Pith. sign in

REVIEW 2 major objections 1 minor 68 references

Bayesian CP tensor decomposition gains a pre-trained diffusion model as an implicit data prior for reconstruction under heavy corruption.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

2026-06-28 11:19 UTC pith:PD7SMH6I

load-bearing objection DiffBCP adds a diffusion prior to Bayesian CP decomposition through a split Gibbs sampler with low-rank-guided denoising, but that guidance step risks distorting the intended posterior. the 2 major comments →

arxiv 2606.03212 v1 pith:PD7SMH6I submitted 2026-06-02 cs.LG

Bayesian Tensor Decomposition with Diffusion Model Prior

Zerui Tao , Qibin Zhao This is my paper
classification cs.LG
keywords Bayesian tensor decompositiondiffusion model priorCP decompositionimage inpaintingGibbs samplerautomatic rank selectionlow-rank tensordata-driven prior
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper introduces DiffBCP to strengthen low-rank tensor decomposition when data suffers from severe missing entries or noise. Standard low-rank priors prove too weak for real-world statistics, so the method adds a cumulative shrinkage process on the CP factors for automatic rank selection and couples it to an off-the-shelf diffusion model that supplies a learned prior on the reconstructed tensor. Tractable inference is achieved by a split Gibbs sampler whose diffusion block is guided by low-rank constraints during denoising, together with a noise-adaptive coupling schedule. Experiments on image inpainting and denoising, including high-resolution out-of-distribution cases, report consistent gains over Bayesian, nonlinear, and plug-and-play tensor baselines.

Core claim

DiffBCP is a hybrid-prior Bayesian CP decomposition framework that couples a cumulative shrinkage process prior over the CP factors for automatic rank selection with an off-the-shelf pre-trained diffusion model as an implicit data prior on the reconstructed tensor; posterior inference remains tractable through a split Gibbs sampler in which CP factors receive conjugate updates while the diffusion block is sampled via low-rank-guided denoising, with a noise-adaptive coupling schedule reducing sensitivity to annealing parameters.

What carries the argument

Split Gibbs sampler that isolates conjugate updates for CP factors from low-rank-guided denoising steps that draw from the diffusion model on the reconstructed tensor.

Load-bearing premise

The pre-trained diffusion model can serve as an effective implicit data prior on the reconstructed tensor when sampled via low-rank-guided denoising in the split Gibbs sampler without introducing inconsistencies or bias into the posterior.

What would settle it

On standard image inpainting benchmarks, posterior samples drawn from DiffBCP either produce reconstructions no better than a plain Bayesian CP baseline or generate tensors whose statistics deviate markedly from both the low-rank constraint and the diffusion model's learned distribution.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Automatic rank selection occurs without manual specification of the CP rank.
  • Reconstruction quality improves on inpainting and denoising tasks relative to existing Bayesian and plug-and-play tensor methods.
  • Performance remains stable on high-resolution images drawn from distributions different from the diffusion model's training data.
  • The noise-adaptive schedule lowers the need for manual tuning of the coupling between low-rank and diffusion blocks.

Where Pith is reading between the lines

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

  • The same split-sampler structure could be reused to attach other pre-trained generative models to Bayesian tensor models beyond diffusion.
  • The hybrid prior may extend to non-image tensor data such as video or hyperspectral volumes if domain-appropriate diffusion models are available.
  • The approach suggests a general route for injecting learned priors into other conjugate Bayesian models whose likelihoods are incompatible with direct sampling.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The manuscript introduces DiffBCP, a hybrid-prior Bayesian CP tensor decomposition that pairs a cumulative shrinkage process prior on the CP factors (for automatic rank selection) with an off-the-shelf pre-trained diffusion model as an implicit data prior on the reconstructed tensor. Tractable inference is achieved via a split Gibbs sampler in which the factors receive conjugate updates while the tensor block is drawn by low-rank-guided denoising; a noise-adaptive coupling schedule is proposed to reduce hand-tuning. Experiments on image inpainting and denoising, including high-resolution out-of-distribution cases, report consistent gains relative to Bayesian, nonlinear, and plug-and-play tensor decomposition baselines.

Significance. If the guided denoising step correctly targets the intended conditional posterior, the approach supplies a practical route for injecting rich, learned data-driven regularization into low-rank tensor models without requiring handcrafted structural priors. The reuse of existing diffusion checkpoints and the conjugate factor updates are pragmatic strengths; the reported robustness on OOD images would, if substantiated, indicate broader utility beyond standard TD settings.

major comments (2)
  1. [Inference procedure (split Gibbs sampler and noise-adaptive schedule)] The description of the split Gibbs sampler provides no derivation establishing that low-rank-guided denoising produces samples from the correct conditional p(tensor | factors, observations) under the diffusion prior. Because the guidance injects the low-rank constraint directly into the denoising trajectory, any distributional mismatch would bias the joint posterior; all experimental gains rest on the diffusion block supplying unbiased regularization.
  2. [Inference procedure (noise-adaptive coupling schedule)] The abstract asserts that the noise-adaptive coupling schedule “further reduces sensitivity to hand-tuned annealing,” yet no analysis or ablation quantifies the residual sensitivity or demonstrates that the schedule preserves the marginal distribution of the pre-trained diffusion model.
minor comments (1)
  1. [Model formulation] Notation for the diffusion prior and the precise form of the low-rank guidance term should be introduced with an explicit equation before the sampler is described.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and commit to revisions that strengthen the justification and empirical validation of the proposed inference procedure.

read point-by-point responses

  1. Referee: [Inference procedure (split Gibbs sampler and noise-adaptive schedule)] The description of the split Gibbs sampler provides no derivation establishing that low-rank-guided denoising produces samples from the correct conditional p(tensor | factors, observations) under the diffusion prior. Because the guidance injects the low-rank constraint directly into the denoising trajectory, any distributional mismatch would bias the joint posterior; all experimental gains rest on the diffusion block supplying unbiased regularization.

    Authors: We agree that the manuscript lacks a formal derivation establishing that the low-rank-guided denoising step exactly targets the conditional p(tensor | factors, observations). The split Gibbs construction alternates conjugate updates for the factors with a guided denoising step intended to incorporate the diffusion prior while enforcing the low-rank structure; however, the guidance mechanism is heuristic in nature. In the revision we will add an explicit derivation of the target conditional under the diffusion prior, clarify the approximation introduced by the guidance, and discuss conditions under which the bias remains controlled. revision: yes

  2. Referee: [Inference procedure (noise-adaptive coupling schedule)] The abstract asserts that the noise-adaptive coupling schedule “further reduces sensitivity to hand-tuned annealing,” yet no analysis or ablation quantifies the residual sensitivity or demonstrates that the schedule preserves the marginal distribution of the pre-trained diffusion model.

    Authors: The noise-adaptive schedule was designed to modulate the coupling strength according to the current noise level in order to lessen dependence on a manually chosen annealing path. We acknowledge that the current manuscript provides neither quantitative sensitivity analysis nor verification that the marginal distribution of the pre-trained diffusion model is preserved. The revision will include ablation experiments that measure performance variation across a range of fixed versus adaptive schedules and report diagnostics on the empirical marginals obtained from the diffusion block. revision: yes

Circularity Check

0 steps flagged

No significant circularity; new hybrid framework combines independent priors and sampler

full rationale

The paper introduces DiffBCP by coupling an existing cumulative shrinkage process prior on CP factors with an off-the-shelf pre-trained diffusion model as implicit prior, using a split Gibbs sampler with conjugate updates and low-rank-guided denoising. No equations or claims in the abstract or description reduce any prediction or result to a fitted quantity by construction, nor rely on load-bearing self-citations or imported uniqueness theorems. The derivation chain is a novel assembly of external components rather than a self-referential reduction, making the central claim self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on the domain assumption that a pre-trained diffusion model provides a useful implicit prior compatible with low-rank structure; no explicit free parameters or invented entities are named.

axioms (1)
  • domain assumption An off-the-shelf pre-trained diffusion model can be coupled as an implicit data prior on the reconstructed tensor via low-rank-guided denoising.
    Invoked to compensate for the limited inductive bias of low-rankness under heavy corruption.

pith-pipeline@v0.9.1-grok · 5750 in / 1253 out tokens · 33518 ms · 2026-06-28T11:19:11.772941+00:00 · methodology

0 comments
read the original abstract

Low-rank tensor decomposition (TD) is usually effective on clean, fully observed data, but it often degrades under severe missingness or noise. Low-rankness is itself a useful but limited structural prior, and additional handcrafted priors (e.g., sparsity or smoothness) still fall short of capturing the rich statistics of real-world data. To compensate for this weak inductive bias under heavy corruption, one would like to inject a learned, data-driven prior; however, the state-of-the-art diffusion models are not readily compatible with current TD and tractable posterior inference. To address these challenges, we introduce DiffBCP, a hybrid-prior Bayesian CP decomposition framework that couples a cumulative shrinkage process prior over the CP factors for automatic rank selection with an off-the-shelf pre-trained diffusion model as an implicit data prior on the reconstructed tensor. To make posterior inference tractable despite the coupling among the likelihood, low-rank constraint, and diffusion prior, we develop a split Gibbs sampler: CP factors admit conjugate updates, while the diffusion block is sampled via low-rank-guided denoising. A noise-adaptive coupling schedule further reduces sensitivity to hand-tuned annealing. Experiments on image inpainting and denoising, including high-resolution out-of-distribution images, show consistent gains over Bayesian, nonlinear, and plug-and-play TD baselines.

Figures

Figures reproduced from arXiv: 2606.03212 by Qibin Zhao, Zerui Tao.

Figure 1
Figure 1. Figure 1: Visualization for FFHQ and ImageNet datasets. From top to bottom: ImageNet with Uniform(0.7), FFHQ with Uniform(0.9), ImageNet with Stripe, and FFHQ with Irregular masks [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization for high-resolution images. From top to bottom: Marseille with Uniform(0.9), Tokyo with Uniform(0.95), and Westerlund with Irregular masks. sus 19.83 dB for PuTT, despite the significant distribution shift. Qualitative results ( [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Trace plot of PSNR for ImageNet with Uniform(0.7) mask. Thinner lines correspond to individual images and thicker lines show the average. (b) PnP-DM recovery of Tokyo with Uniform(0.95) mask. 4.3. Improving Diffusion Posterior Sampling Finally, we investigate how the low-rank structure in Diff￾BCP can improve diffusion posterior sampling, especially comparing with PnP-DM (Wu et al., 2024) which shares … view at source ↗
Figure 4
Figure 4. Figure 4: Effective CP rank as a function of Gibbs iteration on FFHQ inpainting with the Uniform(0.9) mask, for four initial ranks R ∈ {10, 100, 200, 300}. Thick lines are the median across 10 images; thin lines are individual images. Regardless of whether the chain starts from an under-specified (R = 10) or over-specified (R = 300) initial rank, CUSP drives the effective rank toward a common band of active componen… view at source ↗
Figure 5
Figure 5. Figure 5: More visualization for FFHQ and ImageNet datasets. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_5.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

68 extracted references · 5 canonical work pages · 2 internal anchors

  1. [1]

    Advances in neural information processing systems , volume=

    Denoising diffusion restoration models , author=. Advances in neural information processing systems , volume=

  2. [2]

    Advances in neural information processing systems , volume=

    Snips: Solving noisy inverse problems stochastically , author=. Advances in neural information processing systems , volume=

  3. [3]

    2013 IEEE global conference on signal and information processing , pages=

    Plug-and-play priors for model based reconstruction , author=. 2013 IEEE global conference on signal and information processing , pages=. 2013 , organization=

    2013

  4. [4]

    SIAM journal on imaging sciences , volume=

    The little engine that could: Regularization by denoising (RED) , author=. SIAM journal on imaging sciences , volume=. 2017 , publisher=

    2017

  5. [5]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Deep image prior , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  6. [6]

    International conference on machine learning , pages=

    Compressed sensing using generative models , author=. International conference on machine learning , pages=. 2017 , organization=

    2017

  7. [7]

    MONAI: An open-source framework for deep learning in healthcare

    Monai: An open-source framework for deep learning in healthcare , author=. arXiv preprint arXiv:2211.02701 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    Roberts, G. O. and Sahu, S. K. , title =. Journal of the Royal Statistical Society: Series B (Methodological) , volume =. 2002 , month =. doi:10.1111/1467-9868.00070 , url =

    work page doi:10.1111/1467-9868.00070 2002

  9. [9]

    Proceedings of the 39th International Conference on Machine Learning , pages =

    Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes , author =. Proceedings of the 39th International Conference on Machine Learning , pages =. 2022 , editor =

    2022

  10. [10]

    Bayesian Temporal Factorization for Multidimensional Time Series Prediction , year=

    Chen, Xinyu and Sun, Lijun , journal=. Bayesian Temporal Factorization for Multidimensional Time Series Prediction , year=

  11. [11]

    Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=

    Provable sparse tensor decomposition , author=. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=. 2017 , publisher=

    2017

  12. [12]

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , volume=

    Hyperspectral image restoration via total variation regularized low-rank tensor decomposition , author=. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , volume=. 2017 , publisher=

    2017

  13. [13]

    Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages=

    Costco: A neural tensor completion model for sparse tensors , author=. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages=

  14. [14]

    Linear Algebra and its Applications , volume=

    Factorization strategies for third-order tensors , author=. Linear Algebra and its Applications , volume=. 2011 , publisher=

    2011

  15. [15]

    Tensor Ring Decomposition

    Tensor ring decomposition , author=. arXiv preprint arXiv:1606.05535 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  16. [16]

    SIAM Journal on Scientific Computing , volume=

    Tensor-train decomposition , author=. SIAM Journal on Scientific Computing , volume=. 2011 , publisher=

    2011

  17. [17]

    Psychometrika , volume=

    Some mathematical notes on three-mode factor analysis , author=. Psychometrika , volume=. 1966 , publisher=

    1966

  18. [18]

    Journal of Mathematics and Physics , volume=

    The expression of a tensor or a polyadic as a sum of products , author=. Journal of Mathematics and Physics , volume=. 1927 , publisher=

    1927

  19. [19]

    IEEE Transactions on signal processing , volume=

    Tensor decomposition for signal processing and machine learning , author=. IEEE Transactions on signal processing , volume=. 2017 , publisher=

    2017

  20. [20]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    The unreasonable effectiveness of deep features as a perceptual metric , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  21. [21]

    SIAM review , volume=

    Tensor decompositions and applications , author=. SIAM review , volume=. 2009 , publisher=

    2009

  22. [22]

    Foundations and Trends

    Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions , author=. Foundations and Trends. 2016 , publisher=

    2016

  23. [23]

    International Conference on Learning Representations , year=

    Score-Based Generative Modeling through Stochastic Differential Equations , author=. International Conference on Learning Representations , year=

  24. [24]

    International conference on machine learning , pages=

    Deep unsupervised learning using nonequilibrium thermodynamics , author=. International conference on machine learning , pages=. 2015 , organization=

    2015

  25. [25]

    Advances in neural information processing systems , volume=

    Denoising diffusion probabilistic models , author=. Advances in neural information processing systems , volume=

  26. [26]

    Biometrika , volume=

    Sparse Bayesian infinite factor models , author=. Biometrika , volume=. 2011 , publisher=

    2011

  27. [27]

    Proceedings of the AAAI Conference on Artificial Intelligence , author=

    Efficient Nonparametric Tensor Decomposition for Binary and Count Data , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2024 , month=. doi:10.1609/aaai.v38i14.29456 , number=

    work page doi:10.1609/aaai.v38i14.29456 2024

  28. [28]

    2023 , issn =

    Tensor train factorization under noisy and incomplete data with automatic rank estimation , journal =. 2023 , issn =. doi:https://doi.org/10.1016/j.patcog.2023.109650 , author =

    work page doi:10.1016/j.patcog.2023.109650 2023

  29. [29]

    Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior , year=

    Cheng, Lei and Chen, Zhongtao and Shi, Qingjiang and Wu, Yik-Chung and Theodoridis, Sergios , journal=. Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior , year=

  30. [30]

    The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

    Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

  31. [31]

    The Thirteenth International Conference on Learning Representations , year=

    InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences , author=. The Thirteenth International Conference on Learning Representations , year=

  32. [32]

    The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

    Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

  33. [33]

    The Eleventh International Conference on Learning Representations , year=

    Diffusion Posterior Sampling for General Noisy Inverse Problems , author=. The Eleventh International Conference on Learning Representations , year=

  34. [34]

    proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

    Robust tensor factorization with unknown noise , author=. proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

  35. [35]

    International Conference on Neural Information Processing , pages=

    Scalable bayesian tensor ring factorization for multiway data analysis , author=. International Conference on Neural Information Processing , pages=. 2023 , organization=

    2023

  36. [36]

    International Conference on Machine Learning , pages=

    Bayesian poisson tucker decomposition for learning the structure of international relations , author=. International Conference on Machine Learning , pages=. 2016 , organization=

    2016

  37. [37]

    Uncertainty in Artificial Intelligence , pages=

    Bayesian streaming sparse Tucker decomposition , author=. Uncertainty in Artificial Intelligence , pages=. 2021 , organization=

    2021

  38. [38]

    Advances in neural information processing systems , volume=

    Distributed flexible nonlinear tensor factorization , author=. Advances in neural information processing systems , volume=

  39. [39]

    Proceedings of the 29th International Coference on International Conference on Machine Learning , pages=

    Infinite tucker decomposition: nonparametric Bayesian models for multiway data analysis , author=. Proceedings of the 29th International Coference on International Conference on Machine Learning , pages=

  40. [40]

    2009 IEEE conference on computer vision and pattern recognition , pages=

    Imagenet: A large-scale hierarchical image database , author=. 2009 IEEE conference on computer vision and pattern recognition , pages=. 2009 , organization=

    2009

  41. [41]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Karras, Tero and Laine, Samuli and Aila, Timo , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

  42. [42]

    International Conference on Machine Learning , pages=

    Listening to the noise: Blind Denoising with Gibbs Diffusion , author=. International Conference on Machine Learning , pages=. 2024 , organization=

    2024

  43. [43]

    Forty-first International Conference on Machine Learning , year=

    Coarse-To-Fine Tensor Trains for Compact Visual Representations , author=. Forty-first International Conference on Machine Learning , year=

  44. [44]

    Guaranteed Tensor Recovery Fused Low-rankness and Smoothness , year=

    Wang, Hailin and Peng, Jiangjun and Qin, Wenjin and Wang, Jianjun and Meng, Deyu , journal=. Guaranteed Tensor Recovery Fused Low-rankness and Smoothness , year=

  45. [45]

    , journal=

    Saragadam, Vishwanath and Balestriero, Randall and Veeraraghavan, Ashok and Baraniuk, Richard G. , journal=. DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors , year=

  46. [46]

    HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging , year=

    Luo, Yisi and Zhao, Xile and Meng, Deyu and Jiang, Taixiang , booktitle=. HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging , year=

  47. [47]

    IEEE Transactions on Image Processing , volume=

    Bayesian low rank tensor ring for image recovery , author=. IEEE Transactions on Image Processing , volume=. 2021 , publisher=

    2021

  48. [48]

    Advances in Neural Information Processing Systems , volume=

    Undirected probabilistic model for tensor decomposition , author=. Advances in Neural Information Processing Systems , volume=

  49. [49]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=

    Guaranteed tensor recovery fused low-rankness and smoothness , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=. 2023 , publisher=

    2023

  50. [50]

    International conference on machine learning , pages=

    Streaming Bayesian deep tensor factorization , author=. International conference on machine learning , pages=. 2021 , organization=

    2021

  51. [51]

    and Huang, Ting-Zhu , journal=

    Zhao, Xi-Le and Yang, Jing-Hua and Ma, Tian-Hui and Jiang, Tai-Xiang and Ng, Michael K. and Huang, Ting-Zhu , journal=. Tensor Completion via Complementary Global, Local, and Nonlocal Priors , year=

  52. [52]

    Journal of the American Statistical Association , volume=

    Bayesian factorizations of big sparse tensors , author=. Journal of the American Statistical Association , volume=. 2015 , publisher=

    2015

  53. [53]

    Advances in neural information processing systems , volume=

    Elucidating the design space of diffusion-based generative models , author=. Advances in neural information processing systems , volume=

  54. [54]

    Neurocomputing , volume=

    Deep plug-and-play prior for low-rank tensor completion , author=. Neurocomputing , volume=. 2020 , publisher=

    2020

  55. [55]

    IEEE Transactions on Signal Processing , volume=

    Split-and-augmented Gibbs sampler—Application to large-scale inference problems , author=. IEEE Transactions on Signal Processing , volume=. 2019 , publisher=

    2019

  56. [56]

    The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

    Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

  57. [57]

    Journal of Computational and Graphical Statistics , volume =

    Federica Stolf and Antonio Canale , title =. Journal of Computational and Graphical Statistics , volume =. 2025 , publisher =

    2025

  58. [58]

    International conference on machine learning , pages=

    Scalable Bayesian low-rank decomposition of incomplete multiway tensors , author=. International conference on machine learning , pages=. 2014 , organization=

    2014

  59. [59]

    IEEE transactions on pattern analysis and machine intelligence , volume=

    Bayesian CP factorization of incomplete tensors with automatic rank determination , author=. IEEE transactions on pattern analysis and machine intelligence , volume=. 2015 , publisher=

    2015

  60. [60]

    Biometrika , volume=

    Bayesian cumulative shrinkage for infinite factorizations , author=. Biometrika , volume=. 2020 , publisher=

    2020

  61. [61]

    Langley , title =

    P. Langley , title =. Proceedings of the 17th International Conference on Machine Learning (ICML 2000) , address =. 2000 , pages =

    2000

  62. [62]

    T. M. Mitchell. The Need for Biases in Learning Generalizations. 1980

    1980

  63. [63]

    M. J. Kearns , title =

  64. [64]

    Machine Learning: An Artificial Intelligence Approach, Vol. I. 1983

    1983

  65. [65]

    R. O. Duda and P. E. Hart and D. G. Stork. Pattern Classification. 2000

    2000

  66. [66]

    Suppressed for Anonymity , author=

  67. [67]

    Newell and P

    A. Newell and P. S. Rosenbloom. Mechanisms of Skill Acquisition and the Law of Practice. Cognitive Skills and Their Acquisition. 1981

    1981

  68. [68]

    A. L. Samuel. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. 1959

    1959