Scaffold-Constrained Subset Dynamic Programming for Exact SSE Clustering

arXiv:2609.30477 · math.ST, cs.LG, stat.TH · Submitted 2026-09-24 · Read on arXiv

math.ST, cs.LG, stat.TH

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Clustering — 33× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Pruning — 6× in this paper · explained in 4 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Additivity — 3× in this paper · explained in 1 episode
• Sparsity — 3× in this paper · explained in 4 episodes
• Structural Control — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Graph Construction — 2× in this paper · explained in 3 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• VC Dimension — 2× in this paper · explained in 2 episodes
• Anisotropy — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Local Optimization — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Objective Mismatch — 1× in this paper · explained in 2 episodes
• University of California Irvine — 1× in this paper

Sources

• Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D
• How to sample connected $K$-partitions of a graph

Related papers

• Conformal Prediction for Dyadic Regression Under Complex Missingness
• Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel
• Bentkus-type asymptotic e-values
• High-Dimensional Asymptotics of Differentially Private PCA
• A proximal gradient algorithm for composite log-concave sampling
• KL Convergence Guarantees for Score diffusion models under minimal data assumptions