Results support IAIML as a compact, interaction-aware framework appropriate for settings where bounded explanation size and controlled treatment of feature interactions are design requirements.
Abstract
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variables whose predictive value emerges only through joint configurations with other variables. We present Interaction Aware Interpretable Machine Learning (IAIML), a framework that addresses this limitation through three coordinated mechanisms: adaptive per-feature discretization, finite-grid pairwise interaction scoring, and a partitioned explanation budget. Detected interactions are routed through one of two strategies: relaxing the screening filter so that interaction-supported variables enter the pattern search, or constructing explicit pair terms for a sparse downstream classifier. On a 40-dataset panel comprising 24 real-world tabular benchmarks and 16 synthetic interaction stress tests, evaluated under nested cross-validation, IAIML achieves mean AUC within 1.4 points of tuned gradient-boosted ensembles while requiring roughly 14--28 times fewer fitted explanation components. On datasets with strong pairwise interaction structure and low marginal signal, IAIML outperforms all baselines. Among compact interpretable methods, IAIML is comparable to RuleFit in AUC and component count and is less expensive to tune. EBM obtains a small but significant AUC advantage across the full panel, with a substantially larger lookup-table footprint. Performance degrades on datasets requiring higher-order interactions beyond the pairwise scope. Component-isolated ablations confirm that adaptive discretization and interaction-aware admission each contribute incrementally. These results support IAIML as a compact, interaction-aware framework appropriate for settings where bounded explanation size and controlled treatment of feature interactions are design requirements.
These results validate FIG as a principled, interpretable diagnostic tool for algorithm selection in exact learning; its diagnostic relevance becomes apparent on harder instances where solver runtime separation is substantial.
Zhigao Huang, Miao Pan, Yuzhuo Pan et al.· Scientific Reports· 0 citations
Understanding which parameters are influential in Large Language Models (LLMs) is central to improving their efficiency, reliability, and interpretability. We introduce Weight-Adjusted Gradients (WAG), a simple yet effective approach for estimating parameter importance that explicitly captures the interaction between model weights and first-order gradient information and identifies parameters that disproportionately influence model behavior, such as those responsible for collapse phenomena in LLMs. Across a range of models and settings, we show that WAG surfaces a tiny but critical subset of parameters whose modification leads to dramatic degradation in performance, a failure mode that existing importance metrics overlook. These findings reveal a previously underexplored interplay between weights and gradients, suggesting that parameter importance cannot be fully understood through either signal alone. The surprising effectiveness of WAG points to fundamental structural properties of trained networks and motivates new open questions about the role of zeroth-order and first-order information in deep learning. We demonstrate the practical utility of WAG across multiple applications, including expert allocation in mixture-of-expert architectures, parameter-specific unlearning, mixed-precision quantization, and layer selection for knowledge editing. Our results position WAG as a unified approach for analyzing, debugging, and controlling LLMs, and opens new directions for principled model-level interpretation.
Tabular deep learning is still challenging in real-world settings. Many datasets include both numerical and categorical variables, substantial missingness, and a need for not only strong classification performance but also interpretability and reliable probability estimates. DA2-Net is proposed to address this problem through a dual-branch architecture. It combines an interpretable additive pathway for feature-wise main effects with a selective self-attention pathway for higher-order interactions. In this design, features are ranked using additive contribution magnitude, uncertainty, and missingness-aware scaling. Only a Top-K subset is then passed to a single multi-head self-attention block. The final prediction is obtained through uncertainty-aware gated fusion. The model is also supported by sparsity, stability, and Brier-based calibration regularization. This allows it to balance expressive interaction modeling with transparency and robustness under incomplete data. DA2-Net is evaluated on four public binary tabular benchmarks, namely AdultIncome, DefaultCredit, HeartDisease, and BankMarketing, under controlled Missing Completely At Random (MCAR) missingness levels of 0.0, 0.1, 0.2, and 0.3. The evaluation uses 5-fold stratified cross-validation repeated across three random seeds. This produces 15 runs for each dataset and missingness condition, and 128 evaluation blocks in total across AUC, AUPRC, ACC, F1, sensitivity, specificity, Brier score, and Expected Calibration Error (ECE). Across this benchmark, DA2-Net achieves the best overall mean rank with 3.078 ± 2.044, ahead of SAINT-Lite at 3.980 ± 2.624. It achieves or shares the best result in all 16 AUC blocks, 13 of 16 AUPRC blocks, 10 of 16 ACC blocks, 11 of 16 Brier blocks, and 7 of 16 ECE blocks. These results show that its main strength lies in robust ranking-based discrimination and strong overall probability quality under missingness. It also shows a favorable practical-efficiency profile in the current benchmark, remaining more compact and inference-efficient than the main transformer-like baselines. Epoch-wise loss analysis also shows stable convergence across all four datasets. The binary cross-entropy (BCE) term drives the optimization, while the auxiliary regularizers act as controlled refinements. The ablation study further confirms that the interaction branch is essential. Removing it in the AdditiveOnly variant causes the clearest degradation in both predictive and calibration metrics. In contrast, removing the gate or the auxiliary regularization terms leads only to minor changes. A sensitivity analysis also supported the selected interaction subset size k=10 and spline knot count K=8 as balanced settings, while additive shape-function visualizations provided direct qualitative evidence for feature-wise interpretability.
Mücahit Cihan· Gazi university journal of s...· 0 citations
The Normalised Sensitivity Ratio (NSR) is introduced, a post-hoc, model-agnostic diagnostic for this question under a structured-shift regime: environments differ primarily in the mean of spurious features while the causal mechanism and causal marginals remain stable, as in multi-site clinical data or multi-batch genomics.
Athanasios Vlontzos, G. Papanastasiou, Bernhard Kainz et al.· 0 citations
BeyondArena is the first unified holistic benchmark for tabular data that supports diverse task types (IID, temporal, grouped), across sample size and feature dimensionality scales, with diverse feature types from a broad range of disciplines.
Lennart Purucker, Andrej Tschalzev, Nick Erickson et al.· arXiv.org· 2 citations
The results suggest that current training-based methods do not yet extract insights beyond their training data, and motivate benchmarks that explicitly require interpretability methods to demonstrate such insights.