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.
VaSST uses soft symbolic trees for probabilistic symbolic regression.
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.
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.
Bayesian GBMs improve predictive uncertainty calibration for tabular data.
SBAMDT uses adaptive soft splits to model complex decision boundaries.
DeepSIC learns to detect multiple symbols in MIMO systems without assuming a specific channel model.
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. …
Develops a Bayesian framework for symbolic regression of scientific expressions.
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.
Paper proposes an algorithm to reconstruct optimal model structure from graph adjacency matrix.
Neural programming involves training neural networks to learn programs, mathematics, or logic from data. Previous works have failed to achieve good generalization performance, especially on problems and programs with high complexity or on large domains. This is because they mostly rely either on black-box function eval…
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.
GFN-SR uses deep learning to generate diverse mathematical expressions.
Data-driven symbol detection improves performance in complex channels.
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 …
Co-eye combines multiple symbolic representations to improve time series classification accuracy.
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…
This paper shows that scientific discovery can be efficiently learned via compositional function trees, reducing the sample complexity.
Bayesian symbolic regression uncovers missing physics from data with uncertainty quantification.
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…
In [9] Kaimanovich introduced the concept of augmented tree on the symbolic space of a self-similar set. It is hyperbolic in the sense of Gromov, and it was shown in [13] that under the open set condition, a self-similar set can be identified with the hyperbolic boundary of the tree. In the paper, we investigate in det…
Shape-constrained symbolic regression improves model extrapolation with prior knowledge.
Flexible tree ensemble learning framework supports arbitrary loss functions and multi-task learning.
NEMoTS improves time series analysis by deriving efficient, interpretable models.
Classifier evasion consists in finding for a given instance the nearest instance such that the classifier predictions of and are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixe…
Edit distance, also known as Levenshtein distance, is an essential way to compare two strings that proved to be particularly useful in the analysis of genetic sequences and natural language processing. However, edit distance is a discrete function that is known to be hard to optimize. This fact hampers the use of this …
The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Ge…
Paper compares hard and soft EM for BN learning from incomplete data.
EML-CD discovers causal mechanisms from neural networks in a structured way.
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than the…
While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinforcement learning by training decision tree policies, which can represent complex p…
Integrates differentiable decision trees into neural networks for faster training and inference.
Transformers learn multi-step reasoning through gradient descent.
Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as m…
Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as m…
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifier…
Given an iterated function system (IFS) of contractive similitudes, the theory of Gromov hyperbolic graph on the IFS has been established recently. In the paper, we introduce a notion of simple augmented tree which is a Gromov hyperbolic graph. By generalizing a combinatorial device of rearrangeable matrix, we show tha…
SPPL simplifies probabilistic programming for exact 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…
Probabilistic grammars improve equation discovery from data.