Pith Number

pith:GEWCVYRC

pith:2026:GEWCVYRCBADWTMA3ELBDH4NO4O

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A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature

Chaeyeon Lee, Hyungrok Do, Sehwan Kim

QSurv approximates cumulative hazards via Gauss-Legendre quadrature to enable scalable nonparametric continuous-time survival modeling in deep networks.

arxiv:2605.16208 v1 · 2026-05-15 · stat.ML · cs.LG

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Claims

C1strongest claim

We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation.

C2weakest assumption

Gauss-Legendre numerical quadrature approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation, without introducing bias that would affect model learning or predictions.

C3one line summary

QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.

References

48 extracted · 48 resolved · 4 Pith anchors

[1] A. Avati, T. Duan, S. Zhou, K. Jung, N. H. Shah, and A. Y . Ng. Countdown regression: Sharp and calibrated survival predictions. In R. P. Adams and V . Gogate, editors,Proceedings of The 35th Uncertai 2020

[2] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos. Advancing the cancer genome atlas glioma mri collections with expert segmentation 2017

[3] arXiv preprint arXiv:1811.02629 (2018) 2018 · arXiv:1811.02629

[4] A. Bennis, S. Mouysset, and M. Serrurier. Estimation of conditional mixture weibull distribution with right censored data using neural network for time-to-event analysis. InAdvances in Knowledge Disco 2020 · doi:10.1007/978-3-030-47426-3_53

[5] N. E. Breslow and N. Chatterjee. Design and analysis of two-phase studies with binary outcome applied to wilms tumour prognosis.Journal of the Royal Statistical Society: Series C (Applied Statistics), 1999

Formal links

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Receipt and verification

First computed	2026-05-20T00:01:58.114450Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

312c2ae222080769b01b22c233f1aee394324dd2873b0f5bc41e468b14a2114d

Aliases

arxiv: 2605.16208 · arxiv_version: 2605.16208v1 · doi: 10.48550/arxiv.2605.16208 · pith_short_12: GEWCVYRCBADW · pith_short_16: GEWCVYRCBADWTMA3 · pith_short_8: GEWCVYRC

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GEWCVYRCBADWTMA3ELBDH4NO4O \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 312c2ae222080769b01b22c233f1aee394324dd2873b0f5bc41e468b14a2114d

Canonical record JSON

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    "abstract_canon_sha256": "efe91c570509b25f6a23c92bd2266a303a6c54e04ddb2532ee54f5feee132d42",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-15T17:25:17Z",
    "title_canon_sha256": "857b71cb9d2a3715725ddc2ec41499c3295a8a5afe5305c51c9ec99a2b484604"
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    "kind": "arxiv",
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