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.
Oblique BART improves tree-based predictions.
FoLDTree improves oblique decision trees with ULDA, enhancing accuracy and feature selection.
This paper optimizes high-dimensional oblique splits for decision trees, enhancing performance and computational efficiency.
Decision trees are a popular technique in statistical data classification. They recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. The basic Classification and Regression Tree (CART) algorithm partitions the feature space using…
Sparse oblique decision tree improves security rules for renewable power systems.
New tree and forest methods use oblique splits for better risk bounds.
Proposes oblique predictive clustering trees for faster, more efficient learning.
We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally …
Efficient oblique RSF method improves prediction and interpretability.
Enhanced ODT with Feature Concatenation boosts learning efficiency.
Resource-efficient oblique trees reduce neural signal classification costs.
Both neural networks and decision trees are popular machine learning methods and are widely used to solve problems from diverse domains. These two classifiers are commonly used base classifiers in an ensemble framework. In this paper, we first present a new variant of oblique decision tree based on a linear classifier,…
Paper proposes a novel SVM method for creating survival trees.
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many r…
Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scr…
NSOTree combines neural networks and trees for better survival analysis interpretability.
Study integrates reliability constraints into generation planning models.
Single tree outperforms random forest in testing accuracy.
We present a new way of constructing an ensemble classifier, named the Guided Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique decision trees with localized partitioning to obtain a global partitioning. We show that global partitioning bridges the gap between decision trees and boosting alg…
The Mondrian process represents an elegant and powerful approach for space partition modelling. However, as it restricts the partitions to be axis-aligned, its modelling flexibility is limited. In this work, we propose a self-consistent Binary Space Partitioning (BSP)-Tree process to generalize the Mondrian process. Th…
New random forest variants achieve optimal performance in high dimensions.
Proposes PredVAR model for reduced-dimensional dynamics from noisy data.
We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression. Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training. Unlike axis-aligned alternatives…
Unified theory and debiasing framework for random oblique projections in high dimensions.
Develops an oblique projection technique to approximate a foliation for non-normal dynamics.
In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more…
Machine learning identifies chimera states in complex dynamical systems.
Surface area and mean width of a cylinder (the convex hull of two parallel disks) in R^3 are computed. It is more difficult to obtain analogous results for a cone (the convex hull of a disk D and a point p). Oblique formulas for mean width, as well as those for mean curvature, are new. Let L denote the unique diameter …
Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the s…
A new modeling framework CSN simplifies and interprets machine learning models.
New algorithms improve policy evaluation in reinforcement learning.
The paper simplifies hedging and portfolio allocation in markets without a risk-free asset.
The study calculates the average genus of 2-bridge knots based on their crossing numbers.
A new topology design improves zero-shot classification performance in contrastive learning.
Study benchmarks 19 survival models on 34 datasets, finding Cox model still best.
NR retraction approximates geodesics on submanifolds efficiently.
This paper presents four different ways of looking at the well-known Least Squares Temporal Differences (LSTD) algorithm for computing the value function of a Markov Reward Process, each of them leading to different insights: the operator-theory approach via the Galerkin method, the statistical approach via instrumenta…
Adapts Stein's method for geometric inequalities, addressing boundary terms.
We study an agent-based stock market model with heterogeneous agents and friction. Our model is based on that of Foellmer-Schweizer(1993): The process of a stock price in a discrete-time framework is determined by temporary equilibria via agents' excess demand functions, and the diffusion approximation approach is appl…
New spectral estimates for minimal surfaces with boundary conditions.
Boosting meta-trees improve decision tree performance.
The paper studies geometric properties of quasi-trees and tree approximations.
Efficiently updates posterior tree distributions over meta-trees.
This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach. Ideally, we would like to consider predictio…
Study geodesics on neck-degenerate manifolds, focusing and winding behavior observed.
We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification to the MOA implementat…
Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations tha…
We introduce block-tree graphs as a framework for deriving efficient algorithms on graphical models. We define block-tree graphs as a tree-structured graph where each node is a cluster of nodes such that the clusters in the graph are disjoint. This differs from junction-trees, where two clusters connected by an edge al…