Spectral-Residual Continuous Greedy for Tensor Sampling
A finite-step approximation guarantee that approaches the classical $1-1/e$ factor as direction certification becomes exact and finite-step residual error vanishes is established.
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A finite-step approximation guarantee that approaches the classical $1-1/e$ factor as direction certification becomes exact and finite-step residual error vanishes is established.
Meta-LinEXP3 is proposed, an online-within-online algorithm that constructs a predictable task-level prior from completed tasks to guide the inner LinEXP3 learner and establishes a direct connection between prior accuracy and transfer regret, showing that increasingly accurate priors yield sublinear transfer-dependent...
A hybrid distance-spectral encoding that combines anchor-distance profiles with quantized low-frequency Laplacian-energy coordinates is studied, showing that hybrid encodings better recover syntactic-tree geometry than distance-only or spectral-only baselines.
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