Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability

• URL: http://arxiv.org/abs/2601.00655v2
• Date: Tue, 06 Jan 2026 15:21:04 GMT
• Title: Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability
• Authors: Kasra Fouladi, Hamta Rahmani,
• Abstract summary: IGBO encodes feature importance hierarchies as a Directed Acyclic Graph (DAG)<n>Central Limit Theorem-based construction of DAG ensures statistical validity of edge orientation decisions.<n>IGBO's effectiveness in enforcing DAG constraints with minimal accuracy loss, outperforming standard regularization baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper introduces Interpretability-Guided Bi-objective Optimization (IGBO), a framework that trains interpretable models by incorporating structured domain knowledge via a bi-objective formulation. IGBO encodes feature importance hierarchies as a Directed Acyclic Graph (DAG) via Central Limit Theorem-based construction and uses Temporal Integrated Gradients (TIG) to measure feature importance. To address the Out-of-Distribution (OOD) problem in TIG computation, we propose an Optimal Path Oracle that learns data-manifold-aware integration paths. Theoretical analysis establishes convergence properties via a geometric projection mapping $\mathcal{P}$ and proves robustness to mini-batch noise. Central Limit Theorem-based construction of the interpretability DAG ensures statistical validity of edge orientation decisions. Empirical results on time-series data demonstrate IGBO's effectiveness in enforcing DAG constraints with minimal accuracy loss, outperforming standard regularization baselines.

Related papers

• On Multi-Step Theorem Prediction via Non-Parametric Structural Priors [50.16583672681106]
  In this work, we explore training-free theorem prediction through the lens of in-context learning (ICL)<n>We propose Theorem Precedence Graphs, which encode temporal dependencies from historical solution traces as directed graphs, and impose explicit topological constraints that effectively prune the search space during inference.<n>Experiments on the FormalGeo7k benchmark show that our method achieves 89.29% accuracy, substantially outperforming ICL baselines and matching state-of-the-art supervised models.
  arXiv  Detail & Related papers  (2026-03-05T06:08:50Z)
• Pruning for Generalization: A Transfer-Oriented Spatiotemporal Graph Framework [0.6435984242701042]
  We propose TL-GPSGNT to improve graph-based time series forecasting models.<n>We use information-theoretic and correlation-based criteria to extract structurally informative subgraphs.<n>We show that TL-GPSGNT consistently outperforms baselines in low-data transfer scenarios.
  arXiv  Detail & Related papers  (2026-02-04T02:41:29Z)
• Efficient Causal Structure Learning via Modular Subgraph Integration [4.803851977437455]
  We introduce VISTA, a modular framework that decomposes the global causal structure learning problem into local subgraphs based on Blankets.<n>The framework is model-agnostic, imposing no assumptions on the inductive biases of base learners, is compatible with arbitrary data settings, and fully supports parallelization.<n>Extensive experiments on both synthetic and real datasets consistently demonstrate the effectiveness of VISTA.
  arXiv  Detail & Related papers  (2026-01-28T20:13:20Z)
• The Procrustean Bed of Time Series: The Optimization Bias of Point-wise Loss [53.542743390809356]
  This paper aims to provide a first-principles analysis of the Expectation of Optimization Bias (EOB)<n>Our analysis reveals a fundamental paradigm paradox: the more deterministic and structured the time series, the more severe the bias by point-wise loss function.<n>We present a concrete solution that simultaneously achieves both principles via DFT or DWT.
  arXiv  Detail & Related papers  (2025-12-21T06:08:22Z)
• GLOW: Graph-Language Co-Reasoning for Agentic Workflow Performance Prediction [51.83437071408662]
  We propose GLOW, a unified framework for AW performance prediction.<n>GLOW combines the graph-structure modeling capabilities of GNNs with the reasoning power of LLMs.<n>Experiments on FLORA-Bench show that GLOW outperforms state-of-the-art baselines in prediction accuracy and ranking utility.
  arXiv  Detail & Related papers  (2025-12-11T13:30:46Z)
• Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning [1.3728301825671199]
  We propose a versatile framework that integrates Graph Structure Learning (GSL) with Temporal Graph Information Bottleneck (TGIB)<n>We design a novel two-step GSL-based structural enhancer to enrich and optimize node neighborhoods.<n>The TGIB refines the optimized graph by extending the information bottleneck principle to temporal graphs, regularizing both edges and features.
  arXiv  Detail & Related papers  (2025-08-20T17:13:19Z)
• Learning Causal Graphs at Scale: A Foundation Model Approach [28.966180222166766]
  We propose Attention-DAG (ADAG), a novel attention-mechanism-based architecture for learning multiple linear Structural Equation Models (SEMs)<n>ADAG learns the mapping from observed data to both graph structure and parameters via a nonlinear attention-based kernel.<n>We evaluate our proposed approach on benchmark synthetic datasets and find that ADAG achieves substantial improvements in both DAG learning accuracy and zero-shot inference efficiency.
  arXiv  Detail & Related papers  (2025-06-23T04:41:02Z)
• Binarization-Aware Adjuster: Bridging Continuous Optimization and Binary Inference in Edge Detection [0.0]
  Image edge detection (ED) faces a fundamental mismatch between training and inference.<n>In this paper, we propose a theoretical method to design a Binarization-Aware (BAA)<n>BAA explicitly incorporates binarization behavior into gradient-based optimization.
  arXiv  Detail & Related papers  (2025-06-14T11:56:44Z)
• Partial Transportability for Domain Generalization [56.37032680901525]
  Building on the theory of partial identification and transportability, this paper introduces new results for bounding the value of a functional of the target distribution.<n>Our contribution is to provide the first general estimation technique for transportability problems.<n>We propose a gradient-based optimization scheme for making scalable inferences in practice.
  arXiv  Detail & Related papers  (2025-03-30T22:06:37Z)
• Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph [18.06658040186476]
  We introduce a novel framework named the Bipartite and Complete Directed Graph Neural Network (BCGNN) Within BCGNN, observations and features are differentiated as two distinct node types, and the values of observed features are converted into attributed edges linking them. In parallel, the complete directed graph segment adeptly outlines and communicates the complex interdependencies among features.
  arXiv  Detail & Related papers  (2024-11-07T17:48:37Z)
• Discovering Dynamic Causal Space for DAG Structure Learning [64.763763417533]
  We propose a dynamic causal space for DAG structure learning, coined CASPER. It integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground truth DAG.
  arXiv  Detail & Related papers  (2023-06-05T12:20:40Z)
• Validation Diagnostics for SBI algorithms based on Normalizing Flows [55.41644538483948]
  This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. This work should help the design of better specified models or drive the development of novel SBI-algorithms.
  arXiv  Detail & Related papers  (2022-11-17T15:48:06Z)
• BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery [97.79015388276483]
  A structural equation model (SEM) is an effective framework to reason over causal relationships represented via a directed acyclic graph (DAG) Recent advances enabled effective maximum-likelihood point estimation of DAGs from observational data. We propose BCD Nets, a variational framework for estimating a distribution over DAGs characterizing a linear-Gaussian SEM.
  arXiv  Detail & Related papers  (2021-12-06T03:35:21Z)

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.