{ "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" }