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Home All issues Volume 693 (January 2025) A&A, 693 (2025) A256 References
Open Access
Issue
A&A
Volume 693, January 2025
Article Number A256
Number of page(s) 27
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202451073
Published online 23 January 2025
  1. Abazajian, K. N., et al. 2009, ApJS, 182, 543 [NASA ADS] [CrossRef] [Google Scholar]
  2. Baron, D. 2019, arXiv e-prints [arXiv:1904.07248] [Google Scholar]
  3. Bengio, Y., Courville, A., & Vincent, P. 2013, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1798 [CrossRef] [Google Scholar]
  4. Boffin, H. M. J., Vinther, J., Lundin, L. K., & Bazin, G. 2020, SPIE, 11449, 114491B [NASA ADS] [Google Scholar]
  5. Cavanagh, M. K., Bekki, K., & Groves, B. A. 2021, MNRAS, 506, 659 [NASA ADS] [CrossRef] [Google Scholar]
  6. Chen, X., Duan, Y., Houthooft, R., et al. 2016, in NIPS’ 16 (Red Hook, NY, USA: Curran Associates Inc.), 2180 [Google Scholar]
  7. Cranmer, K., Brehmer, J., & Louppe, G. 2020, PNAS, 117, 30055 [NASA ADS] [CrossRef] [Google Scholar]
  8. Dieng, A. B., Kim, Y., Rush, A. M., & Blei, D. M. 2019, in AISTATS, PMLR, 2397 [Google Scholar]
  9. Dittadi, A., Träuble, F., Locatello, F., et al. 2021, in ICLR, [openreview: 8VXvj1QNRl1] [Google Scholar]
  10. Fabbro, S., Venn, K., O’Briain, T., et al. 2018, MNRAS, 475, 2978 [CrossRef] [Google Scholar]
  11. Falcon, W., & The PyTorch Lightning team 2019, PyTorch Lightning [Google Scholar]
  12. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. 2014, in NIPS’ 14, 27, eds. Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Weinberger, (Curran Associates, Inc.), 2672 [Google Scholar]
  13. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press) [Google Scholar]
  14. Gordon, D. F., & Desjardins, M. 1995, Mach. Learn., 20, 5 [Google Scholar]
  15. Gray, D. F., & Kaur, T. 2019, ApJ, 882, 148 [NASA ADS] [CrossRef] [Google Scholar]
  16. Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. 2012, J. Mach. Learn. Res., 13, 723 [Google Scholar]
  17. Gullikson, K., Dodson-Robinson, S., & Kraus, A. 2014, AJ, 148, 53 [NASA ADS] [CrossRef] [Google Scholar]
  18. He, K., Zhang, X., Ren, S., & Sun, J. 2016, in CVPR, 770 [Google Scholar]
  19. Higgins, I., Matthey, L., Pal, A., et al. 2017, in ICLR [openreview:Sy2fzU9gl] [Google Scholar]
  20. Holm, S. 1979, Scand. J. Stat., 6, 65 [Google Scholar]
  21. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. 2017, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700 [Google Scholar]
  22. Kingma, D. P., & Welling, M. 2014, in ICLR, Banff, Canada [arXiv:1312.6114] [Google Scholar]
  23. Kingma, D. P., & Ba, J. 2015, in ICLR [arXiv:1412.6980] [Google Scholar]
  24. Kingma, D. P., Rezende, D. J., Mohamed, S., & Welling, M. 2014, in NIPS’ 14 (Cambridge, MA,: MIT Press), 3581 [Google Scholar]
  25. Kurucz, R. L. 2005, Mem. Soc. Astron. Ital. Suppl., 8, 14 [Google Scholar]
  26. Le, L., Patterson, A., & White, M. 2018, in NIPS’ 18 (Red Hook, NY, USA: Curran Associates Inc.), 107 [Google Scholar]
  27. Leeb, F., Bauer, S., Besserve, M., & Schölkopf, B. 2022, in NIPS’ 22 (Red Hook, NY, USA: Curran Associates Inc.) [Google Scholar]
  28. Leung, H. W., & Bovy, J. 2019, MNRAS, 483, 3255 [NASA ADS] [Google Scholar]
  29. Locatello, F., Bauer, S., Lucic, M., et al. 2020, JMLR, 21, 1 [Google Scholar]
  30. Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, CoRR, [arXiv:1709.06257] [Google Scholar]
  31. Mann, H. B., & Whitney, D. R. 1947, Ann. Math. Stat., 18, 50 [Google Scholar]
  32. Mayor, M., Pepe, F., Queloz, D., et al. 2003, The Messenger, 114, 20 [NASA ADS] [Google Scholar]
  33. Miller, N. J., Maxted, P. F. L., & Smalley, B. 2020, MNRAS, 497, 2899 [Google Scholar]
  34. Montero, M., Bowers, J., Ponte Costa, R., Ludwig, C., & Malhotra, G. 2022, in NIPS’ 22, 35, eds. S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Curran Associates, Inc.), 10136 [Google Scholar]
  35. Mucciarelli, A. 2019, Autokur, Unpublished/in proceeding [Google Scholar]
  36. Murphy, K. P. 2022, Probabilistic Machine Learning: An Introduction (MIT Press) [Google Scholar]
  37. Murphy, K. P. 2023, Probabilistic Machine Learning: Advanced Topics (MIT Press) [Google Scholar]
  38. Muyskens, A. L., Goumiri, I. R., Priest, B. W., et al. 2022, AJ, 163, 148 [NASA ADS] [CrossRef] [Google Scholar]
  39. Nakkiran, P., Kaplun, G., Bansal, Y., et al. 2021, J. Stat. Mech.: Theory Exp., 2021, 124003 [Google Scholar]
  40. Ness, M., Hogg, D. W., Rix, H.-W., Ho, A. Y. Q., & Zasowski, G. 2015, ApJ, 808, 16 [NASA ADS] [CrossRef] [Google Scholar]
  41. O’Briain, T., Ting, Y.-S., Fabbro, S., et al. 2021, ApJ, 906, 130 [CrossRef] [Google Scholar]
  42. Perez, E., Strub, F., de Vries, H., Dumoulin, V., & Courville, A. 2018, in AAAI’ 18/IAAI’ 18/EAAI’ 18 (AAAI Press) [Google Scholar]
  43. Peters, J., Janzing, D., & Schlökopf, B. 2017, Elements of Causal Inference: Foundations and Learning Algorithms (The MIT Press) [Google Scholar]
  44. Portillo, S. K. N., Parejko, J. K., Vergara, J. R., & Connolly, A. J. 2020, AJ, 160, 45 [Google Scholar]
  45. Recio-Blanco, A., Kordopatis, G., de Laverny, P., et al. 2023, A&A, 674, A38 [CrossRef] [EDP Sciences] [Google Scholar]
  46. Rezende, D., & Mohamed, S. 2015, in ICML, PMLR, 1530 [Google Scholar]
  47. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. 2022, in CVPR, 10684 [Google Scholar]
  48. Scott, D. W. 1992, Multivariate Density Estimation: Theory, Practice, and Visualization (John Wiley & Sons) [Google Scholar]
  49. Sedaghat, N., Romaniello, M., Carrick, J. E., & Pineau, F.-X. 2021, MNRAS, 501, 6026 [NASA ADS] [CrossRef] [Google Scholar]
  50. Slijepcevic, I. V., Scaife, A. M. M., Walmsley, M., et al. 2022, MNRAS, 514, 2599 [NASA ADS] [CrossRef] [Google Scholar]
  51. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. 2015, in ICLR, PMLR, 2256 [Google Scholar]
  52. Stassun, K. G., et al. 2019, AJ, 158, 138 [NASA ADS] [CrossRef] [Google Scholar]
  53. Ting, Y.-S., Conroy, C., Rix, H.-W., & Cargile, P. 2019, ApJ, 879, 69 [Google Scholar]
  54. Zhao, S., Song, J., & Ermon, S. 2019, in AAAI’ 19/IAAI’ 19/EAAI’ 19 (AAAI Press) [Google Scholar]

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