{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ON7MVGR7T3EBYCBTIS7JCFH6SI","short_pith_number":"pith:ON7MVGR7","canonical_record":{"source":{"id":"2208.05287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T11:36:00Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"e94f208c41c93889d567cf8bfe2c4c7e1bb577c2c4254cd2a4dea56409c7c80f","abstract_canon_sha256":"8355e14f7c9eccbb3de21c9173efc3726162cb128fdcbc8ed0b48156b5bce672"},"schema_version":"1.0"},"canonical_sha256":"737eca9a3f9ec81c083344be9114fe921824908657b82b3b7365b0b2d05ce0a4","source":{"kind":"arxiv","id":"2208.05287","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.05287","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"arxiv_version","alias_value":"2208.05287v1","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.05287","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"pith_short_12","alias_value":"ON7MVGR7T3EB","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"pith_short_16","alias_value":"ON7MVGR7T3EBYCBT","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"pith_short_8","alias_value":"ON7MVGR7","created_at":"2026-07-05T04:47:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ON7MVGR7T3EBYCBTIS7JCFH6SI","target":"record","payload":{"canonical_record":{"source":{"id":"2208.05287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T11:36:00Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"e94f208c41c93889d567cf8bfe2c4c7e1bb577c2c4254cd2a4dea56409c7c80f","abstract_canon_sha256":"8355e14f7c9eccbb3de21c9173efc3726162cb128fdcbc8ed0b48156b5bce672"},"schema_version":"1.0"},"canonical_sha256":"737eca9a3f9ec81c083344be9114fe921824908657b82b3b7365b0b2d05ce0a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:47:34.336248Z","signature_b64":"d86keJ/qLU57UhRRC9vTTdnlzi/6B8nCYdEkpVDcsiBbVaWNPYR+MC4AKfxsPUKv6mJsfdzNfzLBlvCRh1ZVBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"737eca9a3f9ec81c083344be9114fe921824908657b82b3b7365b0b2d05ce0a4","last_reissued_at":"2026-07-05T04:47:34.335873Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:47:34.335873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.05287","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:47:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oGMpQXHQ/0Bd3+kreCTLt/Ld/oNZdiiANGdJobNFomsMOiRJkDOld4xiy6gijNlDeIncnOPO2nIgJcJSwhxrCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:01:03.397139Z"},"content_sha256":"6dad62825648084c9f42ef56c530c886da6cb0ab9890b279c87599d37938a6a0","schema_version":"1.0","event_id":"sha256:6dad62825648084c9f42ef56c530c886da6cb0ab9890b279c87599d37938a6a0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ON7MVGR7T3EBYCBTIS7JCFH6SI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Learning Rates for Faster Stochastic Gradient Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Konstantin Mishchenko, Peter Richt\\'arik, Samuel Horv\\'ath","submitted_at":"2022-08-10T11:36:00Z","abstract_excerpt":"In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods. Our first method (StoPS) is based on the classical Polyak step size (Polyak, 1987) and is an extension of the recent development of this method for the stochastic optimization-SPS (Loizou et al., 2021), and our second method, denoted GraDS, rescales step size by \"diversity of stochastic gradients\". We provide a theoretical analysis of these methods for strongly convex smooth functions and show they enjoy deterministic-like rates despite stochastic gradients. Furthermore, we demonstrate "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.05287","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.05287/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:47:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8koE6U48VtgAYVIEWj+VlGNeyM52n+4jR4L2XqjOdiL4ZFCdGZ1e8g1je1rck+dey9ue4qSMeQjWXzgunznfAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:01:03.398057Z"},"content_sha256":"a5b3a464339a7b6db3b954d54226b66f69696395911dfa77b176fcf6df621f6c","schema_version":"1.0","event_id":"sha256:a5b3a464339a7b6db3b954d54226b66f69696395911dfa77b176fcf6df621f6c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ON7MVGR7T3EBYCBTIS7JCFH6SI/bundle.json","state_url":"https://pith.science/pith/ON7MVGR7T3EBYCBTIS7JCFH6SI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ON7MVGR7T3EBYCBTIS7JCFH6SI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T20:01:03Z","links":{"resolver":"https://pith.science/pith/ON7MVGR7T3EBYCBTIS7JCFH6SI","bundle":"https://pith.science/pith/ON7MVGR7T3EBYCBTIS7JCFH6SI/bundle.json","state":"https://pith.science/pith/ON7MVGR7T3EBYCBTIS7JCFH6SI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ON7MVGR7T3EBYCBTIS7JCFH6SI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ON7MVGR7T3EBYCBTIS7JCFH6SI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8355e14f7c9eccbb3de21c9173efc3726162cb128fdcbc8ed0b48156b5bce672","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T11:36:00Z","title_canon_sha256":"e94f208c41c93889d567cf8bfe2c4c7e1bb577c2c4254cd2a4dea56409c7c80f"},"schema_version":"1.0","source":{"id":"2208.05287","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.05287","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"arxiv_version","alias_value":"2208.05287v1","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.05287","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"pith_short_12","alias_value":"ON7MVGR7T3EB","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"pith_short_16","alias_value":"ON7MVGR7T3EBYCBT","created_at":"2026-07-05T04:47:34Z"},{"alias_kind":"pith_short_8","alias_value":"ON7MVGR7","created_at":"2026-07-05T04:47:34Z"}],"graph_snapshots":[{"event_id":"sha256:a5b3a464339a7b6db3b954d54226b66f69696395911dfa77b176fcf6df621f6c","target":"graph","created_at":"2026-07-05T04:47:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2208.05287/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods. Our first method (StoPS) is based on the classical Polyak step size (Polyak, 1987) and is an extension of the recent development of this method for the stochastic optimization-SPS (Loizou et al., 2021), and our second method, denoted GraDS, rescales step size by \"diversity of stochastic gradients\". We provide a theoretical analysis of these methods for strongly convex smooth functions and show they enjoy deterministic-like rates despite stochastic gradients. Furthermore, we demonstrate ","authors_text":"Konstantin Mishchenko, Peter Richt\\'arik, Samuel Horv\\'ath","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T11:36:00Z","title":"Adaptive Learning Rates for Faster Stochastic Gradient Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.05287","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6dad62825648084c9f42ef56c530c886da6cb0ab9890b279c87599d37938a6a0","target":"record","created_at":"2026-07-05T04:47:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8355e14f7c9eccbb3de21c9173efc3726162cb128fdcbc8ed0b48156b5bce672","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-10T11:36:00Z","title_canon_sha256":"e94f208c41c93889d567cf8bfe2c4c7e1bb577c2c4254cd2a4dea56409c7c80f"},"schema_version":"1.0","source":{"id":"2208.05287","kind":"arxiv","version":1}},"canonical_sha256":"737eca9a3f9ec81c083344be9114fe921824908657b82b3b7365b0b2d05ce0a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"737eca9a3f9ec81c083344be9114fe921824908657b82b3b7365b0b2d05ce0a4","first_computed_at":"2026-07-05T04:47:34.335873Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:47:34.335873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d86keJ/qLU57UhRRC9vTTdnlzi/6B8nCYdEkpVDcsiBbVaWNPYR+MC4AKfxsPUKv6mJsfdzNfzLBlvCRh1ZVBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:47:34.336248Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.05287","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6dad62825648084c9f42ef56c530c886da6cb0ab9890b279c87599d37938a6a0","sha256:a5b3a464339a7b6db3b954d54226b66f69696395911dfa77b176fcf6df621f6c"],"state_sha256":"505c4ebf2ffb44f748d4ffcc3fecdb9264a1940a2623c7f9e7046c302ddf17fb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OIO1rn38SvBAT732n+NYlPBvX4YlA76/JfHv3MEcMHmi1ABVIEKXc2yrFqwscfywmWnbMXKxc+YViKNIeoqwDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T20:01:03.403798Z","bundle_sha256":"ee28e6dc0213e4816d53ef5ec11ffc6d4134634fa3e0fcea604418f7ace2f762"}}