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REVIEW 4 major objections 4 minor 1 cited by

Robust multi-task boosting using clustering and local ensembling

T0 review · 4 major / 4 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A multi-task learning method that measures task similarity by cross-task errors, then clusters tasks and trains a local ensemble per cluster, outperforms single-task and pooling baselines across synthetic and real-world benchmarks.

desk verdict Useful, reproducible MTL pipeline; the similarity construction doesn't yet support the paper's strong theoretical claims. read the letter →

arxiv 2602.14231 v1 pith:HDERN7EP submitted 2026-02-15 cs.LG

Seyedsaman Emami , Daniel Hernández-Lobato , Gonzalo Martínez-Muñoz This is my paper · ORCID
classification cs.LG
keywords multi-tasklearningnegativetransfertaskclusteringgradientboostingcross-taskerrorlocalensemblinghierarchicalsimilarity
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

The paper claims that task relatedness in multi-task learning should be measured by how well a model trained on one task predicts another task's data, not by in-domain accuracy. It introduces RMB-CLE, which computes cross-task errors, converts them into similarities, clusters tasks via hierarchical agglomerative clustering with silhouette-based model selection, and trains a local ensemble on each cluster. This structural separation of tasks is meant to avoid negative transfer by isolating incompatible tasks. On synthetic data the method recovers ground-truth clusters exactly and matches an oracle given the true clusters; on real-world datasets it consistently beats single-task, pooling, MTGB, and R-MTGB baselines.

What carries the argument

The central object is the cross-task error matrix E_{i,j}, the loss of a model trained on task j evaluated on task i's data. Similarity is s_{i,j} = 1/(E_{i,j} + ε), and each task is embedded as a row of this matrix; cosine distance between rows defines the task geometry. UPGMA agglomerative clustering on this distance, with silhouette-based selection of the number of clusters, yields task groups; a local boosting ensemble (LGBM or MTGB) is then trained per cluster. The load-bearing identity is the risk decomposition R_i(F_j) = E[(η_i - F_j)^2] + E[σ_i^2], which shows cross-task error captures functional relatedness plus task-specific noise.

What would settle it

Generate a regression dataset with several clusters of tasks, then add a task whose underlying regression function exactly matches one cluster's function but whose label noise is much larger. If RMB-CLE assigns this task to a different cluster (or performs worse than a variant that places it with its functional matches), the similarity geometry is not capturing purely functional relatedness.

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Extended reading notes

Core claim

The central claim is that cross-task generalization error provides a principled measure of task compatibility. For regression, the risk of transferring a model F_j to task i decomposes into a functional mismatch term (the squared L2 distance between the conditional functions η_i and F_j) plus an irreducible noise term σ_i^2 that is independent of the source model. For classification, the cross-task error upper-bounds the excess risk relative to the Bayes classifier. RMB-CLE builds a similarity matrix from these cross-task errors, embeds each task as a similarity profile, clusters tasks with UPGMA agglomerative clustering chosen by silhouette score, and trains a local ensemble on each cluster

Load-bearing premise

The paper treats the empirical cross-task error E_{i,j} computed on task i's training data as a pure measure of functional compatibility, but its own risk decomposition contains a task-specific irreducible noise term σ_i^2 that is independent of the source model; a noisy task will appear dissimilar to all tasks, and the similarity vectors include in-domain errors E_{i,i} that the paper says should not be used.

Editorial extensions

If this is right

  • Task clusters can be discovered automatically from transfer behavior without metadata, predefined clusters, or low-rank assumptions.
  • On synthetic data, inferred clusters equal ground-truth clusters, and model performance matches an oracle that knows the true clusters.
  • The method generalizes R-MTGB's binary inlier/outlier split to multiple clusters, covering more complex task landscapes.
  • Prediction is fast: one cluster lookup plus a single ensemble evaluation per instance.
  • Cross-task error-based similarity beats pseudo-residual-based similarity in ablations, indicating the transfer signal matters.

Reading between the lines

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

  • Because the similarity profiles include the diagonal in-domain errors E_{i,i}, a task with high label noise will look dissimilar to every other task even if its underlying function matches; normalizing transfer errors by task difficulty — which the paper defers to future work — would be a natural fix.
  • The density of cross-task evaluation makes training cost quadratic in the number of tasks; for large task counts, a subsampled or sketch-based approximation of the error matrix would be needed.
  • The same error-geometry idea could be used for task selection in transfer learning: choose source tasks that minimize the cross-task error on the target rather than using representation-based affinity.
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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 RMB-CLE, a multi-task boosting framework that estimates inter-task similarity from cross-task generalization errors (Eq. 5), converts these errors to similarities (Eq. 6), embeds tasks as similarity-profile vectors, clusters them with agglomerative average-linkage clustering and silhouette-based model selection, and finally trains cluster-specific LGBM or MTGB ensembles. The authors claim that cross-task errors admit a risk decomposition into functional mismatch and irreducible noise (Section 3.3), that the resulting clusters recover ground-truth structure, and that RMB-CLE consistently outperforms single-task, pooling, and existing multi-task boosting baselines on synthetic and real-world benchmarks.

Significance. If the central claim were supported, the framework would be a useful, model-agnostic addition to the multi-task boosting literature: it is simple, computationally characterized, and ships with public code and data. The empirical protocol is extensive (100 repetitions, task-wise statistical tests, ablations on linkage and pseudo-residual similarity). However, the theoretical justification of the similarity measure is the load-bearing part of the paper, and that justification is currently incomplete: by the paper's own decomposition, cross-task errors are confounded with target-task irreducible noise, and the similarity vectors include the diagonal in-domain errors that the paper's Remark explicitly excludes. The synthetic experiments do not exercise this confound because their data-generating process has no additive noise. The over-strong performance claims in the abstract are also contradicted by a clear, non-marginal exception in the real-world regression results.

major comments (4)
  1. [Section 3.2, Eq. (5)-(8) and Section 3.3.1, Eq. (23)] The central claim that cross-task errors provide a 'principled measure of functional similarity' is not supported by the paper's own risk decomposition. Eq. (23) gives R_i(F_j) = E[(η_i-F_j)^2] + E[σ_i^2], where the second term is the target task's irreducible noise and is independent of the source model j. Thus each row i of the similarity matrix S in Eq. (7) contains a task-specific, roughly constant offset. Cosine distance (Eq. 8) is not invariant to row-wise additive constants: a noisy task will have a flatter similarity profile and will appear cosine-similar to other noisy tasks, even when the underlying functions η_i are unrelated, while clean tasks will appear mutually close. The Conclusions concede that 'another promising direction is to refine cross-task similarity estimation by accounting for task difficulty,' but the method as presented does not do so. This confound must be re
  2. [Section 3.2, Eq. (7) and Remark after Eq. (5)] The similarity vector s_i = (s_{i,1}, ..., s_{i,m}) includes the diagonal entry s_{i,i}, which is derived from the in-domain training error E_{i,i}. The Remark immediately before Eq. (5) states that 'in-domain accuracy only reflects task difficulty (e.g., noise level or sample size) and not inter-task compatibility.' Including the diagonal directly contradicts this Remark. For a well-fit task, E_{i,i} is small and s_{i,i} can dominate the profile, so the cosine distance in Eq. (8) reflects the tasks' own training errors rather than their transfer relationships. The diagonal should be excluded from the similarity profiles (e.g., set to zero or treated as missing) and the analysis repeated.
  3. [Section 4.1, Eq. (36) and Section 4.3] The synthetic experiments do not test the difficulty-confounding concern because the data-generating process in Eq. (36) is deterministic: no additive noise is introduced, so σ_i^2 = 0 for every task and the risk decomposition in Eq. (23) reduces to the functional mismatch term alone. Perfect cluster recovery under these conditions is therefore consistent with a method that clusters by functional mismatch, but it provides no evidence that the method is robust to heterogeneous noise levels. The authors should add synthetic settings where tasks have identical functional forms but different noise levels, and settings where noise is correlated with cluster membership, to determine whether the method clusters by noise or by functional relatedness.
  4. [Section 4.4, Tables 11-12] The abstract and conclusions claim that RMB-CLE 'consistently outperforms' all baselines, but the Parkinson dataset is a clear exception. In Table 11 (RMSE), ST-GB achieves 0.268 and R-MTGB 0.289, while RMB-CLE-via-LGBM achieves 0.349; in Table 12 (MAE), ST-GB achieves 0.110 and R-MTGB 0.137, while RMB-CLE-via-LGBM achieves 0.203. The text calls the ST-GB advantage 'marginal,' which is inaccurate for a 30-80% relative difference. The paper should either soften the 'consistently outperforms' claim or provide a more detailed analysis of why RMB-CLE fails on this dataset, as it weakens the general robustness claim.
minor comments (4)
  1. [Algorithm 1 and Table 3] Algorithm 1 requires Kmax and ε, but these hyperparameters are not specified in the experimental setup or in the hyperparameter search space of Table 3. The authors should state their default values and the range considered, if any, since the silhouette-based selection only determines k within the interval [2, min(m, Kmax)].
  2. [Algorithm 1, lines 24-28] Line 24 assigns k⋆ inside the inner loop over tasks, and line 28 reassigns k⋆ after the loop. This is inconsistent with Eq. (11), which defines k⋆ as the maximizer over k of the mean silhouette. The algorithm pseudocode should be aligned with the equation.
  3. [Table 1 and figures] Table 1 uses '3' and '7' as symbols for present/absent, which is confusing; the '7' appears to be a typographical artifact for a check/cross symbol. Several figures (e.g., Figures 2, 5-7) contain OCR-like typos in axis labels ('Clus er', 'Classifica ion', 'R egression'). These should be corrected.
  4. [Eq. (14)] Prediction requires the task identifier i at test time. For several real-world datasets, tasks are obtained by grouping on an attribute, so the identifier is available; however, this requirement should be stated explicitly in the problem formulation and contrasted with methods that do not need a task identifier at inference.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity: the method is self-contained, with a non-circular risk decomposition and an honest external benchmark comparison; the flagged difficulty/noise issue is a validity limitation, not a circular step.

full rationale

Circularity pass: I do not find a load-bearing circular step. Eq. (23) is a direct expansion of the population risk R_i(F_j) and is not itself an input to the method; the similarity matrix (Eqs. (5)-(7)) is computed from cross-task errors evaluated on task data and then clustered without access to target labels or benchmark outcomes. No parameter is fitted to the reported test results: boosting block sizes are selected by within-training cross-validation (Section 4.2), so the reported gains are genuine empirical comparisons. The Cluster-Known variant is an oracle upper-bound comparison, not a fitted prediction. The self-citations to MTGB [32] and R-MTGB [33] are used as baseline architectures and prior work; the central justification of error-based similarity rests on Eq. (23), not on those citations, and no uniqueness theorem is imported from the authors. The manuscript itself flags the main validity concern: 'Another promising direction is to refine cross-task similarity estimation by accounting for task difficulty' (Conclusions), and the Remark's statement that in-domain accuracy 'only reflects task difficulty (e.g., noise level or sample size) and not inter-task compatibility' conflicts with the inclusion of the diagonal E_{i,i} in S (Eq. (7)). That is an internal-consistency / robustness limitation, not constructional circularity: clustering is still an unsupervised function of the error matrix, and the risk decomposition remains an exact identity. Score 2 reflects minor self-citations and this flagged limitation, not a reduction of the derivation to its inputs.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the assumption that cross-task errors computed on training data reflect functional relatedness. This is an approximation: Eq. (23) shows the risk includes task i's irreducible noise, so a noisy task may be separated from functionally similar tasks. Additionally, the similarity matrix includes diagonal in-domain errors. The method has few free parameters; the main ones are ε and Kmax, both unspecified. The theoretical decomposition itself is standard math.

free parameters (3)
  • ε (similarity smoothing constant) = unspecified (ε > 0)
    Used in Eq. (6) s_{i,j} = 1/(E_{i,j}+ε) to avoid division by zero; the value is not reported.
  • Kmax (max cluster count) = unspecified (min(m,Kmax))
    Upper bound for silhouette search in Step 3; not specified in the text.
  • S(4) local ensemble block size = 100
    Fixed to 100 in experiments instead of being tuned like baseline block sizes (Table 3); gives RMB-CLE a larger model budget.
assumptions (5)
  • standard math Law of total expectation and Bayes classifier optimality
    Used in Section 3.3 to derive risk decomposition and classification bound.
  • domain assumption Cross-task empirical error is a valid proxy for population transfer risk
    The method uses finite-sample in-task training data to estimate E_{i,j}; no variance or confidence is quantified. Section 3.2 Step 1.
  • domain assumption Common input dimension across tasks
    Stated as limitation in Conclusions: the framework assumes tasks share the same feature space.
  • domain assumption Task labels/identifiers are available at test time
    Prediction uses task index i for cluster lookup and as input to local ensembles (Eq. 14).
  • ad hoc to paper Noise and functional mismatch are separable in the empirical error
    The method treats E_{i,j} as similarity without normalizing for task difficulty; this is only partially justified by Eq. (23) because the noise term σ_i^2 remains in the estimate.

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Cite this review

Pith. "Pith review of Robust multi-task boosting using clustering and local ensembling." pith.science (2026). https://pith.science/paper/HDERN7EP

@misc{pith2026260214231,
  author       = {Pith},
  title        = {Pith review of: Robust multi-task boosting using clustering and local ensembling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HDERN7EP}},
  note         = {Machine review of arXiv:2602.14231}
}
read the original abstract

Multi-Task Learning (MTL) aims to boost predictive performance by sharing information across related tasks, yet conventional methods often suffer from negative transfer when unrelated or noisy tasks are forced to share representations. We propose Robust Multi-Task Boosting using Clustering and Local Ensembling (RMB-CLE), a principled MTL framework that integrates error-based task clustering with local ensembling. Unlike prior work that assumes fixed clusters or hand-crafted similarity metrics, RMB-CLE derives inter-task similarity directly from cross-task errors, which admit a risk decomposition into functional mismatch and irreducible noise, providing a theoretically grounded mechanism to prevent negative transfer. Tasks are grouped adaptively via agglomerative clustering, and within each cluster, a local ensemble enables robust knowledge sharing while preserving task-specific patterns. Experiments show that RMB-CLE recovers ground-truth clusters in synthetic data and consistently outperforms multi-task, single-task, and pooling-based ensemble methods across diverse real-world and synthetic benchmarks. These results demonstrate that RMB-CLE is not merely a combination of clustering and boosting but a general and scalable framework that establishes a new basis for robust multi-task learning.

Figures

Figures reproduced from arXiv: 2602.14231 by the authors.

Figure 1
Figure 1. One-dimensional synthetic multi-task dataset with [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
Figure 2
Figure 2. Synthetic task-wise Demšar plots (p = 0.05) comparing multi-task models; solid lines indicate no significant differences (Nemenyi). We adopt a shared neural architecture implemented using scikit-learn, consisting of a Multi-Layer Percep￾tron (MLP) that learns a common feature representation across all tasks (Deep-MTL). The shared network comprises three fully connected hidden layers, each with 100 units and ReLU act… view at source ↗
Figure 3
Figure 3. Cluster assignment stability over 100 runs. Each panel shows the fraction of times a task is [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Stability of sigmoid-based task weighting in R-MTGB over 100 runs. Each panel shows the [PITH_FULL_IMAGE:figures/full_fig_p019_4.png]
Figure 5
Figure 5. Figure 5: Real-world task-wise Demšar plots (p = 0.05) comparing multi-task models; solid lines indicate no significant differences (Nemenyi). effective knowledge sharing among related ones. This is achieved through three components: (i) cross-task error evaluation as a referenc…
Figure 6
Figure 6. Figure 6: Synthetic task-wise Demšar plots (p = 0.05), analogous to Figure [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]
Figure 7
Figure 7. Figure 7: Real-world task-wise Demšar plots (p = 0.05), analogous to Figure [PITH_FULL_IMAGE:figures/full_fig_p028_7.png]
Figure 8
Figure 8. Figure 8: Runtime decomposition (log scale) for regression (top) and classification (bottom), showing mean [PITH_FULL_IMAGE:figures/full_fig_p032_8.png]

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Reference graph

Works this paper leans on

73 extracted references · cited by 1 Pith paper

  1. [1]

    A Survey on Multi-Task Learning

    Yu Zhang and Qiang Yang. A Survey on Multi-Task Learning. IEEE Transactions on Knowledge and Data Engineering , 34(12):5586–5609, 2022

    2022

  2. [2]

    Multitask Learning

    Rich Caruana. Multitask Learning. Machine Learning, 28(1):41–75, 1997

    1997

  3. [3]

    Personalized multitask learning for predicting tomorrow’s mood, stress, and health

    Sara Taylor, Natasha Jaques, Ehimwenma Nosakhare, Akane Sano, and Rosalind Picard. Personalized multitask learning for predicting tomorrow’s mood, stress, and health. IEEE Transactions on Affective Computing, 11(2):200–213, 2020

    2020

  4. [4]

    Radial Basis Function Network for Multi-task Learning

    Xuejun Liao and Lawrence Carin. Radial Basis Function Network for Multi-task Learning. In Advances in Neural Information Processing Systems , volume 18. MIT Press, 2005

    2005

  5. [5]

    Silver, Ryan Poirier, and Duane Currie

    Daniel L. Silver, Ryan Poirier, and Duane Currie. Inductive transfer with context-sensitive neural networks. Machine Learning, 73(3):313–336, 2008

    2008

  6. [6]

    Robust multi-task feature learning

    Pinghua Gong, Jieping Ye, and Changshui Zhang. Robust multi-task feature learning. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining , pages 895–903, 2012

    2012

  7. [7]

    Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework

    Lei Han, Yu Zhang, Guojie Song, and Kunqing Xie. Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 28, 2014

    2014

  8. [8]

    A framework for learning predictive structures from multiple tasks and unlabeled data

    Rie Kubota Ando, Tong Zhang, and Peter Bartlett. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of machine learning research , 6(11), 2005

    2005

Show all 73 references
  1. [9]

    A convex formulation for learning shared structures from multiple tasks

    Jianhui Chen, Lei Tang, Jun Liu, and Jieping Ye. A convex formulation for learning shared structures from multiple tasks. In Proceedings of the 26th Annual International Conference on Machine Learning , pages 137–144. Association for Computing Machinery, 2009

    2009

  2. [10]

    Learning Multiple Tasks using Manifold Regular- ization

    Arvind Agarwal, Samuel Gerber, and Hal Daume. Learning Multiple Tasks using Manifold Regular- ization. In Advances in Neural Information Processing Systems , volume 23. Curran Associates, Inc., 2010

    2010

  3. [11]

    Discovering structure in multiple learning tasks: The TC algorithm

    Sebastian Thrun and Joseph O’Sullivan. Discovering structure in multiple learning tasks: The TC algorithm. In ICML, volume 96, pages 489–497. Citeseer, 1996

    1996

  4. [12]

    Task clustering and gating for bayesian multitask learning

    Bart Bakker and Tom Heskes. Task clustering and gating for bayesian multitask learning. Journal of Machine Learning Research, 4:83–99, 2003. 22

    2003

  5. [13]

    Multi-Task Learning for Classification with Dirichlet Process Priors

    Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram. Multi-Task Learning for Classification with Dirichlet Process Priors. Journal of Machine Learning Research , 8(1), 2007

    2007

  6. [14]

    Regularized multi–task learning

    Theodoros Evgeniou and Massimiliano Pontil. Regularized multi–task learning. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages 109–

  7. [15]

    Large Margin Multi-Task Metric Learning

    Shibin Parameswaran and Kilian Q Weinberger. Large Margin Multi-Task Metric Learning. In Advances in Neural Information Processing Systems , volume 23. Curran Associates, Inc., 2010

    2010

  8. [16]

    Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation

    Nico Goernitz, Christian Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, and Sören Sonnenburg. Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation. In Advances in Neural Information Processing Systems , volume 24. Curran Associates, Inc., 2011

    2011

  9. [17]

    A Dirty Model for Multi-task Learning

    Ali Jalali, Sujay Sanghavi, Chao Ruan, and Pradeep Ravikumar. A Dirty Model for Multi-task Learning. In Advances in Neural Information Processing Systems , volume 23. Curran Associates, Inc., 2010

    2010

  10. [18]

    Learning Tree Structure in Multi-Task Learning

    Lei Han and Yu Zhang. Learning Tree Structure in Multi-Task Learning. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages 397–406. Association for Computing Machinery, 2015

    2015

  11. [19]

    Flexible Clustered Multi-Task Learning by Learning Representative Tasks

    Qiang Zhou and Qi Zhao. Flexible Clustered Multi-Task Learning by Learning Representative Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 38(2):266–278, 2016

    2016

  12. [20]

    Eric Backer and Anil K. Jain. A Clustering Performance Measure Based on Fuzzy Set Decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence , PAMI-3(1):66–75, 1981

    1981

  13. [21]

    Rui Xu and D. Wunsch. Survey of clustering algorithms. IEEE Transactions on Neural Networks , 16 (3):645–678, 2005

    2005

  14. [22]

    Clustering Methods , pages 321–352

    Lior Rokach and Oded Maimon. Clustering Methods , pages 321–352. Springer US, 2005

    2005

  15. [23]

    Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition

    An-An Liu, Yu-Ting Su, Wei-Zhi Nie, and Mohan Kankanhalli. Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(1):102–114, 2017

    2017

  16. [24]

    K-Means Clustering , pages 563–564

    Xin Jin and Jiawei Han. K-Means Clustering , pages 563–564. Springer US, 2010

    2010

  17. [25]

    Friedman

    Jerome H. Friedman. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5):1189–1232, 2001

    2001

  18. [26]

    A comparative analysis of gradient boosting algorithms

    Candice Bentéjac, Anna Csörgő, and Gonzalo Martínez-Mu noz. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review , 54(3):1937–1967, 2021

    1937

  19. [27]

    Tabular data: Deep learning is not all you need

    Ravid Shwartz-Ziv and Amitai Armon. Tabular data: Deep learning is not all you need. Information Fusion, 81:84–90, 2022

    2022

  20. [28]

    XGBoost: A Scalable Tree Boosting System

    Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages 785–794. Association for Computing Machinery, 2016

    2016

  21. [29]

    LightGBM: A Highly Efficient Gradient Boosting Decision Tree

    Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017

    2017

  22. [30]

    CatBoost: unbiased boosting with categorical features

    Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018

    2018

  23. [31]

    Boosted multi-task learning

    Olivier Chapelle, Pannagadatta Shivaswamy, Srinivas Vadrevu, Kilian Weinberger, Ya Zhang, and Belle Tseng. Boosted multi-task learning. Machine Learning, 85(1-2):149–173, 2011

    2011

  24. [32]

    Multi-task gradient boosting

    Seyedsaman Emami, Carlos Ruiz Pastor, and Gonzalo Martínez-Muñoz. Multi-task gradient boosting. In Hybrid Artificial Intelligent Systems , pages 97–107. Springer International Publishing, 2023

    2023

  25. [33]

    Robust-multi-task gradient boosting

    Seyedsaman Emami, Gonzalo Martínez-Mu noz, and Daniel Hernández-Lobato. Robust-multi-task gradient boosting. Expert Systems with Applications , 303:130696, 2026. ISSN 0957-4174

    2026

  26. [34]

    Robust multi-task learning with t-processes

    Shipeng Yu, Volker Tresp, and Kai Yu. Robust multi-task learning with t-processes. In Proceedings of the 24th International Conference on Machine Learning , pages 1103–1110. Association for Computing Machinery, 2007. 23

    2007

  27. [35]

    Clustered Multi-Task Learning: A Convex For- mulation

    Laurent Jacob, Jean-philippe Vert, and Francis Bach. Clustered Multi-Task Learning: A Convex For- mulation. In Advances in Neural Information Processing Systems , volume 21. Curran Associates, Inc., 2008

    2008

  28. [36]

    Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

    Quanquan Gu and Jie Zhou. Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification. In 2009 Ninth IEEE International Conference on Data Mining , pages 159–168, 2009

    2009

  29. [37]

    Learning a kernel for multi-task clustering

    Quanquan Gu, Zhenhui Li, and Jiawei Han. Learning a kernel for multi-task clustering. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 25, pages 368–373, 2011

    2011

  30. [38]

    Clustered Multi-Task Learning Via Alternating Structure Optimization

    Jiayu Zhou, Jianhui Chen, and Jieping Ye. Clustered Multi-Task Learning Via Alternating Structure Optimization. In Advances in Neural Information Processing Systems , volume 24. Curran Associates, Inc., 2011

    2011

  31. [39]

    Convex Discriminative Multitask Clustering

    Xiao-Lei Zhang. Convex Discriminative Multitask Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence , 37(1):28–40, 2015

    2015

  32. [40]

    Multitask Spectral Clustering by Exploring Intertask Correlation

    Yang Yang, Zhigang Ma, Yi Yang, Feiping Nie, and Heng Tao Shen. Multitask Spectral Clustering by Exploring Intertask Correlation. IEEE Transactions on Cybernetics , 45(5):1083–1094, 2015

    2015

  33. [41]

    Self-Adapted Multi-Task Clustering

    Xianchao Zhang, Xiaotong Zhang, and Han Liu. Self-Adapted Multi-Task Clustering. In IJCAI, pages 2357–2363, 2016

    2016

  34. [42]

    Multi-Task Clustering with Model Relation Learning

    Xiaotong Zhang, Xianchao Zhang, Han Liu, and Jiebo Luo. Multi-Task Clustering with Model Relation Learning. In IJCAI, pages 3132–3140, 2018

    2018

  35. [43]

    Multi-Task Learning as Multi-Objective Optimization

    Ozan Sener and Vladlen Koltun. Multi-Task Learning as Multi-Objective Optimization. In Advances in Neural Information Processing Systems , volume 31. Curran Associates, Inc., 2018

    2018

  36. [44]

    Gradient Surgery for Multi-Task Learning

    Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient Surgery for Multi-Task Learning. In Advances in Neural Information Processing Systems , volume 33, pages 5824–5836. Curran Associates, Inc., 2020

    2020

  37. [45]

    Conflict-A verse Gradient Descent for Multi-task learning

    Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. Conflict-A verse Gradient Descent for Multi-task learning. In Advances in Neural Information Processing Systems , volume 34, pages 18878– 18890. Curran Associates, Inc., 2021

    2021

  38. [46]

    Zamir, Alexander Sax, William Shen, Leonidas J

    Amir R. Zamir, Alexander Sax, William Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling Task Transfer Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018

    2018

  39. [47]

    Which Tasks Should Be Learned Together in Multi-task Learning? In Proceedings of the 37th International Conference on Machine Learning , volume 119, pages 9120–9132

    Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. Which Tasks Should Be Learned Together in Multi-task Learning? In Proceedings of the 37th International Conference on Machine Learning , volume 119, pages 9120–9132. PMLR, 2020

    2020

  40. [48]

    Sokal and Charles D

    Robert R. Sokal and Charles D. Michener. A statistical method for evaluating systematic relationships. University of Kansas Scientific Bulletin , 38(2):1409–1438, 1958

    1958

  41. [49]

    Rousseeuw

    Peter J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics , 20:53–65, 1987

    1987

  42. [50]

    Algorithms for hierarchical clustering: an overview, II

    Fionn Murtagh and Pedro Contreras. Algorithms for hierarchical clustering: an overview, II. WIREs Data Mining and Knowledge Discovery , 7(6):e1219, 2017

    2017

  43. [51]

    Random Features for Large-Scale Kernel Machines

    Ali Rahimi and Benjamin Recht. Random Features for Large-Scale Kernel Machines. In Advances in Neural Information Processing Systems , volume 20. Curran Associates, Inc., 2007

    2007

  44. [52]

    Gaussian processes for machine learning , vol- ume 2

    Christopher KI Williams and Carl Edward Rasmussen. Gaussian processes for machine learning , vol- ume 2. MIT press Cambridge, MA, 2006

    2006

  45. [53]

    Data Poisoning Attacks on Multi-Task Relationship Learning

    Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, and Sinno Pan. Data Poisoning Attacks on Multi-Task Relationship Learning. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 32, 2018

    2018

  46. [54]

    Taking Advantage of Multitask Learning for Fair Classification

    Luca Oneto, Michele Doninini, Amon Elders, and Massimiliano Pontil. Taking Advantage of Multitask Learning for Fair Classification. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 227–237. Association for Computing Machinery, 2019

    2019

  47. [55]

    Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, and Ed H. Chi. Understand- ing and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , pages 1748–1757. Associatio...

    2021

  48. [56]

    Multi-Task Learning with Calibrated Mixture of Insightful Experts

    Sinan Wang, Yumeng Li, Hongyan Li, Tanchao Zhu, Zhao Li, and Wenwu Ou. Multi-Task Learning with Calibrated Mixture of Insightful Experts. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) , pages 3307–3319, 2022

    2022

  49. [57]

    Cilia, C

    N.D. Cilia, C. De Stefano, F. Fontanella, C. Marrocco, M. Molinara, and A. Scotto Di Freca. An end-to-end deep learning system for medieval writer identification. Pattern Recognition Letters , 129: 137–143, 2020

    2020

  50. [58]

    S. Moro, R. Laureano, and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In Proceedings of the European Simulation and Modelling Conference - ESM’2011 , pages 117–121. EUROSIS, 2011

    2011

  51. [59]

    Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets

    Hakan Gunduz. Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access, 7:115540–115551, 2019

    2019

  52. [60]

    Detection of Parkinson disease using multiclass machine learning approach

    Saravanan Srinivasan, Parthasarathy Ramadass, Sandeep Kumar Mathivanan, Karthikeyan Pan- neer Selvam, Basu Dev Shivahare, and Mohd Asif Shah. Detection of Parkinson disease using multiclass machine learning approach. Scientific Reports, 14(1):13813, 2024

    2024

  53. [61]

    A Spectral Regularization Framework for Multi-Task Structure Learning

    Andreas Argyriou, Massimiliano Pontil, Yiming Ying, and Charles Micchelli. A Spectral Regularization Framework for Multi-Task Structure Learning. In Advances in Neural Information Processing Systems , volume 20. Curran Associates, Inc., 2007

    2007

  54. [62]

    De Stefano, M

    C. De Stefano, M. Maniaci, F. Fontanella, and A. Scotto di Freca. Reliable writer identification in medieval manuscripts through page layout features: The “A vila” Bible case. Engineering Applications of Artificial Intelligence , 72:99–110, 2018

    2018

  55. [63]

    Barry Becker and Ronny Kohavi. Adult. https://archive.ics.uci.edu, 1996

    1996

  56. [64]

    Passive Mine Detection and Classification Method Based on Hybrid Model

    Cemal Yilmaz, Hamdi Tolga Kahraman, and Salih Söyler. Passive Mine Detection and Classification Method Based on Hybrid Model. IEEE Access, 6:47870–47888, 2018

    2018

  57. [65]

    Warwick Nash, Tracy Sellers, Simon Talbot, Andrew Cawthorn, and Wes Ford. Abalone. https: //archive.ics.uci.edu, 1994

    1994

  58. [66]

    Lenk, Wayne S

    Peter J. Lenk, Wayne S. DeSarbo, Paul E. Green, and Martin R. Young. Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs. Marketing Science, 15(2):173–191, 1996

    1996

  59. [67]

    Parkinsons Telemonitoring

    Athanasios Tsanas and Max Little. Parkinsons Telemonitoring. https://archive.ics.uci.edu, 2009

    2009

  60. [68]

    Efficient Output Kernel Learning for Multiple Tasks

    Pratik Kumar Jawanpuria, Maksim Lapin, Matthias Hein, and Bernt Schiele. Efficient Output Kernel Learning for Multiple Tasks. In Advances in Neural Information Processing Systems , volume 28. Curran Associates, Inc., 2015

    2015

  61. [69]

    Scikit-learn: Machine learning in Python

    Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in Python. the Journal of machine Learning research , 12:2825–2830, 2011

    2011

  62. [70]

    Statistical comparisons of classifiers over multiple data sets

    Janez Demšar. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1–30, 2006. 25 A Ablation study This ablation study analyzes the main design choices of RMB-CLE. First, we examine the impact of the task similarity definiti...

    2006

  63. [72]

    This leads to instability in the estimated residuals error out i

    Data efficiency: In many tasks, especially those with small sample sizes, splitting into training and out-of-sample subsets reduces the effective sample size used for both model fitting and residual construction. This leads to instability in the estimated residuals error out i

  64. [73]

    Comparability across tasks: Out-of-sample residuals rely on task-specific holdout splits; so, error out i and error out j are defined on different sample supports, with split-specific, task-dependent noise reflected in cross-task distances. A.2 Effect of clustering linkage criterion...

  65. [117]

    Association for Computing Machinery, 2004

    2004

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.