Beyond submodular maximization via one-sided smoothness
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Bibliographic record
Abstract
Abstract The multilinear framework for submodular maximization was developed to achieve a tight $$1-1/e$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mi>e</mml:mi> </mml:mrow> </mml:math> approximation for maximizing a monotone submodular function subject to a matroid constraint, including as special case the submodular welfare problem. The framework has a continuous optimization step (solving the multilinear extension of a submodular function) and a rounding part (rounding a fractional solution to an integral one). We extend both parts to provide a framework for a wider array of applications. The continuous part works for a more general class of continuous functions parameterized by a new smoothness parameter $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> . A twice differential function F is called $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> -one-sided-smooth ( $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> -OSS) if its second derivatives are bounded as follows: $$\frac{1}{2}u^T\nabla ^2 F(x) u \le \sigma \cdot \frac{\Vert u\Vert _1}{\Vert x\Vert _1} u^T \nabla F(x)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> <mml:msup> <mml:mi>u</mml:mi> <mml:mi>T</mml:mi> </mml:msup> <mml:msup> <mml:mi>∇</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mi>F</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>u</mml:mi> <mml:mo>≤</mml:mo> <mml:mi>σ</mml:mi> <mml:mo>·</mml:mo> <mml:mfrac> <mml:msub> <mml:mrow> <mml:mo>‖</mml:mo> <mml:mi>u</mml:mi> <mml:mo>‖</mml:mo> </mml:mrow> <mml:mn>1</mml:mn> </mml:msub> <mml:msub> <mml:mrow> <mml:mo>‖</mml:mo> <mml:mi>x</mml:mi> <mml:mo>‖</mml:mo> </mml:mrow> <mml:mn>1</mml:mn> </mml:msub> </mml:mfrac> <mml:msup> <mml:mi>u</mml:mi> <mml:mi>T</mml:mi> </mml:msup> <mml:mi>∇</mml:mi> <mml:mi>F</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> for all $$u,x\ge 0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>u</mml:mi> <mml:mo>,</mml:mo> <mml:mi>x</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> , $$x\ne 0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>x</mml:mi> <mml:mo>≠</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> . For $$\sigma =0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> this includes previously studied continuous DR-Submodular functions as well as quadratics defined by copositive matrices. We give a modification of the continuous greedy algorithm which finds a solution for maximizing a monotone $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> -OSS F over a polytope in the non-negative orthant; the solution approximates the optimum to within factors which are functions of $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> which depend on additional properties. Interestingly, $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> -OSS functions arise as the multilinear extensions of set functions associated with several well-studied diversity maximization problems: $$\max f(S) = \sum _{i,j \in S} A_{ij} : |S| \le k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>max</mml:mo> <mml:mi>f</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mm
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it