Pith. sign in

REVIEW 2 major objections 3 minor 1 cited by

Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects

T0 review · 2 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read This paper reports the fabrication and characterization of nanodiamond pillars measuring 40 × 65 × 80 nm, each containing a single NV center, as a source for matter-wave interferometry with massive objects.

desk verdict Submission is an abstract about nanodiamond fabrication attached to an unrelated AutoML paper; nothing here is refereeable. read the letter →

arxiv 2508.13662 v1 pith:JJ4TS64C submitted 2025-08-19 quant-ph gr-qcphysics.atom-ph

Menachem Givon , Yaniv Bar-Haim , David Groswasser , Asi Solodar , Nadav Aharon , Michael Belman , Amit Yosefi , Erez Golan
show 2 more authors
Jurgen Jopp Ron Folman
This is my paper
classification quant-phgr-qcphysics.atom-ph
keywords nanodiamondNVcentermatter-waveinterferometryStern-Gerlachspatialsuperpositionsourcefabricationquantumgravitymassiveobjects
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 is a technical note reporting progress towards a nanodiamond source for matter-wave interferometry. The central claim is that nanodiamond pillars measuring 40 by 65 by 80 nanometers, each intended to contain a single nitrogen-vacancy (NV) center, have been fabricated and characterized. This matters because a single NV center in a nanodiamond can be trapped, manipulated, and used in a Stern-Gerlach interferometer to test quantum superpositions of massive objects and probe the interface between quantum mechanics and general relativity. The authors present their design considerations, review the fabrication processes, summarize the characterization completed so far, and outline the remaining steps to finalize the source.

What carries the argument

The central object is the nanodiamond pillar with a single nitrogen-vacancy (NV) center. The NV center's electron spin is optically addressable and its magnetic moment interacts with a magnetic field gradient, producing a Stern-Gerlach force that can split the nanodiamond's wavefunction into a spatial superposition. This is what makes matter-wave interferometry with a massive test particle possible, and the paper's focus is on making such a pillar routine to fabricate.

What would settle it

Measure the optically detected magnetic resonance and spin coherence time of NV centers in 40 × 65 × 80 nm pillars. If no single NV center is optically resolvable at this size, or if the coherence time is shorter than the interferometer's cycle time (roughly milliseconds), the source cannot work as proposed. Alternatively, absence of photon antibunching or a characteristic zero-phonon line in the photoluminescence spectrum of the pillars would falsify the single-NV claim.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that nanodiamond pillars measuring 40 × 65 × 80 nm have been successfully produced, each hosting a single nitrogen-vacancy (NV) center, and that these have been characterized as suitable building blocks for a high-precision, enhanced-coherence nanodiamond source. The source is intended for a closed-loop matter-wave interferometer in space-time that uses Stern-Gerlach forces to prepare spatial superpositions of the diamond and read out the interference. The authors present the design considerations, review the fabrication processes, and summarize the characterization work completed to date, concluding with an outlook on finalizing the source fabrication.

Load-bearing premise

The entire program depends on a 40 × 65 × 80 nm diamond pillar containing a single NV center that is still optically addressable and whose electron spin retains enough coherence for a Stern-Gerlach split-recombine sequence.

Editorial extensions

If this is right

  • If the source is finalized as described, a closed-loop Stern-Gerlach interferometer using a nanodiamond with a single NV center becomes feasible.
  • This would enable tests of the spatial superposition principle with masses far larger than in existing matter-wave experiments.
  • It could offer a way to probe the interface between quantum mechanics and general relativity, for instance through proposed tests of quantized gravity.
  • The fabrication and characterization methods, shared as a technical note, can help other groups working toward the same goal.

Reading between the lines

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

  • The yield of pillars that contain exactly one optically resolvable NV center is likely the practical bottleneck; if yield is low, the approach may need a deterministic placement step.
  • A natural next test is measuring NV spin coherence times in these small pillars as a function of size and surface treatment, since coherence is the premise on which the interferometer rests.
  • If single NV centers can be reliably embedded and addressed in 40-nm diamonds, the same platform could serve other quantum-sensing applications beyond interferometry.
  • The choice of 40 × 65 × 80 nm is probably a compromise between preserving NV coherence (favoring larger sizes) and maximizing the quantum effects of mass (favoring smaller sizes); systematic variation of size would map this trade-off.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 3 minor

Summary. The manuscript, titled 'Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects', presents an abstract that claims the design, fabrication, and characterization of nanodiamond pillars of dimensions 40 x 65 x 80 nm containing single NV centers, as part of a series of technical notes aimed at matter-wave interferometry. However, the full text supplied for review is an entirely different paper, 'In-Context Decision Making for Optimizing Complex AutoML Pipelines' by A.R. Balef and K. Eggensperger, which concerns bandit-based AutoML and contains no mention of nanodiamonds, NV centers, or interferometry. The abstract itself provides no experimental or characterization details, no data, and no derivations.

Significance. If substantiated, the fabrication of single-NV nanodiamond pillars with controlled dimensions and coherence would be a valuable step toward matter-wave interferometry with massive objects, and the authors' intention to share technical notes could benefit the community. However, as submitted, the manuscript offers no verifiable evidence for these claims. The scientific content in the full text is unrelated, so the central assertion is unsupported. The paper's potential significance cannot be assessed from the supplied material.

major comments (2)
  1. [Abstract vs. Full text] The central claim of the abstract—that nanodiamond pillars of 40 x 65 x 80 nm with single NV centers were fabricated and characterized—is not present in the full text. The full text is a paper on AutoML with different authors and topic. This is a load-bearing mismatch: there is no methods section, no fabrication procedure, no characterization data, and no analysis anywhere in the manuscript that supports the abstract. This cannot be resolved by local revision; the submission must be replaced.
  2. [Abstract] Even taken alone, the abstract provides no data or references to support the claimed dimensions, single-NV occupancy, or coherence properties. The sentence 'We would be happy to make available more details upon request' is not a substitute for the detailed technical note the abstract purports to be. The claimed characterization results are unverifiable.
minor comments (3)
  1. [Abstract] The phrase 'General relativity (GR), also known as the theory of gravity' is imprecise; GR is a theory of gravity, not synonymous with 'the theory of gravity'.
  2. [Abstract] The abstract refers to 'a series of seven such notes' without providing access to the other notes or citations; please clarify how readers can find them.
  3. [Full text] The full text appears to be a different submission; please verify the uploaded file and ensure the correct manuscript is associated with this arXiv identifier.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation chain exists; the abstract's nanodiamond claims are not addressed by the supplied full text, so no equation-level circularity can be identified.

full rationale

The abstract of arXiv:2508.13662 claims fabrication and characterization of 40x65x80 nm nanodiamond pillars with a single NV center for matter-wave interferometry, including a review of fabrication processes and completed characterization. The supplied full text is arXiv:2508.13657v2, 'In-Context Decision Making for Optimizing Complex AutoML Pipelines' by Balef and Eggensperger, which contains no mention of nanodiamonds, NV centers, fabrication, or interferometry. There is therefore no derivation, fitted parameter, or self-citation chain that could reduce the abstract's central claim to its own inputs. Circularity requires some explicit reduction—an equation, a fitted quantity renamed as a prediction, or a load-bearing self-citation—and no such step is present. The mismatch is a severe verifiability problem: the abstract's central claim is unsupported by the text provided, but unsupportedness is not circularity. Consequently, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the material feasibility of single-NV nanodiamonds and on the reported fabrication processes. The abstract provides no evidence for either.

assumptions (1)
  • domain assumption A 40 x 65 x 80 nm nanodiamond can host a single NV center with sufficient optical addressability and spin coherence.
    The entire source design depends on this physical feasibility, which is the stated purpose of the note but not demonstrated in the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects." pith.science (2026). https://pith.science/paper/JJ4TS64C

@misc{pith2026250813662,
  author       = {Pith},
  title        = {Pith review of: Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JJ4TS64C}},
  note         = {Machine review of arXiv:2508.13662}
}
read the original abstract

Quantum mechanics (QM) and General relativity (GR), also known as the theory of gravity, are the two pillars of modern physics. A matter-wave interferometer with a massive particle can test numerous fundamental ideas, including the spatial superposition principle - a foundational concept in QM - in previously unexplored regimes. It also opens the possibility of probing the interface between QM and GR, such as testing the quantization of gravity. Consequently, there exists an intensive effort to realize such an interferometer. While several approaches are being explored, we focus on utilizing nanodiamonds with embedded spins as test particles which, in combination with Stern-Gerlach forces, enable the realization of a closed-loop matter-wave interferometer in space-time. There is a growing community of groups pursuing this path [1]. We are posting this technical note (as part of a series of seven such notes), to highlight our plans and solutions concerning various challenges in this ambitious endeavor, hoping this will support this growing community. Here we discuss the design considerations for a high-precision enhanced-coherence nanodiamond source, review the fabrication processes used to produce nanodiamond pillars measuring 40 x 65 x 80 nm, summarize the characterization work completed to date, and conclude with an outlook on the remaining steps needed to finalize the source fabrication. We would be happy to make available more details upon request.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Quantum control of Nitrogen-Vacancy spin in Diamonds: Towards matter-wave interferometry with massive objects

    quant-ph 2025-08 reject novelty 3.0 of 10

    The paper shows routine NV spin measurements and claims a feasibility simulation, but the simulation and its derivation are not included.

Reference graph

Works this paper leans on

60 extracted references · 47 canonical work pages · cited by 1 Pith paper

  1. [2]

    Convergence Analysis.For the convergence analysis, letF n(x)denote the empirical CDF estimated fromni.i.d

    SolveF max(x) =U: [F(x)] t =U=⇒F(x) =U 1/t =⇒x=F −1(U 1/t) Thus,max(r 1:t) =F −1(U 1/t)follows the correct distribution. Convergence Analysis.For the convergence analysis, letF n(x)denote the empirical CDF estimated fromni.i.d. samples. Using the Dvoretzky-Kiefer-Wolfowitz (DKW) inequality: P sup x |Fn(x)−F(x)| ≥ϵ ≤δ,withϵ= r ln(2/δ) 2n .(11) Now we want ...

    work page 2000

  2. [6]

    Adriaensen, H

    S. Adriaensen, H. Rakotoarison, S. Müller, and F. Hutter. Efficient Bayesian learning curve extrapolation using prior-data fitted networks. InProc. of NeurIPS’23neu [3]

    work page

  3. [7]

    S. P. Arango, F. Ferreira, A. Kadra, F. Hutter, and J. Grabocka. Quick- tune: Quickly learning which pretrained model to finetune and how. In Proc. of ICLR’24icl [4]

    work page

  4. [8]

    A. R. Balef, C. Vernade, and K. Eggensperger. Towards bandit- based optimization for automated machine learning. In5th Work- shop on practical ML for limited/low resource settings, 2024. URL https://openreview.net/forum?id=S5da3rzyuk

    work page 2024

  5. [9]

    A. R. Balef, C. Vernade, and K. Eggensperger. Put CASH on bandits: A max k-armed problem for automated machine learning.arXiv preprint arXiv:2505.05226, 2025

    arXiv 2025

  6. [10]

    Baudry, P

    D. Baudry, P. Saux, and O.-A. Maillard. From optimality to robust- ness: Adaptive re-sampling strategies in stochastic bandits. InProc. of NeurIPS’21neu [1]

    work page

  7. [11]

    Bergman, M

    E. Bergman, M. Feurer, A. Bahram, A. R. Balef, L. Purucker, S. Segel, M. Lindauer, F. Hutter, and K. Eggensperger. AMLTK: A Modular Automl Toolkit in Python.Journal of Open Source Software, 9(100): 6367, 2024. doi: 10.21105/joss.06367. URL https://doi.org/10.21105/ joss.06367

    doi:10.21105/joss.06367 2024

  8. [12]

    Bischl, G

    B. Bischl, G. Casalicchio, T. Das, M. Feurer, S. Fischer, P. Gijs- bers, S. Mukherjee, A. C. Müller, L. Németh, L. Oala, L. Purucker, S. Ravi, J. N. van Rijn, P. Singh, J. Vanschoren, J. van der Velde, and M. Wever. Openml: Insights from 10 years and more than a thou- sand papers.Patterns, 6(7):101317, 2025. ISSN 2666-3899. doi: https://doi.org/10.1016/j....

    work page arXiv 2025

Show all 60 references
  1. [13]

    F. d. Breejen, S. Bae, S. Cha, and S.-Y . Yun. Fine-tuned in-context learn- ing transformers are excellent tabular data classifiers.arXiv preprint arXiv:2405.13396, 2024

    2024 arXiv

  2. [14]

    Brown, B

    T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert- V oss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Ches...

    1901

  3. [15]

    Cayci, A

    S. Cayci, A. Eryilmaz, and R. Srikant. Budget-constrained bandits over general cost and reward distributions. InProc. of AISTATS’20, 2020

    2020

  4. [16]

    L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch. Decision transformer: Rein- forcement learning via sequence modeling. InProc. of NeurIPS’21neu [1]

  5. [17]

    Chen and C

    T. Chen and C. Guestrin. XGBoost: A scalable tree boosting system. In Proc. of KDD’16, pages 785–794, 2016

    2016

  6. [18]

    Cowen-Rivers, W

    A. Cowen-Rivers, W. Lyu, R. Tutunov, Z. Wang, A. Grosnit, R. Grif- fiths, A. Maraval, H. Jianye, J. Wang, J. Peters, and H. Ammar. HEBO: Pushing the limits of sample-efficient hyper-parameter optimisation. Journal of Artificial Intelligence Research, 74:1269–1349, 2022

    2022

  7. [19]

    L. Cui, H. Li, K. Chen, L. Shou, and G. Chen. Tabular data augmenta- tion for machine learning: Progress and prospects of embracing gener- ative ai.arXiv:2407.21523 [cs.LG], 2024

    2024 arXiv

  8. [20]

    W. Cui, R. Hosseinzadeh, J. Ma, T. Wu, Y . Sui, and K. Golestan. Tabular data contrastive learning via class-conditioned and feature-correlation based augmentation.arXiv preprint arXiv:2404.17489, 2024

    2024 arXiv

  9. [21]

    W. Ding, T. Qin, X.-D. Zhang, and T.-Y . Liu. Multi-armed bandit with budget constraint and variable costs. InProc. of AAAI’13, volume 27, pages 232–238, 2013

    2013

  10. [22]

    Feuer, R

    B. Feuer, R. T. Schirrmeister, V . Cherepanova, C. Hegde, F. Hutter, M. Goldblum, N. Cohen, and C. White. Tunetables: Context optimiza- tion for scalable prior-data fitted networks. InProc. of NeurIPS’24neu [5]

  11. [23]

    Feurer, A

    M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter. Efficient and robust automated machine learning. InProc. of NeurIPS’15, pages 2962–2970, 2015

    2015

  12. [24]

    Feurer, K

    M. Feurer, K. Eggensperger, S. Falkner, M. Lindauer, and F. Hutter. Auto-Sklearn 2.0: Hands-free automl via meta-learning.Journal of Ma- chine Learning Research, 23(261):1–61, 2022

    2022

  13. [25]

    Fiandri, A

    M. Fiandri, A. M. Metelli, and F. Trovò. Thompson sampling-like al- gorithms for stochastic rising rested bandits. InSeventeenth European Workshop on Reinforcement Learning, 2024. URL https://openreview. net/forum?id=jaFhipqjxR

    2024

  14. [26]

    Gorishniy, I

    Y . Gorishniy, I. Rubachev, V . Khrulkov, and A. Babenko. Revisiting deep learning models for tabular data. InProc. of NeurIPS’21neu [1]

  15. [27]

    Hollmann, S

    N. Hollmann, S. Müller, K. Eggensperger, and F. Hutter. TabPFN: A transformer that solves small tabular classification problems in a sec- ond. InProc. of ICLR’23, 2023

    2023

  16. [28]

    Hollmann, S

    N. Hollmann, S. Müller, and F. Hutter. Large language models for auto- mated data science: Introducing CAAFE for context-aware automated feature engineering.arXiv:2305.03403[v5] [cs.AI], 2023

    2023 arXiv

  17. [29]

    Hollmann, S

    N. Hollmann, S. Müller, and F. Hutter. Accurate predictions on small data with a tabular foundation model.Nature, 637:319–326, 2025. doi: 10.1038/s41586-024-08328-6. URL https://www.nature.com/articles/ s41586-024-08328-6

    2025 doi

  18. [30]

    Holzmüller, L

    D. Holzmüller, L. Grinsztajn, and I. Steinwart. Better by default: Strong pre-tuned mlps and boosted trees on tabular data. InProc. of NeurIPS’24neu [5]

  19. [31]

    Y . Hu, X. Liu, and S. L. Y . Yu. Cascaded algorithm selection with extreme-region UCB bandit.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6782–6794, 2021

    2021

  20. [32]

    Kingma and J

    D. Kingma and J. Ba. Adam: A method for stochastic optimization. In Proc. of ICLR’15, 2015

    2015

  21. [33]

    Komer, J

    B. Komer, J. Bergstra, and C. Eliasmith. Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn. InICML Workshop on AutoML, 2014

    2014

  22. [34]

    Kotthoff, C

    L. Kotthoff, C. Thornton, H. H. Hoos, F. Hutter, and K. Leyton-Brown. Auto-WEKA 2.0: Automatic model selection and hyperparameter op- timization in WEKA.Journal of Machine Learning Research, 18(25): 1–5, 2017

    2017

  23. [35]

    Kveton, B

    B. Kveton, B. Oreshkin, Y . Park, A. A. Deshmukh, and R. Song. Online posterior sampling with a diffusion prior. InProc. of NeurIPS’24neu [5]

  24. [36]

    Lattimore and C

    T. Lattimore and C. Szepesvári.Bandit Algorithms. Cambridge Univer- sity Press, 2020

    2020

  25. [37]

    E. H. Lee, V . Perrone, C. Archambeau, and M. Seeger. Cost-aware bayesian optimization. InProc. of UAI’20, 2020

    2020

  26. [38]

    J. Lee, A. Xie, A. Pacchiano, Y . Chandak, C. Finn, O. Nachum, and E. Brunskill. Supervised pretraining can learn in-context reinforcement learning. InProc. of NeurIPS’23neu [3]

  27. [39]

    Y . Li, J. Jiang, J. Gao, Y . Shao, C. Zhang, and B. Cui. Efficient auto- matic CASH via rising bandits. InProc. of AAAI’20, pages 4763–4771, 2020

    2020

  28. [40]

    L. Lin, Y . Bai, and S. Mei. Transformers as decision makers: Provable in-context reinforcement learning via supervised pretraining. InProc. of ICLR’24icl [4]

  29. [41]

    Y . Liu, B. Van Roy, and K. Xu. Nonstationary bandit learning via pre- dictive sampling. InProc. of AISTATS’23, 2023

    2023

  30. [42]

    Loshchilov and F

    I. Loshchilov and F. Hutter. SGDR: Stochastic gradient descent with warm restarts. InProc. of ICLR’17, 2017

    2017

  31. [43]

    Müller, N

    S. Müller, N. Hollmann, S. Arango, J. Grabocka, and F. Hutter. Trans- formers can do Bayesian inference. InProc. of ICLR’22, 2022

    2022

  32. [44]

    S. G. Müller, M. Feurer, N. Hollmann, and F. Hutter. PFNs4BO: In- context learning for bayesian optimization. InProc. of ICML’23icm [2]

  33. [45]

    Nagler, L

    T. Nagler, L. Schneider, B. Bischl, and M. Feurer. Reshuffling resam- pling splits can improve generalization of hyperparameter optimization. neu [5]

  34. [46]

    Nishihara, D

    R. Nishihara, D. Lopez-Paz, and L. Bottou. No regret bound for extreme bandits. InProc. of AISTATS’16, 2016

    2016

  35. [47]

    Pfisterer, L

    F. Pfisterer, L. Schneider, J. Moosbauer, M. Binder, and B. Bischl. Y AHPO Gym – an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization. InProc. of AutoML Conf’22. PMLR, 2022

    2022

  36. [48]

    M. Phan, Y . Abbasi Yadkori, and J. Domke. Thompson sampling and approximate inference. InProc. of NeurIPS’19, 2019

    2019

  37. [49]

    Prokhorenkova, G

    L. Prokhorenkova, G. Gusev, A. V orobev, A. Dorogush, and A. Gulin. Catboost: Unbiased boosting with categorical features. InProc. of NeurIPS’18, page 6639–6649, 2018

    2018

  38. [50]

    Pushak and H

    Y . Pushak and H. Hoos. Automl loss landscapes.ACM Transactions on Evolutionary Learning and Optimization, 2(3):1–30, 2022

    2022

  39. [51]

    Russo and B

    D. Russo and B. Van Roy. Learning to optimize via posterior sampling. Mathematics of Operations Research, 39(4):1221–1243, 2014

    2014

  40. [52]

    Salinas and N

    D. Salinas and N. Erickson. TabRepo: A large scale repository of tabular model evaluations and its AutoML applications. InProc. of AutoML Conf’24. PMLR, 2024

    2024

  41. [53]

    C. Shen, X. Zhang, W. Wei, and J. Xu. Hyperbandit: Contextual ban- dit with hypernewtork for time-varying user preferences in streaming recommendation. InProc. of CIKM’23, 2023

    2023

  42. [54]

    Snoek, H

    J. Snoek, H. Larochelle, and R. P. Adams. Practical Bayesian optimiza- tion of machine learning algorithms. InProc. of NeurIPS’12, 2012

    2012

  43. [55]

    W. R. Thompson. On the likelihood that one unknown probability ex- ceeds another in view of the evidence of two samples.Biometrika, 25 (3-4):285–294, 1933

    1933

  44. [56]

    Thornton, F

    C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown. Auto-WEKA: combined selection and Hyperparameter Optimization of classification algorithms. InProc. of KDD’13, pages 847–855, 2013

    2013

  45. [57]

    van den Nieuwenhuijzen, C

    M. van den Nieuwenhuijzen, C. Doerr, J. N. van Rijn, and H. Gouk. Se- lecting pre-trained models for transfer learning with data-centric meta- features. InAutoML Conference 2024 (Workshop Track), 2024

    2024

  46. [58]

    Vaswani, N

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. InProc. of NeurIPS’17. Curran Associates, Inc., 2017

    2017

  47. [59]

    C. Wang, Q. Wu, M. Weimer, and E. Zhu. Flaml: A fast and lightweight automl library. InProc. of MLSys’21, pages 434–447, 2021

    2021

  48. [60]

    Y . Xia, H. Li, T. Qin, N. Yu, and T.-Y . Liu. Thompson sampling for budgeted multi-armed bandits. InProc. of IJCAI’15, 2015

    2015

  49. [61]

    Y . Xia, H. Li, T. Qin, N. Yu, and T.-Y . Liu. Thompson sampling for budgeted multi-armed bandits.arXiv preprint arXiv:1505.00146, 2015

    2015 arXiv

  50. [62]

    Y . Xia, W. Ding, X.-D. Zhang, N. Yu, and T. Qin. Budgeted bandit problems with continuous random costs. InProc. of ACML’16, 2016

    2016

  51. [63]

    Q. Xie, R. Astudillo, P. Frazier, Z. Scully, and A. Terenin. Cost-aware bayesian optimization via the pandora’s box gittins index. InProc. of NeurIPS’24neu [5]

  52. [64]

    B. Zhu, X. Shi, N. Erickson, M. Li, G. Karypis, and M. Shoaran. Xtab: Cross-table pretraining for tabular transformers. InProc. of ICML’23 icm [2]. Table of Contents for the Appendices •Appendix A: Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.