Error-controlled non-additive interaction discovery in machine learning models

• URL: http://arxiv.org/abs/2408.17016v1
• Date: Fri, 30 Aug 2024 05:13:11 GMT
• Title: Error-controlled non-additive interaction discovery in machine learning models
• Authors: Winston Chen, Yifan Jiang, William Stafford Noble, Yang Young Lu,
• Abstract summary: We introduce Diamond, a novel method for trustworthy feature interaction discovery. Diamond uniquely integrates the model-X knockoffs framework to control the false discovery rate (FDR) Our empirical evaluations on both simulated and real datasets demonstrate Diamond's utility in enabling more reliable data-driven scientific discoveries.
• Score: 8.248260569247595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) models are powerful tools for detecting complex patterns within data, yet their "black box" nature limits their interpretability, hindering their use in critical domains like healthcare and finance. To address this challenge, interpretable ML methods have been developed to explain how features influence model predictions. However, these methods often focus on univariate feature importance, overlooking the complex interactions between features that ML models are capable of capturing. Recognizing this limitation, recent efforts have aimed to extend these methods to discover feature interactions, but existing approaches struggle with robustness and error control, especially under data perturbations. In this study, we introduce Diamond, a novel method for trustworthy feature interaction discovery. Diamond uniquely integrates the model-X knockoffs framework to control the false discovery rate (FDR), ensuring that the proportion of falsely discovered interactions remains low. We further address the challenges of using off-the-shelf interaction importance measures by proposing a calibration procedure that refines these measures to maintain the desired FDR. Diamond's applicability spans a wide range of ML models, including deep neural networks, tree-based models, and factorization-based models. Our empirical evaluations on both simulated and real datasets across various biomedical studies demonstrate Diamond's utility in enabling more reliable data-driven scientific discoveries. This method represents a significant step forward in the deployment of ML models for scientific innovation and hypothesis generation.

Related papers

• Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing [41.89148096989836]
  We propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques.
  arXiv  Detail & Related papers  (2025-02-12T12:30:49Z)
• REX: Causal Discovery based on Machine Learning and Explainability techniques [0.13108652488669734]
  We introduce REX, a causal discovery method that leverages machine learning (ML) models coupled with explainability techniques, specifically Shapley values. REX outperforms state-of-the-art causal discovery methods across diverse data generation processes, including non-linear and additive noise models. REX bridges the gap between predictive modeling and causal inference, offering an effective tool for understanding complex causal structures.
  arXiv  Detail & Related papers  (2025-01-22T08:23:10Z)
• You are out of context! [0.0]
  New data can act as forces stretching, compressing, or twisting the geometric relationships learned by a model. We propose a novel drift detection methodology for machine learning (ML) models based on the concept of ''deformation'' in the vector space representation of data.
  arXiv  Detail & Related papers  (2024-11-04T10:17:43Z)
• Dissecting Fine-Tuning Unlearning in Large Language Models [12.749301272512222]
  Fine-tuning-based unlearning methods prevail for preventing harmful, sensitive, or copyrighted information within large language models. However, the true effectiveness of these methods is unclear. In this work, we delve into the limitations of fine-tuning-based unlearning through activation patching and restoration experiments.
  arXiv  Detail & Related papers  (2024-10-09T06:58:09Z)
• Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration [74.09687562334682]
  We introduce a novel training data attribution method called Debias and Denoise Attribution (DDA) Our method significantly outperforms existing approaches, achieving an averaged AUC of 91.64%. DDA exhibits strong generality and scalability across various sources and different-scale models like LLaMA2, QWEN2, and Mistral.
  arXiv  Detail & Related papers  (2024-10-02T07:14:26Z)
• Sparse Modelling for Feature Learning in High Dimensional Data [0.0]
  This paper presents an innovative approach to dimensionality reduction and feature extraction in high-dimensional datasets. The proposed framework integrates sparse modeling techniques into a comprehensive pipeline for efficient and interpretable feature selection. We aim to advance the understanding and application of sparse modeling in machine learning, particularly in the context of wood surface defect detection.
  arXiv  Detail & Related papers  (2024-09-28T14:17:59Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models [21.929902181609936]
  We propose a novel approach to integrate uncertainty-based active learning and LoRA. For the uncertainty gap, we introduce a dynamic uncertainty measurement that combines the uncertainty of the base model and the uncertainty of the full model. For poor model calibration, we incorporate the regularization method during LoRA training to keep the model from being over-confident.
  arXiv  Detail & Related papers  (2024-03-02T10:38:10Z)
• Beyond Convergence: Identifiability of Machine Learning and Deep Learning Models [0.0]
  We investigate the notion of model parameter identifiability through a case study focused on parameter estimation from motion sensor data. We employ a deep neural network to estimate subject-wise parameters, including mass, stiffness, and equilibrium leg length. The results show that while certain parameters can be identified from the observation data, others remain unidentifiable.
  arXiv  Detail & Related papers  (2023-07-21T03:40:53Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Bayesian Active Learning for Discrete Latent Variable Models [19.852463786440122]
  Active learning seeks to reduce the amount of data required to fit the parameters of a model. latent variable models play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines.
  arXiv  Detail & Related papers  (2022-02-27T19:07:12Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

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.