New algorithms compute provably tight sensitivity bounds for matrix rows, yielding smaller coresets for LMS approximation of affine k-subspaces via an iterative exact method and a dimensionality-reduction trick.
New frameworks for offline and streaming coreset constructions
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
HullFT performs test-time finetuning by sparse convex reconstruction of query embeddings followed by gradient caching on repeated examples, yielding better quality-efficiency tradeoffs than prior TTFT methods.
The thesis gives the first non-trivial dual fault-tolerant pairwise reachability preservers of size O(n^{4/3}|P|^{1/3}) and new approximation algorithms plus a streaming method for fair clustering in graphs.
MinDist sketches using O(d/ε²) points preserve relative error for hyperplanes and Õ((L/ρ)·1/ε²) points for 2D shapes with min-distance ρ in domain L, with k³ factors and exact reconstruction for k-piece trajectories.
citing papers explorer
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Tight Sensitivity Bounds For Smaller Coresets
New algorithms compute provably tight sensitivity bounds for matrix rows, yielding smaller coresets for LMS approximation of affine k-subspaces via an iterative exact method and a dimensionality-reduction trick.
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Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching
HullFT performs test-time finetuning by sparse convex reconstruction of query embeddings followed by gradient caching on repeated examples, yielding better quality-efficiency tradeoffs than prior TTFT methods.
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Creating Robust and Fair Graph Structures for Connectivity and Clustering
The thesis gives the first non-trivial dual fault-tolerant pairwise reachability preservers of size O(n^{4/3}|P|^{1/3}) and new approximation algorithms plus a streaming method for fair clustering in graphs.
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Sketched MinDist
MinDist sketches using O(d/ε²) points preserve relative error for hyperplanes and Õ((L/ρ)·1/ε²) points for 2D shapes with min-distance ρ in domain L, with k³ factors and exact reconstruction for k-piece trajectories.