| { |
| "schema_version": 1, |
| "title": "Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification", |
| "emoji": "📐", |
| "space_id": "snaykey/repro-multinomial-logit-design", |
| "paper": { |
| "arxiv_id": "2605.25592", |
| "openreview_id": "FhBOdaIcUw" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-FhBOdaIcUw" |
| ], |
| "updated_at": "2026-07-28T00:00:00+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-theorem-3-3-milp-lmo", |
| "title": "Theorem 3.3 gives a mixed-integer linear programming (MILP) reformulation of the linear maximization oracle (LMO) needed for optimal design in the multinomial logit (MNL) model (Theorem 3.3).", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-theorem-3-5-lifted-frank-wolfe", |
| "title": "Theorem 3.5 establishes a stopping/approximation guarantee for a lifted Frank-Wolfe algorithm that solves the MNL optimal design problem without requiring exact LMO calls (Theorem 3.5).", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-theorem-4-4-sample-complexity", |
| "title": "Theorem 4.4 shows the proposed best assortment identification algorithm achieves sample complexity O~(d log(N/δ)(1/Δmin² + 1/(κΔmin)) + T0), where d is the feature dimension, N is the number of arms/assortments, and Δmin is the minimum reward gap (Theorem 4.4).", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-corollary-4-5-lifted-design", |
| "title": "Corollary 4.5 shows that using the lifted G-optimal design in place of the MILP-based design multiplies the sample complexity by a factor of only (1+ε_lift), while remaining computationally tractable (Corollary 4.5).", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-table-1-lmo-runtime", |
| "title": "Table 1 reports average LMO runtime comparisons across methods (MILP vs. lifted surrogate) for varying problem sizes (Table 1).", |
| "children": [] |
| }, |
| { |
| "slug": "claim-6-figure-1-stopping-time", |
| "title": "Figure 1 reports the average stopping time τ of the best assortment identification algorithm as N (number of arms) is varied over {30, 50, 100, 200} and K (assortment size) over {3, 4, 5}, using feature dimension d=5 and failure probability δ=0.05 (Figure 1).", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "children": [] |
| } |
| ] |
| }, |
| "agent_view_tokens": 0, |
| "revision": 1 |
| } |
