{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inferno-inference-aware-neural-optimisation","title":"INFERNO: Inference-Aware Neural Optimisation","arxiv_id":"1806.04743","date":"2018-06-12","proceeding":null,"authors":["Pablo de Castro","Tommaso Dorigo"],"abstract":"Complex computer simulations are commonly required for accurate data\nmodelling in many scientific disciplines, making statistical inference\nchallenging due to the intractability of the likelihood evaluation for the\nobserved data. Furthermore, sometimes one is interested on inference drawn over\na subset of the generative model parameters while taking into account model\nuncertainty or misspecification on the remaining nuisance parameters. In this\nwork, we show how non-linear summary statistics can be constructed by\nminimising inference-motivated losses via stochastic gradient descent such they\nprovided the smallest uncertainty for the parameters of interest. As a use\ncase, the problem of confidence interval estimation for the mixture coefficient\nin a multi-dimensional two-component mixture model (i.e. signal vs background)\nis considered, where the proposed technique clearly outperforms summary\nstatistics based on probabilistic classification, which are a commonly used\nalternative but do not account for the presence of nuisance parameters.","url_abs":"http://arxiv.org/abs/1806.04743v2","url_pdf":"http://arxiv.org/pdf/1806.04743v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"inferno-inference-aware-neural-optimisation","repo_url":"https://github.com/pablodecm/paper-inferno","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.04743","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04743"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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