{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:O4ZDHHMY7Q3GU3NWKHJQLHQFQH","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":"bac3400ad26f35ff9093e094cc5045fc3a74f3c21159190dfbbd4c17be365dd9","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-15T08:27:50Z","title_canon_sha256":"06799eb6e95082c7ad61a0c857c16be432471806646fbece0c5b25cc8c8a2e43"},"schema_version":"1.0","source":{"id":"2306.08942","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.08942","created_at":"2026-07-05T06:20:58Z"},{"alias_kind":"arxiv_version","alias_value":"2306.08942v1","created_at":"2026-07-05T06:20:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.08942","created_at":"2026-07-05T06:20:58Z"},{"alias_kind":"pith_short_12","alias_value":"O4ZDHHMY7Q3G","created_at":"2026-07-05T06:20:58Z"},{"alias_kind":"pith_short_16","alias_value":"O4ZDHHMY7Q3GU3NW","created_at":"2026-07-05T06:20:58Z"},{"alias_kind":"pith_short_8","alias_value":"O4ZDHHMY","created_at":"2026-07-05T06:20:58Z"}],"graph_snapshots":[{"event_id":"sha256:a4ba5d0e36a6c7631d2de81928acf20558692e91b8010f25bd3fbf065eac70d4","target":"graph","created_at":"2026-07-05T06:20:58Z","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/2306.08942/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Representation learning based on multi-task pretraining has become a powerful approach in many domains. In particular, task-aware representation learning aims to learn an optimal representation for a specific target task by sampling data from a set of source tasks, while task-agnostic representation learning seeks to learn a universal representation for a class of tasks. In this paper, we propose a general and versatile algorithmic and theoretic framework for \\textit{active representation learning}, where the learner optimally chooses which source tasks to sample from. This framework, along wi","authors_text":"Guanya Shi, Kevin Jamieson, Simon S. Du, Yifang Chen, Yingbing Huang","cross_cats":["cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-15T08:27:50Z","title":"Active Representation Learning for General Task Space with Applications in Robotics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.08942","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":"sha256:4d7793935b21dd196e7d2d71b64847e77c8bf7f5cd08b7bf4e4a7f8f7ef510c4","target":"record","created_at":"2026-07-05T06:20:58Z","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":"bac3400ad26f35ff9093e094cc5045fc3a74f3c21159190dfbbd4c17be365dd9","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-15T08:27:50Z","title_canon_sha256":"06799eb6e95082c7ad61a0c857c16be432471806646fbece0c5b25cc8c8a2e43"},"schema_version":"1.0","source":{"id":"2306.08942","kind":"arxiv","version":1}},"canonical_sha256":"7732339d98fc366a6db651d3059e0581fa77eadc08ad82a923a702ac46d345fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7732339d98fc366a6db651d3059e0581fa77eadc08ad82a923a702ac46d345fb","first_computed_at":"2026-07-05T06:20:58.192601Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:20:58.192601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+RD+Axf9KZX8hL4c0nOO+mStvBNDtbBNi1A+Oi4qrq2BMeG4piDMxNv+Hwx3ymGgQPjzE6xgTQ9KnfxGLY77Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:20:58.193021Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.08942","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d7793935b21dd196e7d2d71b64847e77c8bf7f5cd08b7bf4e4a7f8f7ef510c4","sha256:a4ba5d0e36a6c7631d2de81928acf20558692e91b8010f25bd3fbf065eac70d4"],"state_sha256":"13c92804530e459d8181ff9e7fada955e82eacf0623682f27664cc7e68ac076f"}