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Hierarchical Conformal Classification

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Optimal hierarchical conformal prediction can be restricted to non-overlapping leaf covers without sacrificing finite-sample coverage.

desk verdict A useful practical setup and benchmarks, but the central optimality claim does not hold as stated: Proposition 1's proof quantifies over all alpha while the objective fixes one, so the NOL-cover pruning is unsupported. read the letter →

arxiv 2508.13288 v1 pith:JKVOGWBO submitted 2025-08-18 cs.LG cs.AIstat.ML

Floris den Hengst , Inès Blin , Majid Mohammadi , Syed Ihtesham Hussain Shah , Taraneh Younesian This is my paper · ORCID
classification cs.LGcs.AIstat.ML
keywords conformalpredictionhierarchicalclassificationsetscoverageguaranteeclasstaxonomydirectedacyclicgraphuncertaintyquantification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Standard conformal prediction returns a flat list of class labels. HCC instead builds prediction sets from nodes at any level of a known class hierarchy, so a set can say "cat" rather than enumerate fine-grained breeds, while still guaranteeing that the true label is covered with probability at least $1-\alpha$. The paper shows that the combinatorial search over hierarchy subsets can be restricted to non-overlapping leaf covers without losing optimality or coverage, and proves a finite-sample hierarchical coverage guarantee for the resulting predictors. On three released benchmarks spanning text, image, and audio, the resulting sets are more compact, and annotators prefer them over flat conformal sets in a user study. The contribution is that domain structure is not extra weight on top of conformal prediction but the mechanism that makes its prediction sets understandable.

What carries the argument

The central object is the non-overlapping leaf cover (NOL-cover): a subset of the hierarchy's nodes that covers every leaf through descendants and contains no two nodes on the same root-to-leaf path. Two structural lemmas carry the argument: any feasible prediction set must cover all leaves, and any optimal set can be made ancestor-independent without increasing the objective, so the search over all $2^{|V|}$ subsets collapses to the NOL-covers. The accompanying machinery is score propagation—each internal node's score is the sum of its descendant leaves' scores, and the multi-label conformity score for a cover is the largest propagated score among the cover's ground-truth nodes—followed by the usual split-conformal quantile threshold $\lceil(n_c+1)(1-\alpha)\rceil/n_c$. In a perfect binary tree this reduces the search from $2^{2^d}$ candidate subsets to roughly $2^{d-1}$.

What would settle it

In a two-leaf hierarchy where one leaf has prior probability $0.01$, set $\alpha=0.05$ and a base classifier that always ranks the likely leaf first; if the feasible singleton set with the likely leaf has lower cost than every NOL-cover, then the claimed reduction does not solve the optimization as stated.

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Extended reading notes

Core claim

The paper's central claim is that the constrained optimization defining hierarchical conformal classification—minimize $|N|+\beta\,|\bigcup_{v\in N}\mathrm{leaf-cov}_T(\{v\})|$ subject to $\Pr(y_t\in\mathrm{leaf-cov}_T(N))\ge 1-\alpha$—can be solved by searching only over non-overlapping leaf covers: subsets of hierarchy nodes whose descendant leaves cover all leaves and that contain no ancestor–descendant pair. HCC calibrates one split-conformal predictor for each NOL-cover after propagating leaf scores upward as sums and marking all ancestors of the true leaf as ground truth. For DAGs, where one instance can have several true nodes inside a cover, the conformity score is the largest propagated score among the cover's ground-truth nodes, reducing to the usual single-label score when the cover is the flat leaf set. The paper proves that this construction satisfies $\Pr(y_t\in\mathrm{leaf-cov}_T(C(x_t)))\ge 1-\alpha$ for every $\alpha\in[0,1)$, and that feasible optimal solutions to the optimization need only be sought among NOL-covers.

Load-bearing premise

The pruning step assumes that any set missing a leaf is infeasible for the coverage constraint, because one can always pick an error level smaller than that leaf's probability, even though the optimization is actually run at one fixed error level where such rare leaves could be safely omitted.

Editorial extensions

If this is right

  • Prediction sets can mix specific leaf labels with broader category labels, so a single set can be compact where the classifier is confident and abstract where it is not.
  • The coverage guarantee extends to DAG taxonomies with multiple inheritance, because the max-over-true-nodes conformity score handles several simultaneously true nodes per instance.
  • The $\beta$ parameter gives continuous control: raising it trades nominal set size against the number of covered leaves, letting users choose how abstract predictions may be.
  • Calibration cost is bounded by the number of NOL-covers rather than all $2^{|V|}$ subsets, and those covers are computed once per taxonomy.
  • Dynamic pruning and Bonferroni correction during inference preserve finite-sample coverage while limiting the multiple-comparison penalty.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the fixed-alpha reading of the optimization is taken literally, allowing NOL-covers to omit leaves with prior mass below $\alpha$ could produce lower-cost feasible sets; this is a direct testable relaxation of the paper's pruning step.
  • Because Bonferroni correction is conservative, ordering NOL-covers by expected cost and applying sequential multiple-testing corrections should shrink prediction sets further without touching the coverage proof.
  • The same propagated-score conformity mechanism applies to hierarchical multi-label prediction and to retrieval tasks where ground truth is a set of ancestors, not just to single-label classification.
  • The user-study preference suggests $\beta$ should be learned per user or per task; the paper's median-descendant-count heuristic is a reasonable default but not necessarily the one annotators would choose.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes Hierarchical Conformal Classification (HCC), an extension of conformal prediction for classification with a label hierarchy modeled as a DAG. HCC frames the construction of prediction sets—subsets of hierarchy nodes—as the constrained optimization problem in Equation (3), which minimizes the number of nodes plus a specificity penalty on covered leaves, subject to a leaf-coverage probability guarantee. To make search tractable, the authors restrict attention to non-overlapping leaf covers (NOL-covers), claiming that Propositions 1 and 2 show this restriction preserves optimality, and that Theorem 1 gives a finite-sample coverage guarantee for the proposed conformity score in Equation (4). The paper also presents three new benchmarks (text, image, audio) with taxonomies, experiments comparing HCC to standard conformal prediction and an LCA baseline, ablations, and a user study indicating a preference for HCC's outputs.

Significance. If the theoretical claims were valid, HCC would be a practically relevant contribution: it provides a principled way to produce semantically coherent, compact prediction sets in hierarchical label spaces while maintaining the finite-sample guarantees of conformal prediction. The paper also contributes new benchmark datasets with taxonomies and makes code available, which are useful assets. However, the central theoretical foundation is unsound: the claimed optimality of the NOL-cover restriction is not established, and the stated coverage theorem is internally inconsistent. The empirical results are suggestive but cannot, by themselves, compensate for the failure of the main theoretical claims.

major comments (4)
  1. [Search-Space Pruning, Proposition 1] Proposition 1 is false for the fixed-alpha optimization problem in Equation (3). The proof shows that omitting a leaf y* violates the coverage constraint for every alpha in [0, P(y=y*)), but Equation (3) fixes a single user-chosen alpha. If P(y=y*) < alpha, a set omitting y* can still satisfy the constraint. For example, let a DAG have a root, an internal node AB with leaves A and B, and a leaf C, with P(A)=0.97, P(B)=0.02, P(C)=0.01, alpha=0.02, beta=0.1. The set {AB} has coverage 0.99 >= 0.98 and cost 1+2*0.1=1.2, while the NOL-covers {root}, {AB,C}, and {A,B,C} have costs 1.3, 2.3, and 3.3, respectively. Thus the unique minimizer of Equation (3) is not a NOL-cover, and the claimed restriction can discard the true optimum.
  2. [Search-Space Pruning, Proposition 2] The proof of Proposition 2 is invalid. It constructs a set S from a feasible solution N* by removing ancestor or descendant nodes and then claims S is feasible because its leaf-cover cardinality is no larger than that of N*. Feasibility in Equation (3) is a probability constraint, P(yt in leaf-cov(S)) >= 1-alpha, not a cardinality condition. Removing a node that is the only cover for some high-probability leaves can reduce the covered probability below the constraint. The proof never shows that the removed nodes are redundant in terms of probability mass, so the conclusion that an optimal solution can always be found among NOL-covers is unsupported.
  3. [Theorem 1] Theorem 1 is not self-consistent. The prediction set is defined as C(xt) := {v in V | s(xt, yt, S) <= qhat}, but the conformity score s in Equation (4) depends on the unobserved test label yt through the indicator vector Y'_t, so C(xt) is not computable at inference time. Furthermore, the proof asserts the equivalence st <= qhat <=> ghat(xt)_v'_t >= 1 - qhat, which is incompatible with the stated definition of C(xt) (which uses <= qhat) and also conflicts with the threshold direction in Equation (2). These are internal mathematical errors, not alternative conventions, so the claimed hierarchical coverage guarantee is not established.
  4. [Appendix C] The 'limited search' fallback described in Appendix C restricts the search to one NOL-cover per depth level for large taxonomies, with no statement of optimality or even a bounded approximation ratio. Since the main text claims that the NOL-cover restriction 'upholds optimality,' this further heuristic introduces an additional gap between the stated optimization problem and the actual algorithm that the paper does not discuss or analyze.
minor comments (4)
  1. [Results section] There is an inconsistency in table numbering: the main text repeatedly refers to 'Table 3' for the test results, but the table appears as 'Table 2' in the manuscript; Figure 5 also references 'Table 3'.
  2. [Appendix A, Proposition 1 proof] The proof states that P(y=y*) > 0 'must hold unless y* is excluded from Y altogether,' which is not justified; a leaf in the taxonomy can have zero probability under the data distribution.
  3. [Theorem 1 statement] The theorem says C(xt) ranges over all v in V, while the conformity score s is defined for nodes in the given NOL-cover S; the statement should clarify that the prediction set is a subset of S.
  4. [Abstract] The abstract claims the reduced candidate set 'suffices to ensure coverage while upholding optimality,' which is not supported by the current theory given the errors in Propositions 1 and 2; the claim should be qualified or revised.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the coverage argument is a standard exchangeability argument, and the NOL-cover pruning claims are independent structural arguments rather than fitted inputs or self-citation chains.

full rationale

The derivation chain in HCC does not reduce to its own inputs. The optimization in Eq. (3) is a stated objective over node subsets; alpha is a user-chosen error level and beta is set from the taxonomy via Eq. (6) (median leaf-cover sizes), not from the calibration/test labels or from the coverage guarantee. The calibration procedure follows split conformal prediction, with the conformity score in Eq. (4) presented as an explicit generalization of Sadinle et al.; the coverage proof relies on exchangeability and rank uniformity, the standard CP argument, and does not assume the conclusion it is proving. The restriction to NOL-covers is justified by Propositions 1 and 2 in Appendix A; these are structural claims about feasibility and cost, and even if their proofs contain quantifier or direction errors (a correctness concern, not a circularity), they are not cases where a fitted parameter is renamed as a prediction or where a result is imported from the authors' prior work. The only appearance of the authors' own prior work (den Hengst et al. 2024) is a suggestion for a future, larger user study, and it is not load-bearing for the central claim. The empirical section compares against standard CP, LCA, and structured CP on external benchmarks, so the method's main assertions are not forced by construction.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. Its mathematical construction, the NOL-cover, is a combinatorial constraint rather than a postulated entity. The key hidden assumptions are the false Proposition 1 and the unanalyzed depth-limited search heuristic.

free parameters (2)
  • beta = 0.19 (dbp), 0.04 (img), 0.25 (gtz)
    Trade-off between set size and specificity; set by Eq. (6) as the inverse median number of leaves per internal node in each taxonomy. Not fit to the target coverage, but it is a hand-set hyperparameter that controls the objective.
  • alpha = 1-alpha = .9997, .98, .9
    User-specified error level; the paper sets it inversely proportional to the accuracy of the classifier. This is a standard CP input but is chosen rather than derived.
assumptions (4)
  • domain assumption The class hierarchy is a DAG with a unique root, and the classifier's labels are exactly the leaves (Definitions 1-2).
    All results depend on this structural assumption; real-world taxonomies may have multiple roots or labels at non-leaf nodes.
  • standard math Exchangeability of calibration and test data (split conformal prediction).
    Needed for Theorem 1's rank uniform argument; standard in CP.
  • ad hoc to paper Any feasible solution to Eq. (3) must cover all leaves, i.e., Proposition 1.
    This unproven and incorrect assertion is the basis for restricting the search to NOL-covers. It is false for fixed alpha when an omitted leaf has prior probability below alpha.
  • ad hoc to paper For large taxonomies, restricting to one NOL-cover per depth level preserves near-optimality (Appendix C, 'limited search').
    No analysis is provided for the approximation error of this additional pruning.

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Cite this review

Pith. "Pith review of Hierarchical Conformal Classification." pith.science (2026). https://pith.science/paper/JKVOGWBO

@misc{pith2026250813288,
  author       = {Pith},
  title        = {Pith review of: Hierarchical Conformal Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JKVOGWBO}},
  note         = {Machine review of arXiv:2508.13288}
}
read the original abstract

Conformal prediction (CP) is a powerful framework for quantifying uncertainty in machine learning models, offering reliable predictions with finite-sample coverage guarantees. When applied to classification, CP produces a prediction set of possible labels that is guaranteed to contain the true label with high probability, regardless of the underlying classifier. However, standard CP treats classes as flat and unstructured, ignoring domain knowledge such as semantic relationships or hierarchical structure among class labels. This paper presents hierarchical conformal classification (HCC), an extension of CP that incorporates class hierarchies into both the structure and semantics of prediction sets. We formulate HCC as a constrained optimization problem whose solutions yield prediction sets composed of nodes at different levels of the hierarchy, while maintaining coverage guarantees. To address the combinatorial nature of the problem, we formally show that a much smaller, well-structured subset of candidate solutions suffices to ensure coverage while upholding optimality. An empirical evaluation on three new benchmarks consisting of audio, image, and text data highlights the advantages of our approach, and a user study shows that annotators significantly prefer hierarchical over flat prediction sets.

Figures

Figures reproduced from arXiv: 2508.13288 by the authors.

Figure 1
Figure 1. Input with standard CP output (left), hierarchy sub [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Taxonomy for the running example. substantially reduced search during calibration, which is fur￾ther pruned of spurious solutions during inference in a scal￾able way. Wieslander et al. (2020) combine deep learning with conformal prediction for the classification of subre￾gions of tissues. They assess region-specific drug response by employing hierarchical identification of tissue regions and measuring confidence in … view at source ↗
Figure 3
Figure 3. Visualization of HCC training: (a) for an input DAG with nodes [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Visualization of HCC inference: (a) the prediction scores for a test input [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Prediction set sizes and number of covered leaves for varying [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Number of nodes per level: dbp (left) and wordnet-subset (center), gtz (right). ”yes” denotes the number of leaves, and ”no” the number of internal nodes per level [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: dbp taxonomy. Leaves are denoted in green and intermediate nodes in blue. Entity Classical/Country Electronic/Rock Jazz/Blues/Reggae POP Classical Country Disco Metal Rock Jazz Blues Reggae Hip Hop Pop [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: gtz taxonomy. Appendix E: Additional Results & Ablation Details We compare our approach to Structured Conformal Prediction (SCP) by Zhang, Li, and Bastani (2025) on the ImageNet dataset, using prediction scores obtained with the same ResNet-152 base classifier on the s…
Figure 9
Figure 9. Figure 9: Example output and user preferences for random samples from [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

31 extracted references · 20 canonical work pages · cited by 2 Pith papers

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    N.; Bates, S.; Cand \`e s, E

    Angelopoulos, A. N.; Bates, S.; Cand \`e s, E. J.; Jordan, M. I.; and Lei, L. 2025. Learn then test: Calibrating predictive algorithms to achieve risk control. The Annals of Applied Statistics, 19(2): 1641--1662

    work page 2025

  4. [4]

    N.; Bates, S.; Fisch, A.; Lei, L.; and Schuster, T

    Angelopoulos, A. N.; Bates, S.; Fisch, A.; Lei, L.; and Schuster, T. 2024. Conformal Risk Control. In The Twelfth International Conference on Learning Representations

    work page 2024

  5. [5]

    N.; Bates, S.; Jordan, M.; and Malik, J

    Angelopoulos, A. N.; Bates, S.; Jordan, M.; and Malik, J. 2021. Uncertainty Sets for Image Classifiers using Conformal Prediction. In The Ninth International Conference on Learning Representations

    work page 2021

  6. [6]

    N.; Bates, S.; et al

    Angelopoulos, A. N.; Bates, S.; et al. 2023. Conformal prediction: A gentle introduction. Foundations and Trends in Machine Learning , 16(4): 494--591

    work page 2023

  7. [7]

    Bennett, M. 2013. The financial industry business ontology: Best practice for big data. Journal of Banking Regulation, 14(3): 255--268

    work page 2013

  8. [8]

    Cauchois, M.; Gupta, S.; and Duchi, J. C. 2021. Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction. Journal of machine learning research, 22(81): 1--42

    2021

Show all 31 references
  1. [9]

    den Hengst, F.; Wolter, R.; Altmeyer, P.; and Kaygan, A. 2024. Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition. In Duh, K.; Gomez, H.; and Bethard, S., eds., Findings of the Association for Computational Linguistics: NAACL 2024, 2412-...

    2024

  2. [10]

    Ding, T.; Angelopoulos, A.; Bates, S.; Jordan, M.; and Tibshirani, R. J. 2023. Class-conditional conformal prediction with many classes. Advances in neural information processing systems, 36: 64555--64576

    2023

  3. [11]

    He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770--778

    2016

  4. [12]

    Holm, S. 1979. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, 65--70

    1979

  5. [13]

    Lambrou, A.; and Papadopoulos, H. 2016. Binary relevance multi-label conformal predictor. In Conformal and Probabilistic Prediction with Applications: 5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016, Proceedings 5, 90--104. Springer

    2016

  6. [14]

    Maltoudoglou, L.; Paisios, A.; Lenc, L.; Mart \' i nek, J.; Kr \' a l, P.; and Papadopoulos, H. 2022. Well-calibrated confidence measures for multi-label text classification with a large number of labels . Pattern Recognition, 122: 108271

    2022

  7. [15]

    N.; Jakob, M.; and Bizer, C

    Mendes, P. N.; Jakob, M.; and Bizer, C. 2012. DBpedia: A multilingual cross-domain knowledge base. European Language Resources Association (ELRA)

    2012

  8. [16]

    Miller, G. A. 1995. WordNet: a lexical database for English. Communications of the ACM, 38(11): 39--41

    1995

  9. [17]

    Mortier, T.; H \"u llermeier, E.; Dembczy \'n ski, K.; and Waegeman, W. 2022. Set-valued prediction in hierarchical classification with constrained representation complexity. In Uncertainty in Artificial Intelligence, 1392--1401. PMLR

    2022

  10. [18]

    Mortier, T.; Javanmardi, A.; Sale, Y.; H \"u llermeier, E.; and Waegeman, W. 2025. Conformal Prediction in Hierarchical Classification. arXiv preprint arXiv:2501.19038

    2025 arXiv

  11. [19]

    Paisios, A.; Lenc, L.; Mart\' nek, J.; Kr\'al, P.; and Papadopoulos, H. 2019. A Deep Neural Network Conformal Predictor for Multi-label Text Classification. In Gammerman, A.; Vovk, V.; Luo, Z.; and Smirnov, E., eds., Proceedings of the Eighth Symposium on Conformal and Probabi...

    2019

  12. [20]

    Papadopoulos, H. 2014. A cross-conformal predictor for multi-label classification. In Artificial Intelligence Applications and Innovations: AIAI 2014, 241--250. Springer

    2014

  13. [21]

    Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015. Imagenet large scale visual recognition challenge. International journal of computer vision, 115: 211--252

    2015

  14. [22]

    Sadinle, M.; Lei, J.; and Wasserman, L. 2019. Least ambiguous set-valued classifiers with bounded error levels. Journal of the American Statistical Association, 114(525): 223--234

    2019

  15. [23]

    Sedgwick, P. 2012. Multiple significance tests: the Bonferroni correction. Bmj, 344

    2012

  16. [24]

    Sturm, B. L. 2012. An analysis of the GTZAN music genre dataset. In Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies, 7--12

    2012

  17. [25]

    Tyagi, C.; and Guo, W. 2023. Multi-label classification under uncertainty: a tree-based conformal prediction approach. In Conformal and Probabilistic Prediction with Applications, 488--512. PMLR

    2023

  18. [26]

    Vovk, V.; Gammerman, A.; and Shafer, G. 2005. Algorithmic learning in a random world, volume 29. Springer

    2005

  19. [27]

    Wang, H.; Liu, X.; Lv, B.; Yang, F.; and Hong, Y. 2014. Reliable multi-label learning via conformal predictor and random forest for syndrome differentiation of chronic fatigue in traditional Chinese medicine. PloS one, 9(6): e99565

    2014

  20. [28]

    J.; Skogberg, G.; Jackson, S.; Frid \'e n, M.; Karlsson, J.; Spjuth, O.; and W \"a hlby, C

    Wieslander, H.; Harrison, P. J.; Skogberg, G.; Jackson, S.; Frid \'e n, M.; Karlsson, J.; Spjuth, O.; and W \"a hlby, C. 2020. Deep learning with conformal prediction for hierarchical analysis of large-scale whole-slide tissue images. IEEE journal of biomedical and health info...

    2020

  21. [29]

    Zesch, T.; and Gurevych, I. 2007. Analysis of the Wikipedia category graph for NLP applications. In Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing, 1--8

    2007

  22. [30]

    Zhang, B.; Li, S.; and Bastani, O. 2025. Conformal Structured Prediction. In The Thirtheenth International Conference on Learning Representations

    2025

  23. [31]

    Zhang, X.; Zhao, J.; and LeCun, Y. 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems, 28

    2015

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