraud detection system using improved RF and GBM.