Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data

Andreas Maier; He Lyu; Huan Song; Huazhen Yang; Huolin Zeng; Junren Wang; Linchao He; Siming Bayer; Yong Chen; Zhirui Li

arxiv: 2605.03570 · v1 · submitted 2026-05-05 · 💻 cs.LG · cs.AI

Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data

He Lyu , Huolin Zeng , Junren Wang , Huazhen Yang , Linchao He , Yong Chen , Zhirui Li , Andreas Maier

show 2 more authors

Siming Bayer Huan Song

This is my paper

Pith reviewed 2026-05-07 16:54 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords multi-task learningorthogonal decompositionmultimodal fusiontransformershared representationstask-specific representationsimbalanced clinical datasurgical outcome prediction

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The pith

Enforcing geometric orthogonality between shared and task-specific subspaces improves multi-task clinical outcome prediction.

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

The paper introduces a multi-task framework that fuses multimodal clinical data in a Transformer and then decomposes the resulting patient representations into shared and task-specific subspaces. An orthogonality constraint is imposed to cut redundancy and prevent signals from one outcome from interfering with others. On a cohort of 12,430 surgical patients the method yields an average AUC of 87.5 percent and AUPRC of 37.2 percent while outperforming both standard tabular models and other multi-task approaches, with the largest gains appearing in the precision-recall metric that matters for rare events. A reader would care because clinical data are typically imbalanced and multimodal, so any reliable way to share information across related outcomes without negative transfer could produce more usable risk models.

Core claim

The authors claim that a unified Transformer augmented with Orthogonal Task Decomposition (OrthTD) can split learned patient representations into shared and task-specific subspaces, then enforce a geometric orthogonality constraint that reduces redundancy and isolates task-specific signals; this produces average AUC of 87.5 percent and AUPRC of 37.2 percent across four outcomes on 12,430 real surgical patients and consistently beats advanced tabular and multi-task baselines, especially on the imbalanced-data metric AUPRC.

What carries the argument

Orthogonal Task Decomposition (OrthTD), the module that decomposes patient representations into shared and task-specific subspaces and applies a geometric orthogonality constraint to minimize overlap and isolate outcome-specific information.

If this is right

Multi-task models become less prone to negative transfer when task gradients conflict on related clinical outcomes.
Gains concentrate in AUPRC, showing better detection of rare events without sacrificing overall accuracy.
Information sharing across outcomes occurs more efficiently because redundant signals are geometrically suppressed.
The same decomposition pattern could be applied to any set of jointly predicted multimodal medical endpoints.

Where Pith is reading between the lines

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

The same orthogonality idea could be tried in non-clinical multi-task domains such as joint prediction of text and image labels.
If the fixed constraint sometimes removes useful shared features, an adaptive or soft version of the orthogonality term might restore performance.
Wider use in hospitals could allow one model to flag multiple postoperative complications at once, reducing the need for separate per-outcome systems.

Load-bearing premise

The assumption that a geometric orthogonality constraint on the subspaces will reliably separate task-specific signals from shared ones without discarding useful shared information or creating optimization artifacts in real clinical data.

What would settle it

If a model that performs the same multimodal fusion but omits the orthogonality constraint reaches equal or higher AUPRC on the identical 12,430-patient cohort, the claimed benefit of the constraint would be falsified.

Figures

Figures reproduced from arXiv: 2605.03570 by Andreas Maier, He Lyu, Huan Song, Huazhen Yang, Huolin Zeng, Junren Wang, Linchao He, Siming Bayer, Yong Chen, Zhirui Li.

**Figure 1.** Figure 1: Overview of the Orthogonal Task Decomposition (OrthTD) framework. The figure is composed of two parts. Part 1 (Framework Overview) illustrates view at source ↗

**Figure 2.** Figure 2: Detailed performance of the proposed model. view at source ↗

**Figure 3.** Figure 3: Performance in the ablation study of the proposed model. view at source ↗

read the original abstract

Real-world clinical data is inherently multimodal, providing complementary evidence that mirrors the practical necessity of jointly assessing multiple related outcomes. Although multi-task learning can improve efficiency by sharing information across outcomes, existing approaches often fail to balance shared representation learning with outcome-specific modeling. Hard parameter sharing can trigger negative transfer when task gradients conflict, while flexible sharing may still entangle shared and task-specific signals. To address this, we propose a multi-task framework built on a unified Transformer for multimodal fusion, augmented with Orthogonal Task Decomposition (OrthTD) to split patient representations into shared and task-specific subspaces and impose a geometric orthogonality constraint to reduce redundancy and isolate task-specific signals. We evaluated OrthTD on a real-world cohort of 12,430 surgical patients for predicting four outcomes. OrthTD achieved average AUC (area under the receiver operating characteristic curve) of 87.5% and average AUPRC (area under the precision-recall curve) of 37.2%, consistently outperformed advanced tabular and multi-task methods. Notably, OrthTD achieves substantial gains in AUPRC, indicating superior performance in identifying rare events within imbalanced clinical data. These results suggest that enforcing non-redundant shared and task-specific representations can improve multi-outcome prediction from multimodal clinical data.

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

OrthTD is a straightforward multimodal Transformer plus explicit orthogonality constraint that delivers measurable AUPRC gains on imbalanced surgical outcomes, but the abstract leaves the exact contribution of the constraint and the baseline details thin.

read the letter

The paper's core move is to fuse multimodal clinical inputs with a single Transformer and then split the patient embedding into shared and task-specific subspaces under a geometric orthogonality penalty. That is a clean architectural choice compared with plain hard or soft parameter sharing, and it targets the negative-transfer problem directly. On the 12,430-patient surgical cohort they report average AUC 87.5 % and AUPRC 37.2 % across four outcomes, with the biggest lift on the precision-recall side, which is the right metric for rare events. The cohort size and the focus on real-world imbalance are both practical strengths. The orthogonality step is presented as new relative to recent multi-task clinical work, and the abstract does not simply restate prior results. What is less clear is how much of the reported lift comes from the orthogonality constraint itself versus the Transformer backbone or other modeling choices. The abstract gives no ablation numbers, no statistical significance tests, and no description of the exact baselines or hyper-parameter search, so it is hard to judge whether the gains are robust or sensitive to implementation details. The evaluation is also limited to one surgical population and four specific outcomes, which narrows the claim. This is the kind of paper that clinical-ML groups already running multi-task or multimodal models would want to read and try to reproduce. It is not a foundational result, but the engineering is honest and the numbers are concrete enough to be worth checking. I would send it to peer review; a referee can ask for the missing ablations and significance tests without the paper being fundamentally broken.

Referee Report

2 major / 2 minor

Summary. The paper proposes Orthogonal Task Decomposition (OrthTD), a multi-task framework built on a multimodal Transformer that splits patient representations into shared and task-specific subspaces and enforces a geometric orthogonality constraint to reduce redundancy. On a real-world cohort of 12,430 surgical patients, OrthTD is evaluated for predicting four clinical outcomes and reports average AUC of 87.5% and average AUPRC of 37.2%, claiming consistent outperformance over advanced tabular and multi-task baselines with particular gains in AUPRC for imbalanced data.

Significance. If the reported gains prove robust, OrthTD could advance multi-task clinical prediction by offering a geometric mechanism to mitigate negative transfer and better isolate task-specific signals in multimodal data. The emphasis on AUPRC improvements is relevant for rare-event detection in healthcare, where class imbalance is common, and the approach may generalize to other multi-outcome settings.

major comments (2)

[Abstract and §4] Abstract and §4 (Experiments): The abstract and results claim consistent outperformance with specific AUC/AUPRC numbers, but provide no details on the exact baselines (e.g., which multi-task methods), hyperparameter tuning protocol, data splits, or statistical significance tests (p-values, confidence intervals). This makes it impossible to determine whether the gains are attributable to the orthogonality constraint or to other modeling choices.
[§3.2] §3.2 (OrthTD method): The geometric orthogonality constraint is presented as reliably isolating task-specific signals without discarding useful shared information, yet the manuscript lacks ablation studies (e.g., with vs. without the constraint) or analysis of subspace overlap/correlation to support this assumption. Without such evidence, the central mechanism remains unverified.

minor comments (2)

[Abstract] The abstract mentions 'advanced tabular and multi-task methods' without naming them; a table listing all baselines with references would improve clarity.
[§3] Notation for the orthogonality loss or projection operators should be defined explicitly in the methods section with an equation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): The abstract and results claim consistent outperformance with specific AUC/AUPRC numbers, but provide no details on the exact baselines (e.g., which multi-task methods), hyperparameter tuning protocol, data splits, or statistical significance tests (p-values, confidence intervals). This makes it impossible to determine whether the gains are attributable to the orthogonality constraint or to other modeling choices.

Authors: We agree that the current level of detail is insufficient to allow readers to fully assess reproducibility and attribute performance gains specifically to the orthogonality constraint. In the revised manuscript we will expand both the abstract and §4 to specify the exact baseline methods (including the particular multi-task and tabular approaches), the hyperparameter tuning protocol and search ranges, the patient-level data splitting procedure, and the results of statistical significance tests (paired t-tests with p-values and 95% confidence intervals on the AUC and AUPRC differences). revision: yes
Referee: [§3.2] §3.2 (OrthTD method): The geometric orthogonality constraint is presented as reliably isolating task-specific signals without discarding useful shared information, yet the manuscript lacks ablation studies (e.g., with vs. without the constraint) or analysis of subspace overlap/correlation to support this assumption. Without such evidence, the central mechanism remains unverified.

Authors: We concur that direct empirical verification of the orthogonality constraint is necessary. The present manuscript demonstrates overall gains but does not isolate the contribution of the constraint. We will add to §3.2 and §4 an ablation comparing OrthTD with and without the orthogonality term, together with quantitative analysis of subspace overlap (cosine similarity and correlation between the shared and task-specific representations) to confirm reduced redundancy while retaining useful shared information. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents OrthTD as an architectural innovation: a Transformer-based multi-task model augmented with an orthogonality constraint on shared and task-specific subspaces. All load-bearing claims are empirical (AUC 87.5%, AUPRC 37.2% on the 12,430-patient cohort, outperforming baselines). No equations derive a target quantity from fitted parameters that are themselves defined by that quantity; the orthogonality is imposed by design rather than recovered from data or prior self-referential results. No self-citation chains, uniqueness theorems, or ansatzes are invoked to justify the core decomposition. The method is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unproven premise that geometric orthogonality cleanly separates shared versus task-specific clinical signals; no free parameters or invented entities are named in the abstract.

axioms (1)

domain assumption Orthogonality constraint on subspaces reduces redundancy and isolates task-specific signals
Invoked to justify the OrthTD module; treated as a geometric property that improves representation quality.

pith-pipeline@v0.9.0 · 8602 in / 1204 out tokens · 44566 ms · 2026-05-07T16:54:40.577837+00:00 · methodology

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discussion (0)

Reference graph

Works this paper leans on

22 extracted references

[1]

Big data and machine learning algorithms for health-care delivery,

K. Y . Ngiam and I. W. Khor, “Big data and machine learning algorithms for health-care delivery,”The Lancet Oncology, vol. 20, no. 5, pp. e262– e273, 2019

2019
[2]

Combining clinical notes with structured electronic health records enhances the prediction of mental health crises,

R. Garriga, T. S. Buda, J. Guerreiro, J. Oma ˜na Iglesias, I. Estella Aguerri, and A. Mati ´c, “Combining clinical notes with structured electronic health records enhances the prediction of mental health crises,”Cell Reports Medicine, vol. 4, no. 11, 2023

2023
[3]

Artificial intelligence in surgery,

C. Varghese, E. M. Harrison, G. O’Grady, and E. J. Topol, “Artificial intelligence in surgery,”Nature Medicine, vol. 30, no. 5, pp. 1257–1268, 2024

2024
[4]

Multi-task learning for medical foundation models,

J. Yang, “Multi-task learning for medical foundation models,”Nature Computational Science, vol. 4, no. 7, pp. 473–474, 2024

2024
[5]

From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization,

M. Xue, J. Yang, H. Wang, Z. Yan, X. Chen, W. Gao, R. Luo, X. Lv, and Z. Ye, “From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization,”Journal of Anesthesia and Translational Medicine, vol. 4, no. 3, pp. 132–141, 2025

2025
[6]

Multimodal deep learning for biomedical data fusion: a review,

S. R. Stahlschmidt, B. Ulfenborg, and J. Synnergren, “Multimodal deep learning for biomedical data fusion: a review,”Briefings in bioinformat- ics, vol. 23, no. 2, p. bbab569, 2022

2022
[7]

A survey on multi-task learning,

Y . Zhang and Q. Yang, “A survey on multi-task learning,”IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5586–5609, 2022

2022
[8]

Cross-stitch Net- works for Multi-task Learning,

I. Misra, A. Shrivastava, A. Gupta, and M. Hebert, “Cross-stitch Net- works for Multi-task Learning,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3994– 4003

2016
[9]

Modeling task relationships in multi-task learning with multi-gate mixture-of- experts,

J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of- experts,” inProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 1930–1939

2018
[10]

Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,

A. Kendall, Y . Gal, and R. Cipolla, “Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

2018
[11]

Cohort profile: the China surgery and anesthesia cohort (CSAC),

L. Yang, W. Chen, D. Chen, J. He, J. Wang, Y . Qu, Y . Yang, Y . Tang, H. Zeng, W. Deng, H. Liu, L. Huang, X. Li, L. Du, J. Liu, Q. Li, and H. Song, “Cohort profile: the China surgery and anesthesia cohort (CSAC),”European Journal of Epidemiology, vol. 39, no. 2, pp. 207– 218, 2024

2024
[12]

Jammer, N

I. Jammer, N. Wickboldt, M. Sander, A. Smith, M. J. Schultz, P. Pelosi, B. Leva, A. Rhodes, A. Hoeft, B. Walder, M. S. Chew, and R. M. Pearse, “Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions: a statement from the ESA-ESICM joint...

2015
[13]

Attention is all you need,

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” inAdvances in Neural Information Processing Systems, 2017, pp. 5999–6009

2017
[14]

BERT: Pre-training of deep bidirectional transformers for language understanding,

J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” inPro- ceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, 2019, pp. 4171–4186

2019
[15]

Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review,

E. M. Wesselink, T. H. Kappen, H. M. Torn, A. J. Slooter, and W. A. van Klei, “Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review,”British Journal of Anaesthesia, vol. 121, no. 4, pp. 706–721, 2018

2018
[16]

Asymmetric loss for multi-label classification,

T. Ridnik, E. Ben-Baruch, N. Zamir, A. Noy, I. Friedman, M. Protter, and L. Zelnik-Manor, “Asymmetric loss for multi-label classification,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 82–91

2021
[17]

Pytorch: An im- perative style, high-performance deep learning library,

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, and L. Antiga, “Pytorch: An im- perative style, high-performance deep learning library,” inAdvances in neural information processing systems, 2019

2019
[18]

Decoupled Weight Decay Regularization,

I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization,” inInternational Conference on Learning Representations, 2017

2017
[19]

Lightgbm: A highly efficient gradient boosting decision tree,

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y . Liu, “Lightgbm: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, 2017

2017
[20]

Xgboost: A scalable tree boosting system,

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” inProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794

2016
[21]

Accurate predictions on small data with a tabular foundation model,

N. Hollmann, S. M ¨uller, L. Purucker, A. Krishnakumar, M. K¨orfer, S. B. Hoo, R. T. Schirrmeister, and F. Hutter, “Accurate predictions on small data with a tabular foundation model,”Nature, vol. 637, no. 8045, pp. 319–326, 2025

2025
[22]

Revisiting deep learning models for tabular data,

Y . Gorishniy, I. Rubachev, V . Khrulkov, and A. Babenko, “Revisiting deep learning models for tabular data,” inAdvances in Neural Informa- tion Processing Systems, 2021, pp. 18 932–18 943

2021

[1] [1]

Big data and machine learning algorithms for health-care delivery,

K. Y . Ngiam and I. W. Khor, “Big data and machine learning algorithms for health-care delivery,”The Lancet Oncology, vol. 20, no. 5, pp. e262– e273, 2019

2019

[2] [2]

Combining clinical notes with structured electronic health records enhances the prediction of mental health crises,

R. Garriga, T. S. Buda, J. Guerreiro, J. Oma ˜na Iglesias, I. Estella Aguerri, and A. Mati ´c, “Combining clinical notes with structured electronic health records enhances the prediction of mental health crises,”Cell Reports Medicine, vol. 4, no. 11, 2023

2023

[3] [3]

Artificial intelligence in surgery,

C. Varghese, E. M. Harrison, G. O’Grady, and E. J. Topol, “Artificial intelligence in surgery,”Nature Medicine, vol. 30, no. 5, pp. 1257–1268, 2024

2024

[4] [4]

Multi-task learning for medical foundation models,

J. Yang, “Multi-task learning for medical foundation models,”Nature Computational Science, vol. 4, no. 7, pp. 473–474, 2024

2024

[5] [5]

From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization,

M. Xue, J. Yang, H. Wang, Z. Yan, X. Chen, W. Gao, R. Luo, X. Lv, and Z. Ye, “From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization,”Journal of Anesthesia and Translational Medicine, vol. 4, no. 3, pp. 132–141, 2025

2025

[6] [6]

Multimodal deep learning for biomedical data fusion: a review,

S. R. Stahlschmidt, B. Ulfenborg, and J. Synnergren, “Multimodal deep learning for biomedical data fusion: a review,”Briefings in bioinformat- ics, vol. 23, no. 2, p. bbab569, 2022

2022

[7] [7]

A survey on multi-task learning,

Y . Zhang and Q. Yang, “A survey on multi-task learning,”IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5586–5609, 2022

2022

[8] [8]

Cross-stitch Net- works for Multi-task Learning,

I. Misra, A. Shrivastava, A. Gupta, and M. Hebert, “Cross-stitch Net- works for Multi-task Learning,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3994– 4003

2016

[9] [9]

Modeling task relationships in multi-task learning with multi-gate mixture-of- experts,

J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of- experts,” inProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 1930–1939

2018

[10] [10]

Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,

A. Kendall, Y . Gal, and R. Cipolla, “Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

2018

[11] [11]

Cohort profile: the China surgery and anesthesia cohort (CSAC),

L. Yang, W. Chen, D. Chen, J. He, J. Wang, Y . Qu, Y . Yang, Y . Tang, H. Zeng, W. Deng, H. Liu, L. Huang, X. Li, L. Du, J. Liu, Q. Li, and H. Song, “Cohort profile: the China surgery and anesthesia cohort (CSAC),”European Journal of Epidemiology, vol. 39, no. 2, pp. 207– 218, 2024

2024

[12] [12]

Jammer, N

I. Jammer, N. Wickboldt, M. Sander, A. Smith, M. J. Schultz, P. Pelosi, B. Leva, A. Rhodes, A. Hoeft, B. Walder, M. S. Chew, and R. M. Pearse, “Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions: a statement from the ESA-ESICM joint...

2015

[13] [13]

Attention is all you need,

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” inAdvances in Neural Information Processing Systems, 2017, pp. 5999–6009

2017

[14] [14]

BERT: Pre-training of deep bidirectional transformers for language understanding,

J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” inPro- ceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, 2019, pp. 4171–4186

2019

[15] [15]

Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review,

E. M. Wesselink, T. H. Kappen, H. M. Torn, A. J. Slooter, and W. A. van Klei, “Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review,”British Journal of Anaesthesia, vol. 121, no. 4, pp. 706–721, 2018

2018

[16] [16]

Asymmetric loss for multi-label classification,

T. Ridnik, E. Ben-Baruch, N. Zamir, A. Noy, I. Friedman, M. Protter, and L. Zelnik-Manor, “Asymmetric loss for multi-label classification,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 82–91

2021

[17] [17]

Pytorch: An im- perative style, high-performance deep learning library,

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, and L. Antiga, “Pytorch: An im- perative style, high-performance deep learning library,” inAdvances in neural information processing systems, 2019

2019

[18] [18]

Decoupled Weight Decay Regularization,

I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization,” inInternational Conference on Learning Representations, 2017

2017

[19] [19]

Lightgbm: A highly efficient gradient boosting decision tree,

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y . Liu, “Lightgbm: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, 2017

2017

[20] [20]

Xgboost: A scalable tree boosting system,

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” inProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794

2016

[21] [21]

Accurate predictions on small data with a tabular foundation model,

N. Hollmann, S. M ¨uller, L. Purucker, A. Krishnakumar, M. K¨orfer, S. B. Hoo, R. T. Schirrmeister, and F. Hutter, “Accurate predictions on small data with a tabular foundation model,”Nature, vol. 637, no. 8045, pp. 319–326, 2025

2025

[22] [22]

Revisiting deep learning models for tabular data,

Y . Gorishniy, I. Rubachev, V . Khrulkov, and A. Babenko, “Revisiting deep learning models for tabular data,” inAdvances in Neural Informa- tion Processing Systems, 2021, pp. 18 932–18 943

2021