Extending curvature to all submodular functions yields the first multiplicative greedy approximation guarantees that apply even when the function takes negative values.
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Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin
Canonical reference. 80% of citing Pith papers cite this work as background.
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2026 13representative citing papers
A new algorithm finds a matroid basis in tilde O(n to the 3/7) adaptive rounds via independence oracle.
ContextualJailbreak uses evolutionary search over simulated primed dialogues with novel mutations to reach 90-100% attack success on open LLMs and transfers to some closed frontier models at 15-90% rates.
A covariance-adapting algorithm for semi-bandits achieves asymptotically tight regret bounds under a new sub-exponential distribution family, with direct application to sparse rewards.
The observability and controllability Gramians parameterized by sensor and actuator node subsets are determinantal point processes.
SeedER uses initial dense seeding followed by RL-driven selective expansion to improve recall on compositional KG queries while limiting candidate set size.
SkillLens organizes skills into policies-strategies-procedures-primitives layers, retrieves via degree-corrected random walk, and uses a verifier for local adaptation, yielding up to 6.31 pp gains on MuLocbench and raising ALFWorld success from 45% to 51.31%.
A reformulation of Bayesian OED as dense matrix subset selection plus a pipelined Schur-complement greedy algorithm on hundreds of GPUs enables optimization of 175-sensor networks for billion-degree-of-freedom tsunami models with near-perfect scaling.
A question-adaptive greedy frame selector combines SigLIP relevance and DINOv2 coverage under a submodular objective with a text classifier routing to preset trade-offs, yielding accuracy gains on MLVU especially at low frame budgets.
Revenue maximization for pricing datasets to budget-constrained buyers is APX-hard, with a 2-approximation for online arrivals and a (1-1/e)^{-1}-approximation for offline.
Selective prediction abstains unless all Lipschitz-consistent heads in the version space agree on a certified label for each pool point.
RCD balances relevance, coverage, and diversity in a knapsack-constrained selection framework, with experiments showing that selector choice and budget level determine optimal unitization strategies on clinical datasets.