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Under practitioner-friendly assumptions, we reduce this setting to linear bandit with stationary mean but heteroskedastic and non-stationary noise. We further study the case when the learner must ensure the mean reward of each decision must exceed that of a baseline strategy $\\boldsymbol{\\pi}_0$ at each decision step. We introduce Dri-MED, an algorithm inspired from the linear version of the MED strategy, and carefully adapted to handle the non-stationary heteroskedastic noise. We show that the instance-dependent regret scales as $\\tilde{\\mathcal O}\\left(\\frac{\\kappa}{\\tilde{\\Delta}}d^2(\\log(T)\\right)$, where $\\tilde{\\Delta}$ is the constraint-aware sub-optimality gap sub","title":"Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts","url":"https://arxiv.org/abs/2606.09802","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.09802v1 Announce Type: cross \nAbstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time. Under practitioner-friendly assumptions, we reduce this setting to linear bandit with stationary mean but heteroskedastic and non-stationary noise. We further study the case when the learner must ensure the mean reward of each decision must exceed that of a baseline strategy $\\boldsymbol{\\pi}_0$ at each decision step. We introduce Dri-MED, an algorithm inspired from the linear version of the MED strategy, and carefully adapted to handle the non-stationary heteroskedastic noise. We show that the instance-dependent regret scales as $\\tilde{\\mathcal O}\\left(\\frac{\\kappa}{\\tilde{\\Delta}}d^2(\\log(T)\\right)$, where $\\tilde{\\Delta}$ is the constraint-aware sub-optimality gap sub","title":"Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-06-09T04:43:45Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2606.09802"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:82e43562cdc739b06ef10fe8f3afa7277faa0d70b793c74b3fa4cdd38d90564d4e10942f3873558e5d59b91208cece78421fa4184af888b8565e53a6cd32f904","signer":"crovia.substrate","subject":{"observed_at":"2026-06-09T04:43:45Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2606.09802"},"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":"70971c080841b1809384f5e3efe6989d4487a41c6354f2f530a543d5ed36027b","leaf_index":224436,"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":"969542a4d6b113fe9d97607fd02d1f62f1431e5d0353fb7abd13a8e505d8e1a3","side":"right"},{"sibling":"1efc765a6f146bb3fa83913d9ac148982f6777d0f82efe338b6f9edb9abe48b8","side":"right"},{"sibling":"cf8400e16575de8af6db2cd7af427b556bd317fde99c90aaa132a55b9d8426b7","side":"left"},{"sibling":"a909202b2150a8c822c4c678880f9945606c6c1921e12fa89af2fb7cbdad81bd","side":"right"},{"sibling":"fc43c956481b908fb7739e5c17940bf0d8d2e8fe7ab509985e8f03e5d593e24b","side":"left"},{"sibling":"8033e13154cb38c92e03fb5fc12985d4eddf4f8e615951518fd7cf67cd43018b","side":"left"},{"sibling":"e608affc2056362c3a340b4c445f33ace512b2cfe2d91dd48dbc62ed1cd1d32e","side":"right"},{"sibling":"0dba1b4aa8c69a39c01dbd9d9884405788a8121b073c56815159112e9ccba4bd","side":"left"},{"sibling":"f6fb234a4e2f067b22329eec05b093a8b38f0411de9434d5a8eb55c2f70f1a2f","side":"right"},{"sibling":"b2df6a4bb3e928f0b447931cc688ae01d2415773a2b07cfed0b1cba689078aed","side":"right"},{"sibling":"b1c9ec856caa0fd46bb47b46f18c59ebcd295d774ca17adb3b46f05d394a6a5d","side":"left"},{"sibling":"24fdc29d461691aedb6fa920758206b5bb43851f477ef7a04c34aaed84b8971b","side":"left"},{"sibling":"036922da4e1e2c46d948f070454bfad299b7406fb00735ea9d8bd1e687f5f445","side":"right"},{"sibling":"533d82482604463aa4a281b9d8b85917b383b7c5f924b2b494039524c55e8797","side":"left"},{"sibling":"b2590791b920ca2a4ed39de126d2c0b1a10d9e7e62f572f12425f214e767b6e1","side":"left"},{"sibling":"87c6b850dfec08ac35a693d9db3a3315250a68adb1cfab9b1015f212b63b15bd","side":"right"},{"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":224761,"merkle_root":"e9f7b49b652e869ab97ffba9c5a31356b2d0e3dc5d00bb28944adf737c46b1e7","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260609T103805Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-06-09T14:15:34Z","sig_algorithm":"ed25519","signature":"8ad8076fb12c8e486ae1d1559a9a7ba8e2ee996a9ad3d8ba7bcdbdbd88ab3a15bcb429707aca6d3e9d8b97e2ba755b3dcc77b1abb6601ccb829842719a6fb30d","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_3d6861e6d87857c6cd60d2e32ca04098e075e1c6ffd61682d64265b4590f9235"}}