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.
This paper introduces TNTK to study infinite soft tree ensembles.
Paper analyzes soft tree ensembles using NTK, finding only leaf count matters.
Rectified decision trees improve machine learning interpretability and effectiveness.
ASBART accelerates Soft BART for faster Bayesian regression.
A new type of distributional regression tree uses soft split rules for better predictive performance.
Bayesian GBMs improve predictive uncertainty calibration for tabular data.
VaSST uses soft symbolic trees for probabilistic symbolic regression.
SBAMDT uses adaptive soft splits to model complex decision boundaries.
We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. …
This paper improves deep forest models with soft routing and topology learning.
The paper introduces a new model to correct bias in treatment effect estimates due to sample selection.
How to obtain a model with good interpretability and performance has always been an important research topic. In this paper, we propose rectified decision trees (ReDT), a knowledge distillation based decision trees rectification with high interpretability, small model size, and empirical soundness. Specifically, we ext…
sGBM speeds up gradient boosting by parallelizing and adapting base learners.
Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years. Hierarchical mixture of experts is a hierarchically gated model that defines a soft decision tree where leaves correspond to experts and decision nodes correspond to gating …
We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree learning, our method operates in the incremental learning setting rather than the batch learning setting, and does not make use of soft splits…
Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous parameter space, and therefore a budding tree can be jointly trained with backpropagation, like a neural network. Even though this continuity …
SoftBart improves BART for high-noise modeling in science.
Deep neural networks have proved to be a very effective way to perform classification tasks. They excel when the input data is high dimensional, the relationship between the input and the output is complicated, and the number of labeled training examples is large. But it is hard to explain why a learned network makes a…
Flexible tree ensemble learning framework supports arbitrary loss functions and multi-task learning.
Paper compares hard and soft EM for BN learning from incomplete data.
Integrates differentiable decision trees into neural networks for faster training and inference.
To deal with various datasets over different complexity, this paper presents an self-adaptive learning model that combines the proposed Dynamic Connected Neural Decision Networks (DNDN) and a new pruning method--Dynamic Soft Pruning (DSP). DNDN is a combination of random forests and deep neural networks that enjoys bot…
Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem. It is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features, which are massive-dimensional. We propose a multiscale dic…
Framework learns stochastic dynamics from endpoint and intermediate distributions using soft energy constraints.
Tropical SVM tackles phylogenomics by classifying multi-locus data.
In this work, we argue that the implications of Pseudo and Quantum Random Number Generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These implications are yet to be explored in Soft Computing until this work. We use a CPU and a …
Neural Networks and Decision Trees: two popular techniques for supervised learning that are seemingly disconnected in their formulation and optimization method, have recently been combined in a single construct. The connection pivots on assembling an artificial Neural Network with nodes that allow for a gate-like funct…
DBDT uses deep boosting decision trees for fraud detection.
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
Proposes a method to improve hierarchical clustering using set-level structural priors.
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary va…
The Hierarchical Mixture of Experts (HME) is a well-known tree-based model for regression and classification, based on soft probabilistic splits. In its original formulation it was trained by maximum likelihood, and is therefore prone to over-fitting. Furthermore the maximum likelihood framework offers no natural metri…
Soft labeling impacts OOD detection in neural networks.
Developed a new thresholding method that connects soft and hard thresholding.
Framework explains how dual deep networks learn features from unlabeled data.
Soft cells fill space without gaps, derived from minimal surfaces and deformed using edge bending.
In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data,…
Introduces CStrees for modeling context-specific causal models from observational and interventional data.
Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid l…
New method improves stability of soft FQI for offline RL.
Generalizes soft noncommutative schemes to flag varieties.
Paper proposes a new loss function for conditional models using soft targets.
Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a recent work \cite{efroni2018beyond}, multiple-step greedy policies and their use in vanilla Policy Iteration algorithms were proposed and analy…
Introduces Soft-SVM for binary classification bridging logistic and SVM.
Deep model learns from labeled and unlabeled data for industrial soft sensing.
Improves deep neural networks using soft labels through alternating minimization.
The paper develops a multi-unit soft sensing model for virtual flow meters that improves few-shot learning.
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…