{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:GGW5B6BNDTOXCCJX6PNCNE5SA5","short_pith_number":"pith:GGW5B6BN","canonical_record":{"source":{"id":"1706.10207","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-30T14:09:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"06c8900e72b365b6b415fcf47b492cf79e4c6799c9caeb67dda59bc15e69a9fd","abstract_canon_sha256":"c4e9ad89add5451ce3d2ff38b58e4c257b9b4c24686dc38b28cd44c0a0f09d53"},"schema_version":"1.0"},"canonical_sha256":"31add0f82d1cdd710937f3da2693b2076de811aa593108aa801f04722c282533","source":{"kind":"arxiv","id":"1706.10207","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.10207","created_at":"2026-05-18T00:41:09Z"},{"alias_kind":"arxiv_version","alias_value":"1706.10207v1","created_at":"2026-05-18T00:41:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.10207","created_at":"2026-05-18T00:41:09Z"},{"alias_kind":"pith_short_12","alias_value":"GGW5B6BNDTOX","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GGW5B6BNDTOXCCJX","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GGW5B6BN","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:GGW5B6BNDTOXCCJX6PNCNE5SA5","target":"record","payload":{"canonical_record":{"source":{"id":"1706.10207","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-30T14:09:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"06c8900e72b365b6b415fcf47b492cf79e4c6799c9caeb67dda59bc15e69a9fd","abstract_canon_sha256":"c4e9ad89add5451ce3d2ff38b58e4c257b9b4c24686dc38b28cd44c0a0f09d53"},"schema_version":"1.0"},"canonical_sha256":"31add0f82d1cdd710937f3da2693b2076de811aa593108aa801f04722c282533","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:09.140548Z","signature_b64":"VkJiuR6Un9wNLlAhymsCObHCiVtb/J/sm5kb2dNpslZ77tgyGBYjD/an/1bmU8ytekEDT/S6QmZasKYOjQAJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31add0f82d1cdd710937f3da2693b2076de811aa593108aa801f04722c282533","last_reissued_at":"2026-05-18T00:41:09.140072Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:09.140072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.10207","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":"2026-05-18T00:41:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F9xMSE3GnMWS/yaCP/WJb+p1XZrQrvP+76yBtMj6pLT7TP85k5hBtEeDKzvwVDbnExK92iCQpLqiZj/Ru40rAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T15:10:44.414974Z"},"content_sha256":"8c25abd677ee4a3c5f42420c210fdae8398cad9410ee7069763445244aa48b96","schema_version":"1.0","event_id":"sha256:8c25abd677ee4a3c5f42420c210fdae8398cad9410ee7069763445244aa48b96"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:GGW5B6BNDTOXCCJX6PNCNE5SA5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Frank E. Curtis, Katya Scheinberg","submitted_at":"2017-06-30T14:09:44Z","abstract_excerpt":"The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning. It is written with an INFORMS audience in mind, specifically those readers who are familiar with the basics of optimization algorithms, but less familiar with machine learning. We begin by deriving a formulation of a supervised learning problem and show how it leads to various optimization problems, depending on the context and underlying assumptions. We then discuss some of the distinctive features of these optimizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.10207","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":"2026-05-18T00:41:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qVFa5Bv0t/ZpFyQHJ5uifscEy4NIU/ax8KgiV15jaeeQaRIGQkZdAfaydK6QFDjv3k561BmMlX0qgWAd+hfLDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T15:10:44.415350Z"},"content_sha256":"2bbf87e7de383ab96ab6923fb1872952467d593b715862ee6a6994580ac2e515","schema_version":"1.0","event_id":"sha256:2bbf87e7de383ab96ab6923fb1872952467d593b715862ee6a6994580ac2e515"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GGW5B6BNDTOXCCJX6PNCNE5SA5/bundle.json","state_url":"https://pith.science/pith/GGW5B6BNDTOXCCJX6PNCNE5SA5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GGW5B6BNDTOXCCJX6PNCNE5SA5/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":"2026-06-03T15:10:44Z","links":{"resolver":"https://pith.science/pith/GGW5B6BNDTOXCCJX6PNCNE5SA5","bundle":"https://pith.science/pith/GGW5B6BNDTOXCCJX6PNCNE5SA5/bundle.json","state":"https://pith.science/pith/GGW5B6BNDTOXCCJX6PNCNE5SA5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GGW5B6BNDTOXCCJX6PNCNE5SA5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GGW5B6BNDTOXCCJX6PNCNE5SA5","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":"c4e9ad89add5451ce3d2ff38b58e4c257b9b4c24686dc38b28cd44c0a0f09d53","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-30T14:09:44Z","title_canon_sha256":"06c8900e72b365b6b415fcf47b492cf79e4c6799c9caeb67dda59bc15e69a9fd"},"schema_version":"1.0","source":{"id":"1706.10207","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.10207","created_at":"2026-05-18T00:41:09Z"},{"alias_kind":"arxiv_version","alias_value":"1706.10207v1","created_at":"2026-05-18T00:41:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.10207","created_at":"2026-05-18T00:41:09Z"},{"alias_kind":"pith_short_12","alias_value":"GGW5B6BNDTOX","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GGW5B6BNDTOXCCJX","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GGW5B6BN","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:2bbf87e7de383ab96ab6923fb1872952467d593b715862ee6a6994580ac2e515","target":"graph","created_at":"2026-05-18T00:41:09Z","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":"The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning. It is written with an INFORMS audience in mind, specifically those readers who are familiar with the basics of optimization algorithms, but less familiar with machine learning. We begin by deriving a formulation of a supervised learning problem and show how it leads to various optimization problems, depending on the context and underlying assumptions. We then discuss some of the distinctive features of these optimizati","authors_text":"Frank E. Curtis, Katya Scheinberg","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-30T14:09:44Z","title":"Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.10207","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":"sha256:8c25abd677ee4a3c5f42420c210fdae8398cad9410ee7069763445244aa48b96","target":"record","created_at":"2026-05-18T00:41:09Z","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":"c4e9ad89add5451ce3d2ff38b58e4c257b9b4c24686dc38b28cd44c0a0f09d53","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-30T14:09:44Z","title_canon_sha256":"06c8900e72b365b6b415fcf47b492cf79e4c6799c9caeb67dda59bc15e69a9fd"},"schema_version":"1.0","source":{"id":"1706.10207","kind":"arxiv","version":1}},"canonical_sha256":"31add0f82d1cdd710937f3da2693b2076de811aa593108aa801f04722c282533","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31add0f82d1cdd710937f3da2693b2076de811aa593108aa801f04722c282533","first_computed_at":"2026-05-18T00:41:09.140072Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:09.140072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VkJiuR6Un9wNLlAhymsCObHCiVtb/J/sm5kb2dNpslZ77tgyGBYjD/an/1bmU8ytekEDT/S6QmZasKYOjQAJDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:09.140548Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.10207","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c25abd677ee4a3c5f42420c210fdae8398cad9410ee7069763445244aa48b96","sha256:2bbf87e7de383ab96ab6923fb1872952467d593b715862ee6a6994580ac2e515"],"state_sha256":"7a61f08554da1ce42fffa7ce7b3b19c0020d1c598caa51ce5e6643c3538e84ba"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W/tB1dUthjAwk9ao+ocQn0WfAhxibB7pzzQgD9Ek+HUE9dwGFKR8+8629iZl41ob35xPnDcpma29BOjUMPI+Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T15:10:44.417429Z","bundle_sha256":"3e91a7be4f3de88aa377544c7185de5f8243b1eeb7986bc947444dfad8b26844"}}