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retag: logbook.json (verbatim titles)
Browse files- logbook.json +31 -27
logbook.json
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{
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"schema_version": 1,
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"title": "
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"emoji": "
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"space_id": "snaykey/repro-flashoptim",
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"paper": {
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"openreview_id": "
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},
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"tags": [
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"icml2026-repro",
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"paper-
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],
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"updated_at": "2026-07-
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"root": {
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"slug": "index",
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"title": "
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"file": "pages/index.md",
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"children": [
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"children": []
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}
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"agent_view_tokens": 7777,
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"revision": "1784570467807071400"
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}
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{
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"schema_version": 1,
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"title": "Reproduction: All ERMs Can Fail in Stochastic Convex Optimization (Lower Bounds in Linear Dimension)",
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"emoji": "📉",
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"space_id": "snaykey/repro-flashoptim",
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"paper": {
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"openreview_id": "Lzwp2KXedc"
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},
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"tags": [
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"icml2026-repro",
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"paper-Lzwp2KXedc"
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],
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"updated_at": "2026-07-30T00:00:00Z",
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"root": {
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"slug": "index",
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"title": "Reproduction: All ERMs Can Fail in Stochastic Convex Optimization (Lower Bounds in Linear Dimension)",
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"file": "pages/index.md",
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"children": [
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{
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"slug": "executive-summary",
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"title": "Executive summary",
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"file": "pages/executive-summary/page.md",
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"children": []
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},
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{
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"slug": "claim1-thm1-all-erms-fail-linear-dim",
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"title": "In dimension d = 6m, there exists a stochastic convex optimization instance where every ε-ERM solution with ε = Θ(m^{-3/2}) incurs constant excess risk and fails to generalize (Theorem 1).",
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"file": "pages/claim1-thm1-all-erms-fail-linear-dim/page.md",
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"children": []
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},
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{
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"slug": "claim2-thm2-strongly-convex-rate",
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"title": "For λ-strongly convex losses with m^{-3/2} ≤ λ ≤ m^{-1/2}, any ε-ERM solution w_S satisfies F(w_S) − min F(w) ≥ Ω(1/(λ m^{3/2})) (Theorem 2).",
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"file": "pages/claim2-thm2-strongly-convex-rate/page.md",
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"children": []
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},
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{
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"slug": "claim3-cor3-gd-constant-error",
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"title": "When ηT = Ω(m^{3/2}), gradient descent incurs constant generalization error with high probability, as a corollary of the ERM lower bound (Corollary 3).",
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"file": "pages/claim3-cor3-gd-constant-error/page.md",
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"children": []
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},
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{
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"slug": "claim4-thm4-gd-excess-risk-rate",
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"title": "Gradient descent's excess risk is lower-bounded by F(w_S^{GD}) − min F(w) = Ω(min{√(ηT/m^{3/2}), 1}) (Theorem 4).",
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"file": "pages/claim4-thm4-gd-excess-risk-rate/page.md",
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"children": []
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},
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{
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"slug": "claim5-thm4-narrowing-gap",
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"title": "This new generalization lower bound of Ω(ηT + √(ηT/m^{3/2})) for constrained gradient descent narrows the previously exponential gap to the best known upper bound of O(ηT + ηT/m) (Theorem 4).",
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"file": "pages/claim5-thm4-narrowing-gap/page.md",
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"children": []
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},
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{
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"slug": "claim6-construction-code-link-function",
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"title": "The construction uses Feldman's asymptotically good binary code combined with a novel 'link function' mapping sample information to bad ERM solutions while preserving convexity, giving the first proof that all approximate ERMs fail in linear dimension (Section on technical construction / proof of Theorem 1).",
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"file": "pages/claim6-construction-code-link-function/page.md",
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"children": []
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},
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{
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"children": []
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}
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]
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}
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}
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