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Although variance reduction techniques such as SGD with momentum, STORM, and PAGE have demonstrated improved convergence properties in non-convex optimization, their implications for sampling from non-log-concave distributions remain largely unexplored. In this work, we develop the first unified analysis of these estimators for sampling from non-log-concave distributions. We establish improved non-asymptotic convergence rates in $\\varepsilon$-relative Fisher information and, under a Poincar\\'e inequality assumption, in squared total variation distance, and further prove weak convergence to the tar","title":"Variance Reduction for Non-Log-Concave Sampling with Applications to Inverse Problems","url":"https://arxiv.org/abs/2606.16257","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.16257v1 Announce Type: cross \nAbstract: Sampling from high-dimensional, non-log-concave distributions with unnormalized densities is a fundamental challenge in machine learning, particularly when the exact gradient of the potential is unavailable and must be approximated via stochastic gradients that exhibit high variance under a fixed budget of gradient computations per iteration. Although variance reduction techniques such as SGD with momentum, STORM, and PAGE have demonstrated improved convergence properties in non-convex optimization, their implications for sampling from non-log-concave distributions remain largely unexplored. In this work, we develop the first unified analysis of these estimators for sampling from non-log-concave distributions. We establish improved non-asymptotic convergence rates in $\\varepsilon$-relative Fisher information and, under a Poincar\\'e inequality assumption, in squared total variation distance, and further prove weak convergence to the tar","title":"Variance Reduction for Non-Log-Concave Sampling with Applications to Inverse Problems","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-06-16T04:43:43Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2606.16257"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:1c789c77c18ae2f514196b787bf4921080e05dbe972cdfbed210a8dfb1c1477e79fea2b24115d2e2d6485eede3780705c00d0ee61916a37c1a4cce74d5a1d90c","signer":"crovia.substrate","subject":{"observed_at":"2026-06-16T04:43:43Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2606.16257"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"2a8994fbe9085ab82be039e0b0d40cbf3e72e21b0a33b1df2de78cb2fdde30d7","leaf_index":230530,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"8d220370ac0b0dea9a7e67e34985005accb0bd8c2c351e4f7b9ca98330cc929c","side":"right"},{"sibling":"c805c8840b58d020662aad9dbe3e5d3b07e7d8957b7fe82f0e867f05b5d63727","side":"left"},{"sibling":"507e4cf53163926091250b69498fc38bdd262ba01b032048176a28257eab71f7","side":"right"},{"sibling":"532b59dc8ac5e5ead21c688444f8d45a0119c1d835ea4be08dfb35dff80902f7","side":"right"},{"sibling":"f23ea8151f42419e55d497f9020fb2b8777a1061e89f8c961e79b4cc28317d75","side":"right"},{"sibling":"9e48de8b9accda154b84b6e9f28dc6e3474d9a3cce1c2ff89e33ab238f2d3501","side":"right"},{"sibling":"b49ca064746e7e6295be61ea90a8ad7b8ae51e000a19ff00ba9ac201b696c28b","side":"right"},{"sibling":"f5958932b707fbb8d9de8cb158fe61200709928e9e5d860d183ba06f040858b6","side":"left"},{"sibling":"63d6b9d8af7ae8348285d3493af29f64992ecec42f5f1cae99c8604cd1703487","side":"right"},{"sibling":"0c5669692381d605223c74b8d30f70cd308e77e33d5e40ea84bb7b4f84f2d4d9","side":"right"},{"sibling":"d5b9f8b1a2c9f6a46e17982dfbe6ce1f3b5fa4e730220397f2253d114dcc8486","side":"left"},{"sibling":"d10d772a4984cae00e65ab24af21d1d260e475bbaae3f17871859eb705bd3999","side":"right"},{"sibling":"e79159853f2f35ddae8e3247d515e433c534277b287d65bbd77ae989aa4992fa","side":"right"},{"sibling":"3054319f1840cce0eaaf0bc4b1ae38e5bf8b6210927d924a750775cc7d77cca6","side":"right"},{"sibling":"0fd8b5059f279c4a4a6688de2472fdbc25543fee183df19b21dacca880354cff","side":"right"},{"sibling":"a04392fb9f2a3a620840e3b3fecd93d12e6d224e481c162389d8ae64e7194599","side":"left"},{"sibling":"c300cf0154c136afc09b1702a0be98f4ba5b6dc5cf57e8cc714ec1eaf4196eff","side":"left"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":232015,"merkle_root":"62bfb7809bb55667ad7eeebdb48267b9b2c1ee89bb808ea1e6d3a814a15aa402","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260617T133701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-06-17T13:39:59Z","sig_algorithm":"ed25519","signature":"930a3563c643cc7518d048b12a1f5392a96a5edce49533ba0a56f11b6cc69319bb1c800aacc46b52a6941c4d05dae255a45cac757d6093c971b96852c24f140e","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_16f38ddc5444a79d84067032605e78d4062ca9278d241d747c94c30ba56ed3c0"}}