{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:07:26Z","timestamp":1772755646676,"version":"3.50.1"},"reference-count":59,"publisher":"Informa UK Limited","issue":"1","content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational and Graphical Statistics"],"published-print":{"date-parts":[[2026,1,2]]},"DOI":"10.1080\/10618600.2025.2530048","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T17:00:46Z","timestamp":1752253246000},"page":"330-338","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Doubly Adaptive Importance Sampling"],"prefix":"10.1080","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1777-3857","authenticated-orcid":false,"given":"Willem","family":"van den Boom","sequence":"first","affiliation":[{"name":"Institute for Human Development and Potential, Agency for Science, Technology and Research","place":["Singapore"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1179-0768","authenticated-orcid":false,"given":"Andrea","family":"Cremaschi","sequence":"additional","affiliation":[{"name":"School of Science and Technology, IE University","place":["Madrid, Spain"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9542-509X","authenticated-orcid":false,"given":"Alexandre H.","family":"Thiery","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, National University of Singapore","place":["Singapore"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"301","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-STS611"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-020-09983-1"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017746"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AAP1113"},{"key":"e_1_3_3_6_1","volume-title":"Pattern Recognition and Machine Learning, Information Science and Statistics","author":"Bishop C. M.","year":"2006","unstructured":"Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Information Science and Statistics, New York: Springer."},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_3_8_1","unstructured":"Bradbury J. Frostig R. Hawkins P. Johnson M. J. Leary C. Maclaurin D. and Wanderman-Milne S. (2018) \u201cJAX: Composable Transformations of Python\u2009+\u2009NumPy Programs \u201d http:\/\/github.com\/google\/jax."},{"key":"e_1_3_3_9_1","unstructured":"Branchini N. and Elvira V. (2024) \u201cGeneralizing Self-Normalized Importance Sampling with Couplings \u201d arXiv:2406.19974v1."},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2699226"},{"key":"e_1_3_3_11_1","unstructured":"Cabezas A. Corenflos A. Lao J. Louf R. Carnec A. Chaudhari K. et\u00a0al. (2024) \u201cBlackJAX: Composable Bayesian Inference in JAX \u201d arXiv:2402.10797v2."},{"key":"e_1_3_3_12_1","unstructured":"Chen Y. Huang D. Z. Huang J. Reich S. and Stuart A. M. (2023) \u201cGradient Flows for Sampling: Mean-Field Models Gaussian Approximations and Affine Invariance \u201d arXiv:2302.11024v7."},{"key":"e_1_3_3_13_1","unstructured":"Chopin N. Crucinio F. R. and Korba A. (2024) \u201cA Connection between Tempering and Entropic Mirror Descent \u201d arXiv:2310.11914v3."},{"key":"e_1_3_3_14_1","volume-title":"Springer Series in Statistics","author":"Chopin N.","year":"2020","unstructured":"Chopin, N., and Papaspiliopoulos, O. (2020), An Introduction to Sequential Monte Carlo, Springer Series in Statistics, Cham: Springer Nature."},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9469.2011.00756.x"},{"key":"e_1_3_3_16_1","first-page":"1","article-title":"\u201cMonotonic Alpha-Divergence Minimisation for Variational Inference,\u201d","volume":"24","author":"Daudel K.","year":"2023","unstructured":"Daudel, K., Douc, R., and Roueff, F. (2023), \u201cMonotonic Alpha-Divergence Minimisation for Variational Inference,\u201d Journal of Machine Learning Research, 24, 1\u201376.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_17_1","unstructured":"Dehaene G. P. (2016) \u201cExpectation Propagation Performs a Smoothed Gradient Descent \u201d arXiv:1612.05053v1."},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2006.00553.x"},{"key":"e_1_3_3_19_1","volume-title":"Advances in Neural Information Processing Systems","author":"Domke J.","year":"2018","unstructured":"Domke, J., and Sheldon, D. R. (2018), \u201cImportance Weighting and Variational Inference,\u201d in Advances in Neural Information Processing Systems (Vol. 31), Curran Associates, Inc."},{"key":"e_1_3_3_20_1","unstructured":"Dua D. and Graff C. (2017) \u201cUCI Machine Learning Repository \u201d University of California Irvine School of Information and Computer Sciences http:\/\/archive.ics.uci.edu\/ml."},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3172619"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2023.06.041"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/insr.12500"},{"key":"e_1_3_3_24_1","first-page":"1022","volume-title":"Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (Vol","volume":"2","author":"Fayyad U. M.","year":"1993","unstructured":"Fayyad, U. M., and Irani, K. B. (1993), \u201cMulti-Interval Discretization of Continuous-Valued Attributes for Classification Learning,\u201d in Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (Vol. 2), pp. 1022\u20131027."},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOAS191"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717761"},{"key":"e_1_3_3_27_1","volume-title":"Advances in Neural Information Processing Systems","author":"Grosse R. B.","year":"2013","unstructured":"Grosse, R. B., Maddison, C. J., and Salakhutdinov, R. R. (2013), \u201cAnnealing between Distributions by Averaging Moments,\u201d in Advances in Neural Information Processing Systems (Vol. 26), Curran Associates, Inc."},{"key":"e_1_3_3_28_1","first-page":"3871","volume-title":"Proceedings of The 27th International Conference on Artificial Intelligence and Statistics","author":"Guilmeau T.","year":"2024","unstructured":"Guilmeau, T., Branchini, N., Chouzenoux, E., and Elvira, V. (2024a), \u201cAdaptive Importance Sampling for Heavy-Tailed Distributions via \u03b1 -divergence Minimization,\u201d in Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Volume 238 of Proceedings of Machine Learning Research, pp. 3871\u20133879, PMLR."},{"key":"e_1_3_3_29_1","unstructured":"Guilmeau T. Chouzenoux E. and Elvira V. (2024b) \u201cRegularized R\u00e9nyi Divergence Minimization through Bregman Proximal Gradient Algorithms \u201d arXiv:2211.04776v4."},{"key":"e_1_3_3_30_1","volume-title":"Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017","author":"Han J.","year":"2017","unstructured":"Han, J., and Liu, Q. (2017), \u201cStein Variational Adaptive Importance Sampling,\u201d in Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11\u201315, 2017, AUAI Press."},{"key":"e_1_3_3_31_1","first-page":"1511","volume-title":"Proceedings of The 33rd International Conference on Machine Learning","author":"Hern\u00e1ndez-Lobato J.","year":"2016","unstructured":"Hern\u00e1ndez-Lobato, J., Li, Y., Rowland, M., Bui, T., Hern\u00e1ndez-Lobato, D., and Turner, R. (2016), \u201cBlack-Box \u03b1 -Divergence Minimization,\u201d in Proceedings of The 33rd International Conference on Machine Learning, Volume 48 of Proceedings of Machine Learning Research, New York, NY, pp. 1511\u20131520, PMLR."},{"key":"e_1_3_3_32_1","first-page":"1819","volume-title":"Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence","author":"Jerfel G.","year":"2021","unstructured":"Jerfel, G., Wang, S., Wong-Fannjiang, C., Heller, K. A., Ma, Y., and Jordan, M. I. (2021), \u201cVariational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence,\u201d in Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, Volume 161 of Proceedings of Machine Learning Research, pp. 1819\u20131829, PMLR."},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.23919\/ISITA.2018.8664326"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-013-9440-2"},{"key":"e_1_3_3_35_1","volume-title":"Advances in Neural Information Processing Systems","author":"Li Y.","year":"2016","unstructured":"Li, Y., and Turner, R. E. (2016), \u201cR\u00e9nyi Divergence Variational Inference,\u201d in Advances in Neural Information Processing Systems (Vol. 29), Curran Associates, Inc."},{"key":"e_1_3_3_36_1","unstructured":"Lin W. Khan M. E. and Schmidt M. (2019) \u201cStein\u2019s Lemma for the Reparameterization Trick with Exponential Family Mixtures \u201d arXiv:1910.13398v1."},{"key":"e_1_3_3_37_1","volume-title":"Advances in Neural Information Processing Systems","author":"Liu Q.","year":"2016","unstructured":"Liu, Q., and Wang, D. (2016), \u201cStein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm,\u201d in Advances in Neural Information Processing Systems (Vol. 29), Curran Associates, Inc."},{"key":"e_1_3_3_38_1","first-page":"146","article-title":"\u201cNew Insights and Perspectives on the Natural Gradient Method,\u201d","volume":"21","author":"Martens J.","year":"2020","unstructured":"Martens, J. (2020), \u201cNew Insights and Perspectives on the Natural Gradient Method,\u201d Journal of Machine Learning Research, 21, 146.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2016.08.025"},{"key":"e_1_3_3_40_1","unstructured":"Minka T. (2004) \u201cPower EP \u201d Technical Report MSR-TR-2004-149 Microsoft Research Ltd. Cambridge UK."},{"key":"e_1_3_3_41_1","unstructured":"Minka T. (2005) \u201cDivergence Measures and Message Passing \u201d Technical Report MSR-TR-2005-173 Microsoft Research Ltd. Cambridge UK."},{"key":"e_1_3_3_42_1","first-page":"362","volume-title":"Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence","author":"Minka T. P.","year":"2001","unstructured":"Minka, T. P. (2001), \u201cExpectation Propagation for Approximate Bayesian Inference,\u201d in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 362\u2013369."},{"key":"e_1_3_3_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-012-9344-6"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008923215028"},{"key":"e_1_3_3_45_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12185"},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2017.1390472"},{"key":"e_1_3_3_47_1","first-page":"2177","article-title":"\u201cExpectation Consistent Approximate Inference,\u201d","volume":"6","author":"Opper M.","year":"2005","unstructured":"Opper, M., and Winther, O. (2005), \u201cExpectation Consistent Approximate Inference,\u201d Journal of Machine Learning Research, 6, 2177\u20132204.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-020-09982-2"},{"key":"e_1_3_3_49_1","unstructured":"Prangle D. and Viscardi C. (2022) \u201cDistilling Importance Sampling \u201d arXiv:1910.03632v4."},{"key":"e_1_3_3_50_1","first-page":"5537","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Ruiz F.","year":"2019","unstructured":"Ruiz, F., and Titsias, M. (2019), \u201cA Contrastive Divergence for Combining Variational Inference and MCMC,\u201d in Proceedings of the 36th International Conference on Machine Learning, Volume 97 of Proceedings of Machine Learning Research, pp. 5537\u20135545, PMLR."},{"key":"e_1_3_3_51_1","unstructured":"Ryu E. K. and Boyd S. P. (2015) \u201cAdaptive Importance Sampling via Stochastic Convex Programming \u201d arXiv:1412.4845v2."},{"key":"e_1_3_3_52_1","doi-asserted-by":"publisher","DOI":"10.1137\/16M1093549"},{"key":"e_1_3_3_53_1","unstructured":"Schuster I. (2015) \u201cGradient Importance Sampling.\u201d"},{"key":"e_1_3_3_54_1","first-page":"583","volume-title":"Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, volume 2: Probability Theory","volume":"6","author":"Stein C.","year":"1972","unstructured":"Stein, C. (1972), \u201cA Bound For the Error in the Normal Approximation to the Distribution of a Sum of Dependent Random Variables,\u201d in Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, volume 2: Probability Theory (Vol. 6), pp. 583\u2013603, University of California Press."},{"key":"e_1_3_3_55_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492910000061"},{"key":"e_1_3_3_56_1","first-page":"1","article-title":"\u201cExpectation Propagation As A Way Of Life: A Framework for Bayesian Inference on Partitioned Data,\u201d","volume":"21","author":"Vehtari A.","year":"2020","unstructured":"Vehtari, A., Gelman, A., Sivula, T., Jyl\u00e4nki, P., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J. P., Schiminovich, D., and Robert, C. P. (2020), \u201cExpectation Propagation As A Way Of Life: A Framework for Bayesian Inference on Partitioned Data,\u201d Journal of Machine Learning Research, 21, 1\u201353.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_57_1","unstructured":"Vehtari A. Simpson D. Gelman A. Yao Y. and Gabry J. (2024) \u201cPareto Smoothed Importance Sampling \u201d arXiv:1507.02646v9."},{"key":"e_1_3_3_58_1","volume-title":"Advances in Neural Information Processing Systems","author":"Wang D.","year":"2018","unstructured":"Wang, D., Liu, H., and Liu, Q. (2018), \u201cVariational Inference with Tail-Adaptive f-divergence,\u201d in Advances in Neural Information Processing Systems (Vol. 31), Curran Associates, Inc."},{"key":"e_1_3_3_59_1","volume-title":"Advances in Neural Information Processing Systems","author":"Wiegerinck W.","year":"2003","unstructured":"Wiegerinck, W., and Heskes, T. (2003), \u201cFractional Belief Propagation,\u201d in Advances in Neural Information Processing Systems (Vol. 15), MIT Press."},{"key":"e_1_3_3_60_1","first-page":"5581","volume-title":"Proceedings of the 35th International Conference on Machine Learning","author":"Yao Y.","year":"2018","unstructured":"Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018), \u201cYes, But Did It Work?: Evaluating Variational Inference,\u201d in Proceedings of the 35th International Conference on Machine Learning, Volume 80 of Proceedings of Machine Learning Research, pp. 5581\u20135590, PMLR."}],"container-title":["Journal of Computational and Graphical Statistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/10618600.2025.2530048","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:14:47Z","timestamp":1772752487000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/10618600.2025.2530048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,2]]}},"alternative-id":["10.1080\/10618600.2025.2530048"],"URL":"https:\/\/doi.org\/10.1080\/10618600.2025.2530048","relation":{},"ISSN":["1061-8600","1537-2715"],"issn-type":[{"value":"1061-8600","type":"print"},{"value":"1537-2715","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ucgs20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ucgs20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2024-04-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}