{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:U4QXXN5VJDMA4BGVTHMXI57W4D","short_pith_number":"pith:U4QXXN5V","canonical_record":{"source":{"id":"1712.07102","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-12-19T18:40:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0403fc13315531392288cd4b8cf0fa682f3ebaa2dd7b476e0c01061c0fb74f57","abstract_canon_sha256":"6523eff1be80e08cc8d4a4d563fe6caeace36957b51c4f3f8b8620dd958af636"},"schema_version":"1.0"},"canonical_sha256":"a7217bb7b548d80e04d599d97477f6e0eb26716c6a32ecaf4f578975f95367a7","source":{"kind":"arxiv","id":"1712.07102","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07102","created_at":"2026-05-18T00:27:37Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07102v1","created_at":"2026-05-18T00:27:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07102","created_at":"2026-05-18T00:27:37Z"},{"alias_kind":"pith_short_12","alias_value":"U4QXXN5VJDMA","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"U4QXXN5VJDMA4BGV","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"U4QXXN5V","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:U4QXXN5VJDMA4BGVTHMXI57W4D","target":"record","payload":{"canonical_record":{"source":{"id":"1712.07102","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-12-19T18:40:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0403fc13315531392288cd4b8cf0fa682f3ebaa2dd7b476e0c01061c0fb74f57","abstract_canon_sha256":"6523eff1be80e08cc8d4a4d563fe6caeace36957b51c4f3f8b8620dd958af636"},"schema_version":"1.0"},"canonical_sha256":"a7217bb7b548d80e04d599d97477f6e0eb26716c6a32ecaf4f578975f95367a7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:37.168250Z","signature_b64":"pVzfH+MgObxHwHfpVvypLIB422pTCRCmJP4RgwB7quLFyD7lHmd4Ny1PIVJ0s8h12sY5QMKu/8y87RNj+90zCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7217bb7b548d80e04d599d97477f6e0eb26716c6a32ecaf4f578975f95367a7","last_reissued_at":"2026-05-18T00:27:37.167507Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:37.167507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.07102","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":"2026-05-18T00:27:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9QDDD6W/7ugOEwRtpWiqurKjpIBnkcuLy8/Ty/2sP9piieEikkbttN9CUMes3lXNiZqt4JYLJ2IlGESbySJ4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:39:00.602116Z"},"content_sha256":"02bc54130c954bbf46ad3012659fe6a26d04cc5d4b9083f02d9dc598d743f125","schema_version":"1.0","event_id":"sha256:02bc54130c954bbf46ad3012659fe6a26d04cc5d4b9083f02d9dc598d743f125"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:U4QXXN5VJDMA4BGVTHMXI57W4D","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Data-Dependent Random Features for Improved Generalization in Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ahmad Beirami, Shahin Shahrampour, Vahid Tarokh","submitted_at":"2017-12-19T18:40:36Z","abstract_excerpt":"The randomized-feature approach has been successfully employed in large-scale kernel approximation and supervised learning. The distribution from which the random features are drawn impacts the number of features required to efficiently perform a learning task. Recently, it has been shown that employing data-dependent randomization improves the performance in terms of the required number of random features. In this paper, we are concerned with the randomized-feature approach in supervised learning for good generalizability. We propose the Energy-based Exploration of Random Features (EERF) algo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07102","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":"2026-05-18T00:27:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A48/NdEimdIXPmuga1PiPRvCz0gJOT+/Uc9yHKLVdfIoapO+P7eIPyJh09htSehnytCeHiJbDSFeDLg2REZ8Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:39:00.602544Z"},"content_sha256":"7e2d2c2af31d6d4d888cbe1485536c355d7162f8fdcf053264a498c1e49ada78","schema_version":"1.0","event_id":"sha256:7e2d2c2af31d6d4d888cbe1485536c355d7162f8fdcf053264a498c1e49ada78"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U4QXXN5VJDMA4BGVTHMXI57W4D/bundle.json","state_url":"https://pith.science/pith/U4QXXN5VJDMA4BGVTHMXI57W4D/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U4QXXN5VJDMA4BGVTHMXI57W4D/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":"2026-05-26T11:39:00Z","links":{"resolver":"https://pith.science/pith/U4QXXN5VJDMA4BGVTHMXI57W4D","bundle":"https://pith.science/pith/U4QXXN5VJDMA4BGVTHMXI57W4D/bundle.json","state":"https://pith.science/pith/U4QXXN5VJDMA4BGVTHMXI57W4D/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U4QXXN5VJDMA4BGVTHMXI57W4D/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:U4QXXN5VJDMA4BGVTHMXI57W4D","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":"6523eff1be80e08cc8d4a4d563fe6caeace36957b51c4f3f8b8620dd958af636","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-12-19T18:40:36Z","title_canon_sha256":"0403fc13315531392288cd4b8cf0fa682f3ebaa2dd7b476e0c01061c0fb74f57"},"schema_version":"1.0","source":{"id":"1712.07102","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07102","created_at":"2026-05-18T00:27:37Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07102v1","created_at":"2026-05-18T00:27:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07102","created_at":"2026-05-18T00:27:37Z"},{"alias_kind":"pith_short_12","alias_value":"U4QXXN5VJDMA","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"U4QXXN5VJDMA4BGV","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"U4QXXN5V","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:7e2d2c2af31d6d4d888cbe1485536c355d7162f8fdcf053264a498c1e49ada78","target":"graph","created_at":"2026-05-18T00:27:37Z","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 randomized-feature approach has been successfully employed in large-scale kernel approximation and supervised learning. The distribution from which the random features are drawn impacts the number of features required to efficiently perform a learning task. Recently, it has been shown that employing data-dependent randomization improves the performance in terms of the required number of random features. In this paper, we are concerned with the randomized-feature approach in supervised learning for good generalizability. We propose the Energy-based Exploration of Random Features (EERF) algo","authors_text":"Ahmad Beirami, Shahin Shahrampour, Vahid Tarokh","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-12-19T18:40:36Z","title":"On Data-Dependent Random Features for Improved Generalization in Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07102","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":"sha256:02bc54130c954bbf46ad3012659fe6a26d04cc5d4b9083f02d9dc598d743f125","target":"record","created_at":"2026-05-18T00:27:37Z","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":"6523eff1be80e08cc8d4a4d563fe6caeace36957b51c4f3f8b8620dd958af636","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-12-19T18:40:36Z","title_canon_sha256":"0403fc13315531392288cd4b8cf0fa682f3ebaa2dd7b476e0c01061c0fb74f57"},"schema_version":"1.0","source":{"id":"1712.07102","kind":"arxiv","version":1}},"canonical_sha256":"a7217bb7b548d80e04d599d97477f6e0eb26716c6a32ecaf4f578975f95367a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7217bb7b548d80e04d599d97477f6e0eb26716c6a32ecaf4f578975f95367a7","first_computed_at":"2026-05-18T00:27:37.167507Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:37.167507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pVzfH+MgObxHwHfpVvypLIB422pTCRCmJP4RgwB7quLFyD7lHmd4Ny1PIVJ0s8h12sY5QMKu/8y87RNj+90zCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:37.168250Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.07102","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:02bc54130c954bbf46ad3012659fe6a26d04cc5d4b9083f02d9dc598d743f125","sha256:7e2d2c2af31d6d4d888cbe1485536c355d7162f8fdcf053264a498c1e49ada78"],"state_sha256":"3438837e0668e6e11512f8432d804b126ed902622308fc3a51bc3a0d0a47c726"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SLOkL0wxIL4p6B7uKsvXINtA7i0ezkuJJLZ7J6kEieoloptnuXhB+4eP1xLxkWs8VyFmQV8aMSOVocduRyiiBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:39:00.605054Z","bundle_sha256":"761a6a835e7c5c59b6e0c92d7607665f8c652d1e2ade08aae713a5bf385a16d3"}}