arXiv Machine Learning
When Weak Submodularity Meets Matroids: How Does Standard Greedy Behave?
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Poisson Exchange Beyond Submodularity: Effective Approximation Algorithms for Offline and Online Subset Selection over Matroids
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A General $\widetilde{\Omega}(\sqrt{T \gamma_T})$ Lower Bound for Kernel Bandits
The paper establishes a general minimax regret lower bound of Ω(√{Tγ_T/ log T}) for kernel bandits with non‑constant continuous kernels on compact domains, matching existing upper bounds up to logarithmic factors. It shows that this log factor is unavoidable in general but can be removed under specific conditions. Consequently, the optimal scaling is exactly Θ(√{Tγ_T}) for several common kernels, including Matérn‑ν (ν∈(0,2)), γ‑exponential (γ∈(0,2)), and certain piecewise‑polynomial kernels.
arXiv Machine Learning
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Geometry-Dependent Bounds for Online Non-Monotone DR-Submodular Maximization
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arXiv AI
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Adversarial Resilience of Poisson-Process Submodular Maximization over Matroids, and Full-Bandit Learning
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arXiv Machine Learning
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Geometry-Dependent Approximation for Non-Monotone $k$-Submodular Maximization
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arXiv Machine Learning
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Sharp Minimax Regret for Infinite-Memory Logistic Prediction
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