{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6FEGSNO5OWCQEN3ZOVWWY6OJFY","short_pith_number":"pith:6FEGSNO5","canonical_record":{"source":{"id":"1809.06517","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-18T03:27:49Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"ee9bac48ad87708fb7406445078d00fadb13d7cd29f9070076dadc539aa3e1d8","abstract_canon_sha256":"e435d3d256b3b3699baa09acbadc0ec28bf1d03a0d1b4a357c4b7a31f9183af7"},"schema_version":"1.0"},"canonical_sha256":"f1486935dd7585023779756d6c79c92e0f1dd8f3fa1f970bbfa284491812c83d","source":{"kind":"arxiv","id":"1809.06517","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06517","created_at":"2026-05-18T00:05:26Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06517v1","created_at":"2026-05-18T00:05:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06517","created_at":"2026-05-18T00:05:26Z"},{"alias_kind":"pith_short_12","alias_value":"6FEGSNO5OWCQ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"6FEGSNO5OWCQEN3Z","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"6FEGSNO5","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6FEGSNO5OWCQEN3ZOVWWY6OJFY","target":"record","payload":{"canonical_record":{"source":{"id":"1809.06517","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-18T03:27:49Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"ee9bac48ad87708fb7406445078d00fadb13d7cd29f9070076dadc539aa3e1d8","abstract_canon_sha256":"e435d3d256b3b3699baa09acbadc0ec28bf1d03a0d1b4a357c4b7a31f9183af7"},"schema_version":"1.0"},"canonical_sha256":"f1486935dd7585023779756d6c79c92e0f1dd8f3fa1f970bbfa284491812c83d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:26.926287Z","signature_b64":"hElcJHhcRO48KeMLpj1vyNMQLxQ9wRi3a6LyYTXEzxuXZ2opiIIllCMoOwtqxaWlmNbZElkVYCfGCR2SWmkHBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1486935dd7585023779756d6c79c92e0f1dd8f3fa1f970bbfa284491812c83d","last_reissued_at":"2026-05-18T00:05:26.925503Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:26.925503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.06517","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":"2026-05-18T00:05:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RIh/WhVQxZYvSS2DAytm7kd+tBKAprX6jfCAJiuEeeOsp7PJJC6idKYSlwrujtBq6ebbZZO8UJLiLSh+O9KpCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T16:02:03.752548Z"},"content_sha256":"cfe91afb76790dc277628bd1dc0c6707190b9a6e9ffb4fe7be93395ecf4c12d8","schema_version":"1.0","event_id":"sha256:cfe91afb76790dc277628bd1dc0c6707190b9a6e9ffb4fe7be93395ecf4c12d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6FEGSNO5OWCQEN3ZOVWWY6OJFY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hernan Aguirre, Kouhei Nishida, Shinichi Shirakawa, Shota Saito, Youhei Akimoto","submitted_at":"2018-09-18T03:27:49Z","abstract_excerpt":"Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of system parameters, such as hyper parameter tuning of machine learning based systems when being installed for a specific task. However, automation is often jeopardized by the need of strategy parameter tuning for BBDO algorithms. An expert with the domain knowledge must undergo time-consuming strategy parameter tuning. This paper proposes a parameterless BBDO algorithm based on information geometric optimization, a r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06517","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":""},"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":"2026-05-18T00:05:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bf4G46Wv8+YxGpM4gRZ0DQI5oD4iNVoAWy7K+dPoSQ5XeNgUqp67eZvc2PBeJ8O8eKf98QKwQWQjvqaBkR2dDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T16:02:03.753269Z"},"content_sha256":"110a6330d1d80a190297b2d8cae948525c72a1e4b1998e18488708a4831cefd2","schema_version":"1.0","event_id":"sha256:110a6330d1d80a190297b2d8cae948525c72a1e4b1998e18488708a4831cefd2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6FEGSNO5OWCQEN3ZOVWWY6OJFY/bundle.json","state_url":"https://pith.science/pith/6FEGSNO5OWCQEN3ZOVWWY6OJFY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6FEGSNO5OWCQEN3ZOVWWY6OJFY/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":"2026-06-11T16:02:03Z","links":{"resolver":"https://pith.science/pith/6FEGSNO5OWCQEN3ZOVWWY6OJFY","bundle":"https://pith.science/pith/6FEGSNO5OWCQEN3ZOVWWY6OJFY/bundle.json","state":"https://pith.science/pith/6FEGSNO5OWCQEN3ZOVWWY6OJFY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6FEGSNO5OWCQEN3ZOVWWY6OJFY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6FEGSNO5OWCQEN3ZOVWWY6OJFY","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":"e435d3d256b3b3699baa09acbadc0ec28bf1d03a0d1b4a357c4b7a31f9183af7","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-18T03:27:49Z","title_canon_sha256":"ee9bac48ad87708fb7406445078d00fadb13d7cd29f9070076dadc539aa3e1d8"},"schema_version":"1.0","source":{"id":"1809.06517","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06517","created_at":"2026-05-18T00:05:26Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06517v1","created_at":"2026-05-18T00:05:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06517","created_at":"2026-05-18T00:05:26Z"},{"alias_kind":"pith_short_12","alias_value":"6FEGSNO5OWCQ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"6FEGSNO5OWCQEN3Z","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"6FEGSNO5","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:110a6330d1d80a190297b2d8cae948525c72a1e4b1998e18488708a4831cefd2","target":"graph","created_at":"2026-05-18T00:05:26Z","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"},"paper":{"abstract_excerpt":"Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of system parameters, such as hyper parameter tuning of machine learning based systems when being installed for a specific task. However, automation is often jeopardized by the need of strategy parameter tuning for BBDO algorithms. An expert with the domain knowledge must undergo time-consuming strategy parameter tuning. This paper proposes a parameterless BBDO algorithm based on information geometric optimization, a r","authors_text":"Hernan Aguirre, Kouhei Nishida, Shinichi Shirakawa, Shota Saito, Youhei Akimoto","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-18T03:27:49Z","title":"Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06517","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:cfe91afb76790dc277628bd1dc0c6707190b9a6e9ffb4fe7be93395ecf4c12d8","target":"record","created_at":"2026-05-18T00:05:26Z","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":"e435d3d256b3b3699baa09acbadc0ec28bf1d03a0d1b4a357c4b7a31f9183af7","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-18T03:27:49Z","title_canon_sha256":"ee9bac48ad87708fb7406445078d00fadb13d7cd29f9070076dadc539aa3e1d8"},"schema_version":"1.0","source":{"id":"1809.06517","kind":"arxiv","version":1}},"canonical_sha256":"f1486935dd7585023779756d6c79c92e0f1dd8f3fa1f970bbfa284491812c83d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1486935dd7585023779756d6c79c92e0f1dd8f3fa1f970bbfa284491812c83d","first_computed_at":"2026-05-18T00:05:26.925503Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:26.925503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hElcJHhcRO48KeMLpj1vyNMQLxQ9wRi3a6LyYTXEzxuXZ2opiIIllCMoOwtqxaWlmNbZElkVYCfGCR2SWmkHBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:26.926287Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.06517","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cfe91afb76790dc277628bd1dc0c6707190b9a6e9ffb4fe7be93395ecf4c12d8","sha256:110a6330d1d80a190297b2d8cae948525c72a1e4b1998e18488708a4831cefd2"],"state_sha256":"2dc028463b0540898aebb541fdd558aace9517190107668baf93c47a73ea3efb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LBmviBcaRxa9znsFr/RYqMSMscYUP8YcJm3Ew56ka5HDW4Yoh6AHESg1hIp916q51sEcN2bcyi3PkvYHFWkRCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T16:02:03.757938Z","bundle_sha256":"20a367d8fe9cb301ad6130e5181d70b2053a219651872d8044033434a33eb28b"}}