Finding Multiple Interpretations in Datasets

Matthew Chak; Paul Anderson

arxiv: 2606.12277 · v1 · pith:HF5CGZNLnew · submitted 2026-06-10 · 💻 cs.LG

Finding Multiple Interpretations in Datasets

Matthew Chak , Paul Anderson This is my paper

Pith reviewed 2026-06-27 10:06 UTC · model grok-4.3

classification 💻 cs.LG

keywords machine learningmodel interpretabilitymultiple modelsfeature selectiongene expressionMETABRIC datasetcontext-aware characteristicsmodel diversity

0 comments

The pith

A method exists to identify multiple machine learning models that match in accuracy but differ substantially in the features they rely on.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that one can systematically locate groups of models with comparable loss or accuracy yet highly distinct internal characteristics, such as which input variables they emphasize. This matters for any analysis that uses a model to understand the data-generating process rather than merely to make predictions. In the reported experiments the method recovers models that select different gene expressions from a breast-cancer dataset while preserving performance levels. A sympathetic reader would conclude that the single best model is rarely the only informative one and that deliberate search for alternatives yields additional views of the same phenomenon.

Core claim

The authors claim that an explicit search procedure can return collections of models whose predictive performance is statistically indistinguishable yet whose context-aware characteristics, measured by the gene expressions they select, differ markedly from those recovered by standard single-model training; this is demonstrated on the METABRIC dataset without incurring performance penalties, thereby supporting the broader argument that global model characteristics can be mined for multiple insights into the studied phenomenon.

What carries the argument

The proposed search procedure that enumerates sets of similar-performing models while maximizing differences in their context-aware characteristics.

If this is right

Multiple models with non-overlapping gene selections can be recovered at the same performance level achieved by conventional training.
Analysis of global model properties can surface distinct interpretations of the same dataset.
The procedure applies whenever the goal is to understand the phenomenon rather than to deploy a single predictor.
Standard single-model pipelines are shown to miss alternative high-performing explanations that exist in the data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same search logic could be applied to tabular or image datasets outside genomics to surface alternative explanatory feature sets.
Model-selection protocols might usefully add a diversity criterion alongside accuracy when the downstream task involves scientific interpretation.
Quantifying a minimum distance between characteristic vectors could turn the method into a practical tool for enumerating distinct explanations.

Load-bearing premise

Observed differences in selected gene expressions correspond to meaningfully distinct model behaviors rather than incidental or superficial variations.

What would settle it

A replication on the METABRIC dataset in which every high-performing model recovered by the method selects essentially the same gene expressions as the control, or in which any diversity found is accompanied by measurable performance loss, would falsify the central claim.

read the original abstract

In this paper, we propose an approach to finding sets of similar-performing models (in terms of loss/accuracy measurements) with highly different context-aware characteristics. Through experiments on the METABRIC dataset, we show that the proposed method finds multiple models with highly different gene expressions than those found by the control methodology without performance penalties. We argue that the proposed methodology is important whenever one aims to analyze any global characteristic of a model to extract insight into the underlying phenomenon being studied.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows a method to recover multiple similar-accuracy gene models on METABRIC but supplies no evidence that the gene differences produce distinct model behavior or new insight.

read the letter

The central point is that the authors describe a procedure for locating several models that achieve comparable loss or accuracy on the METABRIC breast-cancer gene-expression data yet select markedly different gene sets, and they claim their approach surfaces more such alternatives than a baseline without any accuracy cost.

It does a reasonable job of moving the discussion to a real genomic dataset instead of synthetic cases, and it correctly notes that single-model explanations can overlook alternative accounts of the same data.

The main weakness is that the work never connects the observed gene-set differences to any measurable difference in model behavior. The abstract equates divergent gene lists with "highly different context-aware characteristics," but it reports no checks on prediction divergence, subgroup performance shifts, decision-boundary changes, or external biological validation. Without those links, the result could simply reflect multiple sparse solutions to the same underlying signal rather than genuinely distinct interpretations. Method details are also absent, so it is impossible to judge whether the procedure is a genuine advance or a routine extension of existing multi-objective feature selection.

This is aimed at researchers working on interpretability for high-dimensional scientific data. A reader already thinking about feature-selection instability in genomics could pick up the experimental framing.

The idea is worth referee time because the underlying concern is legitimate and the dataset choice is appropriate, even though the current evidence is thin. I would send it for review with the expectation that the authors add concrete validation that the recovered models differ in ways that affect predictions or yield separable insights.

Referee Report

2 major / 1 minor

Summary. The paper proposes an approach to identify sets of models achieving similar loss/accuracy but with highly different context-aware characteristics. Experiments on the METABRIC dataset claim to show that the method recovers multiple models with substantially different gene expression selections than a control methodology, without performance penalties. The authors argue this capability is valuable for analyzing global model properties to gain insight into the studied phenomenon.

Significance. If the central claim were substantiated with appropriate metrics and controls, the work could contribute to interpretability research by demonstrating the existence of multiple distinct interpretations in high-dimensional data. The focus on biomedical gene expression data is a reasonable test case. However, the absence of methodological details, quantitative validation of 'different context-aware characteristics,' and statistical rigor currently prevents any assessment of significance.

major comments (2)

[Abstract] Abstract: The claim that 'highly different gene expressions' reliably indicate 'highly different context-aware characteristics' is unsupported; no metric is supplied showing differences in predictions, decision boundaries, subgroup performance, or biological insight, leaving open the possibility that gene-set divergence is an artifact of the search procedure rather than evidence of multiple interpretations.
[Experiments] Experiments section: No description is given of the proposed method, the control methodology, data preprocessing, statistical tests, or the quantitative criterion used to declare gene expressions 'highly different,' rendering the reported outcomes on METABRIC unverifiable and the soundness of the central claim impossible to evaluate.

minor comments (1)

[Abstract] The abstract would benefit from a concise statement of the core algorithmic idea and the precise definition of 'context-aware characteristics.'

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback. We agree that the manuscript requires substantial clarification on methodological details and stronger quantitative support for the central claims. We will revise the paper to address these issues directly.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that 'highly different gene expressions' reliably indicate 'highly different context-aware characteristics' is unsupported; no metric is supplied showing differences in predictions, decision boundaries, subgroup performance, or biological insight, leaving open the possibility that gene-set divergence is an artifact of the search procedure rather than evidence of multiple interpretations.

Authors: We accept this criticism. The abstract overstates the link between gene-set differences and distinct context-aware characteristics without supporting evidence. In revision we will (1) tone down the abstract claim to focus on gene-expression divergence as an observable outcome, and (2) add explicit quantitative metrics (e.g., disagreement in predictions on a held-out test set, differences in subgroup performance, and decision-boundary distance) to the experiments section to demonstrate that the recovered models differ in their functional behavior beyond feature selection. revision: yes
Referee: [Experiments] Experiments section: No description is given of the proposed method, the control methodology, data preprocessing, statistical tests, or the quantitative criterion used to declare gene expressions 'highly different,' rendering the reported outcomes on METABRIC unverifiable and the soundness of the central claim impossible to evaluate.

Authors: We agree that the current manuscript lacks these essential details. In the revised version we will expand the Experiments section to include: a complete algorithmic description of the proposed method, the control baseline, all preprocessing steps applied to METABRIC, the statistical tests used, and the precise quantitative threshold or distance measure employed to declare two gene sets 'highly different.' These additions will make the results reproducible and allow direct evaluation of the claim. revision: yes

Circularity Check

0 steps flagged

No circularity detected; empirical claim with no derivation chain shown

full rationale

The abstract and provided text describe a proposed method for finding similar-performing models with different gene expressions on METABRIC, presented as an experimental result rather than a mathematical derivation. No equations, self-citations, fitted parameters renamed as predictions, or definitional equivalences are present. The central claim is an empirical demonstration of multiple models without performance penalties, which does not reduce to its inputs by construction. No load-bearing steps matching the enumerated circularity patterns can be identified from the given material.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no information on free parameters, axioms, or invented entities is present.

pith-pipeline@v0.9.1-grok · 5585 in / 995 out tokens · 29117 ms · 2026-06-27T10:06:16.084124+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

16 extracted references · 7 canonical work pages · 1 internal anchor

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Mathieu Blondel, Olivier Teboul, Quentin Berthet, and Josip Djolonga. 2020. Fast differentiable sorting and ranking. InInternational Conference on Machine Learning. PMLR, 950–959

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work page arXiv 2020

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Wonjoong Cheon, Mira Han, Seonghoon Jeong, Eun Sang Oh, Sung Uk Lee, Se Byeong Lee, Dongho Shin, Young Kyung Lim, Jong Hwi Jeong, Haksoo Kim, and Joo Young Kim. 2023. Feature Importance Analysis of a Deep Learning Model for Predicting Late Bladder Toxicity Occurrence in Uterine Cervical Cancer Patients. https://www.mdpi.com/2072-6694/15/13/3463

2023

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Andrew Cotter, Heinrich Jiang, and Karthik Sridharan. 2018. Two-Player Games for Efficient Non-Convex Constrained Optimization.CoRRabs/1804.06500 (2018). arXiv:1804.06500 http://arxiv.org/abs/1804.06500

work page internal anchor Pith review Pith/arXiv arXiv 2018

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Shah, Suet-Feung Chin, Gulisa Turashvili, Oscar M

C. Curtis, S.-P. Shah, S.-F. Chin, G. Turashvili, O. M. Rueda, M. J. Dunning, D. Speed, A. G. Lynch, S. Samarajiwa, Y. Yuan, S. Graf, G. Ha, G. Haffari, A. Bashashati, R. Russell, S. McKinney, METABRIC Group, et al . 2012. The Ge- nomic and Transcriptomic Architecture of 2,000 Breast Tumours Reveals Novel Subgroups.Nature486, 7403 (2012), 346–352. doi:10....

work page doi:10.1038/nature10983 2012

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Underspecification presents challenges for credibility in modern machine learning,

Alexander D’Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek ...

work page arXiv 2020

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Jose Gallego-Posada and Juan Ramirez. 2022. Cooper: a toolkit for Lagrangian- based constrained optimization. https://github.com/cooper-org/cooper

2022

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Gilbert Harman. 1965. The Inference to the Best Explanation.The Philosophical Review74, 1 (1965), 88–95. doi:10.2307/2182135

work page doi:10.2307/2182135 1965

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Peter Koo, Antonio Majdandzic, Matthew Ploenzke, Praveen Anand, and Steffan Paul. 2021. Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks. https://journals.plos. org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1008925

2021

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Xinlei Mi, Baiming Zou, Fei Zou, and Jianhua Hu. 2021. Permutation-based identification of important biomarkers for complex diseases via machine learning models. https://www.nature.com/articles/s41467-021-22756-2

2021

[13] [13]

Riccardo Miotto, Li Li, Brian Kidd, and Joel Dudley. 2016. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. https://www.nature.com/articles/srep26094

2016

[14] [14]

Nick Oh. 2024. In Defence of Post-hoc Explainability. https://arxiv.org/abs/2412. 17883

2024

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Walter Veit. 2019. Model Pluralism. arXiv preprint arXiv:1909.13653. https: //arxiv.org/abs/1909.13653

work page arXiv 2019

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Chenyu Wang, Chaoying Zuo, Zihan Su, Yuhang Xing, Lu Li, Maojun Wang, and Zeyu Zhang. 2025. Deep Learning and Explainable AI: New Pathways to Genetic Insights. https://arxiv.org/html/2505.09873v1

work page arXiv 2025