File size: 3,879 Bytes
f7a22ba 078cc41 f7a22ba 078cc41 f7a22ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | {
"schema_version": 2,
"title": "Reproduction: Near-Optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation",
"emoji": "🎯",
"space_id": "SabaPivot/repro-near-optimal-and-efficient-first-order-algorithm-for-multi-task-learning-with-shared-linea",
"paper": {
"arxiv_id": "2605.00473"
},
"tags": [
"icml2026-repro",
"paper-TnquAvyTtL"
],
"updated_at": "2026-07-25T04:00:54+00:00",
"root": {
"slug": "index",
"title": "Reproduction: Near-Optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation",
"file": "pages/index.md",
"children": [
{
"slug": "executive-summary",
"title": "Executive summary",
"file": "pages/executive-summary/page.md",
"children": []
},
{
"slug": "claim-1-tpgd-algorithm-1-is-a-two-phase-first-order-gradient-descent-method",
"title": "Claim 1: TPGD (Algorithm 1) is a two-phase first-order gradient-descent method",
"file": "pages/claim-1-tpgd-algorithm-1-is-a-two-phase-first-order-gradient-descent-method/page.md",
"children": []
},
{
"slug": "claim-2-phase-i-performs-k1-2-iterations-of-unregularized-gradient-descent",
"title": "Claim 2: Phase I performs K1/2 iterations of unregularized gradient descent",
"file": "pages/claim-2-phase-i-performs-k1-2-iterations-of-unregularized-gradient-descent/page.md",
"children": []
},
{
"slug": "claim-3-theorem-5-1-and-corollary-5-3-prove-the-method-attains",
"title": "Claim 3: Theorem 5.1 and Corollary 5.3 prove the method attains",
"file": "pages/claim-3-theorem-5-1-and-corollary-5-3-prove-the-method-attains/page.md",
"children": []
},
{
"slug": "claim-4-the-algorithm-achieves-1-iteration-complexity-i-e-convergence-in",
"title": "Claim 4: The algorithm achieves Õ(1) iteration complexity, i.e., convergence in",
"file": "pages/claim-4-the-algorithm-achieves-1-iteration-complexity-i-e-convergence-in/page.md",
"children": []
},
{
"slug": "claim-5-the-estimation-error-guarantee-requires-a-per-task-sample-size-of",
"title": "Claim 5: The estimation-error guarantee requires a per-task sample size of",
"file": "pages/claim-5-the-estimation-error-guarantee-requires-a-per-task-sample-size-of/page.md",
"children": []
},
{
"slug": "claim-6-theorem-5-4-establishes-excess-risk-bounds-for-transferring-the-learned",
"title": "Claim 6: Theorem 5.4 establishes excess-risk bounds for transferring the learned",
"file": "pages/claim-6-theorem-5-4-establishes-excess-risk-bounds-for-transferring-the-learned/page.md",
"children": []
},
{
"slug": "conclusion",
"title": "Conclusion",
"file": "pages/conclusion/page.md",
"children": []
}
]
},
"traces": [],
"workspace": {
"file": "workspace.json",
"file_count": 0,
"total_size": 0,
"bucket_id": null
},
"agent_view_tokens": 8404,
"trace_view_tokens": 1850621,
"workspace_view_tokens": 309,
"revision": "978198954c593c9f2233",
"traces_ref": {
"repo_id": "SabaPivot/icml26-tnquavyttl-traces",
"repo_type": "dataset",
"repo_url": "https://huggingface.co/datasets/SabaPivot/icml26-tnquavyttl-traces",
"private": true
},
"trace_dataset": "https://huggingface.co/datasets/SabaPivot/icml26-tnquavyttl-traces",
"workspace_ref": {
"repo_id": "SabaPivot/icml26-tnquavyttl-artifacts",
"repo_type": "bucket",
"repo_url": "https://huggingface.co/buckets/SabaPivot/icml26-tnquavyttl-artifacts",
"private": true
},
"workspace_bucket": "https://huggingface.co/buckets/SabaPivot/icml26-tnquavyttl-artifacts"
} |