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 insights into pseudo-Anosov flows with special periodic orbits.
SCALLOP improves likelihood flow maps for efficient Boltzmann generation.
Classifies pseudo-Anosov flows on 3-manifolds up to orbit equivalence.
The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resemble compact, axis-aligned decision trees without significant compromises in accuracy. However, it may be unreasonable to expect that a single …
This paper presents a new probabilistic generative model for image segmentation, i.e. the task of partitioning an image into homogeneous regions. Our model is grounded on a mid-level image representation, called a region tree, in which regions are recursively split into subregions until superpixels are reached. Given t…
USNRT uses tree-structured learning to improve uncertainty quantification of variance networks.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
This paper proposes an improved active learning method using classification trees.
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty in the output variable, using for example a quantile loss in Random Forests (Me…
flexBART improves BART for categorical predictors by creating flexible tree partitions.
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…
Paper studies ensemble probabilistic regression trees for smooth approximations.
Decision Machines embeds decision trees into vector spaces for improved optimization.
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods. ITs adaptively sample from hierarchica…
Gradient boosting with randomized trees reduces discontinuities and complexity.
A new model predicts spatio-temporal data using adaptive decision trees and point processes.
This work introduces a novel nonparametric density index defined on graphs, the Sum-over-Forests (SoF) density index. It is based on a clear and intuitive idea: high-density regions in a graph are characterized by the fact that they contain a large amount of low-cost trees with high outdegrees while low-density regions…
Topological methods improve neuron analysis and tracer injection summary.
A new approach uses partial likelihood to improve tree-based density estimation and inference.
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…
Proves convergence of gradient Ricci shrinkers with uniform bounds.
Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications. Existing methods of calibrating probability estimates are applied globally, ignoring the potential for improvements by applying a more fine-gra…
A new algorithm, Regular Tree Search, tackles non-convex simulation optimization problems.
Hollow-tree Super resolves feature importance in large datasets.
Study evaluates how limited training data affects streamflow predictions.
Alpha-trimming prunes trees in random forests to improve predictive performance.
Study on prescribing positive curvature with conical singularities on a sphere.
In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable. Such information is vital since it completely determines the direction in which two variables are associated. In this work, we resort to information theoretical approaches to achieve two fundamental goals: Fir…
As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream mining algorithms are meant to be solutions to extract knowledge online, specially tailored from continuous data problem. Many of the current alg…
Paper introduces methods to handle missing data in probabilistic regression trees.
New method creates adaptive prediction intervals for regression models.
DTE uses tree leaf means to embed data, balancing accuracy and speed.
The paper develops a theory for speculative decoding acceptance criteria.
Bayesian optimization algorithm reduces regret with efficient region pruning.
The paper introduces a method to control false splits in tree-based data aggregation.
We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currenc…
Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications. There exists a large body of work aiming to help humans understand these black box functions to varying levels of granularity -- for example, through distillati…
Study integrates reliability constraints into generation planning models.
Stochastic partition models divide a multi-dimensional space into a number of rectangular regions, such that the data within each region exhibit certain types of homogeneity. Due to the nature of their partition strategy, existing partition models may create many unnecessary divisions in sparse regions when trying to d…
New method combines heuristics and search techniques to speed up cooperative planning for autonomous vehicles.
We introduce a new algorithm, called CDER, for supervised machine learning that merges the multi-scale geometric properties of Cover Trees with the information-theoretic properties of entropy. CDER applies to a training set of labeled pointclouds embedded in a common Euclidean space. If typical pointclouds correspondin…
Using transfer entropy, we observed the strength and direction of information flow between stock indices. We uncovered that the biggest source of information flow is America. In contrast, the Asia/Pacific region the biggest is receives the most information. According to the minimum spanning tree, the GSPC is located at…
This paper uses two hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1996-2001. We find that these two techniques generate a defined and robust scale free network with meaningful ta…
The paper proposes a method to improve random forest classification accuracy by weighting trees based on their decision path reliability.
This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed fi…
By analyzing the foreign exchange market data of various currencies, we derive a hierarchical taxonomy of currencies constructing minimal-spanning trees. Clustered structure of the currencies and the key currency in each cluster are found. The clusters match nicely with the geographical regions of corresponding countri…
The study models credit risk using Merton's framework and binomial trees.
Bayesian model estimates treatment effects near cutoffs in regression discontinuity designs.