{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:7ZXLYHEIJHZ2OWZLJRFOZ3SRX2","short_pith_number":"pith:7ZXLYHEI","canonical_record":{"source":{"id":"1702.04121","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-14T09:06:07Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b5949e22eca644d4c19ab9f0cd1684b5050d38312f07e521fcc1d40603737167","abstract_canon_sha256":"d8fdeeeba8be295d806c499d7631b64780e7a539ffa3a094481602f8a13eba20"},"schema_version":"1.0"},"canonical_sha256":"fe6ebc1c8849f3a75b2b4c4aecee51be96e929bd7a951e717af7fbf2cae150d9","source":{"kind":"arxiv","id":"1702.04121","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.04121","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"arxiv_version","alias_value":"1702.04121v1","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.04121","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"pith_short_12","alias_value":"7ZXLYHEIJHZ2","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7ZXLYHEIJHZ2OWZL","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7ZXLYHEI","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:7ZXLYHEIJHZ2OWZLJRFOZ3SRX2","target":"record","payload":{"canonical_record":{"source":{"id":"1702.04121","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-14T09:06:07Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b5949e22eca644d4c19ab9f0cd1684b5050d38312f07e521fcc1d40603737167","abstract_canon_sha256":"d8fdeeeba8be295d806c499d7631b64780e7a539ffa3a094481602f8a13eba20"},"schema_version":"1.0"},"canonical_sha256":"fe6ebc1c8849f3a75b2b4c4aecee51be96e929bd7a951e717af7fbf2cae150d9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:49.004095Z","signature_b64":"2zS778o9dChx8ZpIwoNQ5QE8PfcJEghnSP9eyKSd7TYDiGdV169DHW018jJPErdj0ScRGomcFr/JUH70IdHCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe6ebc1c8849f3a75b2b4c4aecee51be96e929bd7a951e717af7fbf2cae150d9","last_reissued_at":"2026-05-18T00:50:49.003498Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:49.003498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.04121","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":"2026-05-18T00:50:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0LMQAZNrgOn/4kGFcmRIsR801HVw16CBDgRQmFhJfOriz+iu/Sv6ZwvdqH2IYwyLqB49CSbjffzZmqBIAfyJBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:27:57.528509Z"},"content_sha256":"c4c047404b682df6b3ed6311aaa39f97724cc35a6e63b1639d2db5e64bc02b7d","schema_version":"1.0","event_id":"sha256:c4c047404b682df6b3ed6311aaa39f97724cc35a6e63b1639d2db5e64bc02b7d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:7ZXLYHEIJHZ2OWZLJRFOZ3SRX2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Practical Learning of Predictive State Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ahmed Hefny, Carlton Downey, Geoffrey Gordon","submitted_at":"2017-02-14T09:06:07Z","abstract_excerpt":"Over the past decade there has been considerable interest in spectral algorithms for learning Predictive State Representations (PSRs). Spectral algorithms have appealing theoretical guarantees; however, the resulting models do not always perform well on inference tasks in practice. One reason for this behavior is the mismatch between the intended task (accurate filtering or prediction) and the loss function being optimized by the algorithm (estimation error in model parameters).\n  A natural idea is to improve performance by refining PSRs using an algorithm such as EM. Unfortunately it is not o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.04121","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":"2026-05-18T00:50:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CS9KoWr1PbL2oVcm+BttsxxXqR573yFHncNDk1rH5ZY/cEkPzRxJ0l1ZD9PZHKShZgpXQiQ4a0TTHjBk8+T3Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:27:57.528863Z"},"content_sha256":"2c747256385338b5bcb2b2b181631f949e6258c450592f7e886b858d494c4808","schema_version":"1.0","event_id":"sha256:2c747256385338b5bcb2b2b181631f949e6258c450592f7e886b858d494c4808"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7ZXLYHEIJHZ2OWZLJRFOZ3SRX2/bundle.json","state_url":"https://pith.science/pith/7ZXLYHEIJHZ2OWZLJRFOZ3SRX2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7ZXLYHEIJHZ2OWZLJRFOZ3SRX2/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":"2026-05-26T02:27:57Z","links":{"resolver":"https://pith.science/pith/7ZXLYHEIJHZ2OWZLJRFOZ3SRX2","bundle":"https://pith.science/pith/7ZXLYHEIJHZ2OWZLJRFOZ3SRX2/bundle.json","state":"https://pith.science/pith/7ZXLYHEIJHZ2OWZLJRFOZ3SRX2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7ZXLYHEIJHZ2OWZLJRFOZ3SRX2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:7ZXLYHEIJHZ2OWZLJRFOZ3SRX2","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":"d8fdeeeba8be295d806c499d7631b64780e7a539ffa3a094481602f8a13eba20","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-14T09:06:07Z","title_canon_sha256":"b5949e22eca644d4c19ab9f0cd1684b5050d38312f07e521fcc1d40603737167"},"schema_version":"1.0","source":{"id":"1702.04121","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.04121","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"arxiv_version","alias_value":"1702.04121v1","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.04121","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"pith_short_12","alias_value":"7ZXLYHEIJHZ2","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7ZXLYHEIJHZ2OWZL","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7ZXLYHEI","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:2c747256385338b5bcb2b2b181631f949e6258c450592f7e886b858d494c4808","target":"graph","created_at":"2026-05-18T00:50:49Z","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":"Over the past decade there has been considerable interest in spectral algorithms for learning Predictive State Representations (PSRs). Spectral algorithms have appealing theoretical guarantees; however, the resulting models do not always perform well on inference tasks in practice. One reason for this behavior is the mismatch between the intended task (accurate filtering or prediction) and the loss function being optimized by the algorithm (estimation error in model parameters).\n  A natural idea is to improve performance by refining PSRs using an algorithm such as EM. Unfortunately it is not o","authors_text":"Ahmed Hefny, Carlton Downey, Geoffrey Gordon","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-14T09:06:07Z","title":"Practical Learning of Predictive State Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.04121","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:c4c047404b682df6b3ed6311aaa39f97724cc35a6e63b1639d2db5e64bc02b7d","target":"record","created_at":"2026-05-18T00:50:49Z","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":"d8fdeeeba8be295d806c499d7631b64780e7a539ffa3a094481602f8a13eba20","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-14T09:06:07Z","title_canon_sha256":"b5949e22eca644d4c19ab9f0cd1684b5050d38312f07e521fcc1d40603737167"},"schema_version":"1.0","source":{"id":"1702.04121","kind":"arxiv","version":1}},"canonical_sha256":"fe6ebc1c8849f3a75b2b4c4aecee51be96e929bd7a951e717af7fbf2cae150d9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe6ebc1c8849f3a75b2b4c4aecee51be96e929bd7a951e717af7fbf2cae150d9","first_computed_at":"2026-05-18T00:50:49.003498Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:50:49.003498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2zS778o9dChx8ZpIwoNQ5QE8PfcJEghnSP9eyKSd7TYDiGdV169DHW018jJPErdj0ScRGomcFr/JUH70IdHCBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:50:49.004095Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.04121","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c4c047404b682df6b3ed6311aaa39f97724cc35a6e63b1639d2db5e64bc02b7d","sha256:2c747256385338b5bcb2b2b181631f949e6258c450592f7e886b858d494c4808"],"state_sha256":"88cfae0bba1eeb1e527cd85ba009c1394386bd0158a43d4c94cdda1c80384e84"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oqsR8zWHaHrYq4huy+aCBLyA0ZPo9Vl14yMDSSbfrJHpMZtc60TyMoK6CtQhGuYjTO2mrXzWJZHRgjpHJRiCBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:27:57.530975Z","bundle_sha256":"240c836e3f57f4231d4a5aef430fe6705e890b97c7264fa8582a01930ddf7960"}}