arXiv Machine Learning
Accurate Trace Estimation with Fewer Random Bits via Recursive TensorSketch
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 16
Stochastic trace estimation with tensor train random vectors
arXiv:2606. 15679v1 Announce Type: cross Abstract: Stochastic trace estimation is a standard tool for approximating the trace of a large-scale matrix available only through matrix-vector products.
arXiv Statistics ML
Aug 25
Tensor-normal maximum likelihood estimation at the operator-norm sample threshold
arXiv:2608.10488v2 Announce Type: replace-cross Abstract: Let $X_1,\ldots,X_n$ be independent Gaussian tensors in $\mathbb{R}^{d_1}\otimes\cdots\otimes\mathbb{R}^{d_k}$ with a common covariance matri...
arXiv Machine Learning
Aug 14
Active-Trace Complexity Bounds for Moreau--Yosida Unadjusted Langevin Sampling
arXiv:2608. 13467v1 Announce Type: new Abstract: We study the Moreau--Yosida unadjusted Langevin algorithm (MYULA) for the nonsmooth composite target \[ \pi(dx)\propto \exp\{-f(x)-g(x)\}\,dx, \qquad x\in\mathbb R^d, \] where \(f\) is \(m\)-strongly convex with \(L_f\)-Lipschitz gradient and \(g\) is convex and \(G\)-Lipschitz.
arXiv Machine Learning
Aug 11
Logarithmic-Free Moment and Generalization Bounds for Uniformly Stable Algorithms
arXiv:2608. 09870v1 Announce Type: cross Abstract: Uniform stability is a classical tool for controlling the generalization error of a learning algorithm.
arXiv Statistics ML
2d ago
Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model
arXiv:2606.07124v2 Announce Type: replace-cross Abstract: We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two a...
arXiv Machine Learning
Jul 17
Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs
arXiv:2607. 14304v1 Announce Type: cross Abstract: We study sparse random geometric graphs generated by connecting pairs of high-dimensional vectors whose inner product exceeds a threshold.