Learning Ensembles of Interpretable Simple Structure

• URL: http://arxiv.org/abs/2502.19602v1
• Date: Wed, 26 Feb 2025 22:37:51 GMT
• Title: Learning Ensembles of Interpretable Simple Structure
• Authors: Gaurav Arwade, Sigurdur Olafsson,
• Abstract summary: In operations research applications, understanding how a decision is made is often as crucial as the decision itself.<n>Traditional interpretable models, such as decision trees and logistic regression, provide transparency but may struggle with datasets containing intricate feature interactions.<n>We propose a bottom-up simple structure-identifying algorithm that partitions data into interpretable subgroups known as simple structure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision-making in complex systems often relies on machine learning models, yet highly accurate models such as XGBoost and neural networks can obscure the reasoning behind their predictions. In operations research applications, understanding how a decision is made is often as crucial as the decision itself. Traditional interpretable models, such as decision trees and logistic regression, provide transparency but may struggle with datasets containing intricate feature interactions. However, complexity in decision-making stem from interactions that are only relevant within certain subsets of data. Within these subsets, feature interactions may be simplified, forming simple structures where simple interpretable models can perform effectively. We propose a bottom-up simple structure-identifying algorithm that partitions data into interpretable subgroups known as simple structure, where feature interactions are minimized, allowing simple models to be trained within each subgroup. We demonstrate the robustness of the algorithm on synthetic data and show that the decision boundaries derived from simple structures are more interpretable and aligned with the intuition of the domain than those learned from a global model. By improving both explainability and predictive accuracy, our approach provides a principled framework for decision support in applications where model transparency is essential.

Related papers

• Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction [55.914891182214475]
  We introduce neural network reprogrammability as a unifying framework for model adaptation.<n>We present a taxonomy that categorizes such information manipulation approaches across four key dimensions.<n>We also analyze remaining technical challenges and ethical considerations.
  arXiv  Detail & Related papers  (2025-06-05T05:42:27Z)
• Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures [49.19753720526998]
  We derive theoretical scaling laws for neural network performance on synthetic datasets.<n>We validate that convolutional networks, whose structure aligns with that of the generative process through locality and weight sharing, enjoy a faster scaling of performance.<n>This finding clarifies the architectural biases underlying neural scaling laws and highlights how representation learning is shaped by the interaction between model architecture and the statistical properties of data.
  arXiv  Detail & Related papers  (2025-05-11T17:44:14Z)
• On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games [55.2480439325792]
  In a sequential decision-making problem, the information structure is the description of how events in the system occurring at different points in time affect each other. By contrast, real-world sequential decision-making problems typically involve a complex and time-varying interdependence of system variables. We formalize a novel reinforcement learning model which explicitly represents the information structure.
  arXiv  Detail & Related papers  (2024-03-01T21:28:19Z)
• Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models [5.317624228510748]
  Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. We present the first principled approach for mining structural motifs in lifted graphical models. We show that we outperform state-of-the-art structure learning approaches by up to 6% in terms of accuracy and up to 80% in terms of runtime.
  arXiv  Detail & Related papers  (2023-02-09T12:21:55Z)
• Discrete Latent Structure in Neural Networks [32.41642110537956]
  This text explores three broad strategies for learning with discrete latent structure. We show how most consist of the same small set of fundamental building blocks, but use them differently, leading to substantially different applicability and properties.
  arXiv  Detail & Related papers  (2023-01-18T12:30:44Z)
• Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics [20.953728061894044]
  Well-calibrated probabilistic regression models are a crucial learning component in robotics applications as datasets grow rapidly and tasks become more complex. We consider a probabilistic hierarchical modeling paradigm that combines the benefits of both worlds to deliver computationally efficient representations with inherent complexity regularization. We derive two efficient variational inference techniques to learn these representations and highlight the advantages of hierarchical infinite local regression models.
  arXiv  Detail & Related papers  (2022-11-02T13:54:07Z)
• Dynamic Latent Separation for Deep Learning [67.62190501599176]
  A core problem in machine learning is to learn expressive latent variables for model prediction on complex data. Here, we develop an approach that improves expressiveness, provides partial interpretation, and is not restricted to specific applications.
  arXiv  Detail & Related papers  (2022-10-07T17:56:53Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• On Neural Architecture Inductive Biases for Relational Tasks [76.18938462270503]
  We introduce a simple architecture based on similarity-distribution scores which we name Compositional Network generalization (CoRelNet) We find that simple architectural choices can outperform existing models in out-of-distribution generalizations.
  arXiv  Detail & Related papers  (2022-06-09T16:24:01Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• TAGPRIME: A Unified Framework for Relational Structure Extraction [71.88926365652034]
  TAGPRIME is a sequence tagging model that appends priming words about the information of the given condition to the input text. With the self-attention mechanism in pre-trained language models, the priming words make the output contextualized representations contain more information about the given condition. Extensive experiments and analyses on three different tasks that cover ten datasets across five different languages demonstrate the generality and effectiveness of TAGPRIME.
  arXiv  Detail & Related papers  (2022-05-25T08:57:46Z)
• Parsimonious Inference [0.0]
  Parsimonious inference is an information-theoretic formulation of inference over arbitrary architectures. Our approaches combine efficient encodings with prudent sampling strategies to construct predictive ensembles without cross-validation.
  arXiv  Detail & Related papers  (2021-03-03T04:13:14Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.