Hugging Face Trending Papers
The Price of List Replicability: Tight Bounds for Bias Estimation
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arXiv Machine Learning
Jul 13
A Fourier analytique approach to Gaussian mixture learning
arXiv:2004. 05813v3 Announce Type: replace-cross Abstract: Suppose that we are given independent, identically distributed random samples $x_1,\cdots,x_n$ from a mixture at most $k$ many $d$-dimensional spherical Gaussian distributions $\mu_1,\cdots,\mu_{k_0}$ of identical and known variance $\sigma^2$ in each coordinate, such that the minimum $\ell^2$ distance between two distinct centers $y_l$ and $y_j$ is greater than $2\Delta\sigma \min\{\sqrt{d},\sqrt k\}$, where $\Delta>C_0$, and $C_0$ is a sufficiently large universal constant.
arXiv Machine Learning
Jun 18
How fast can you find a good hypothesis?
arXiv:2509. 03734v3 Announce Type: replace-cross Abstract: In the hypothesis selection problem, we are given sample and query access to finite set of candidate distributions (hypotheses), $\mathcal{H} = \{H_1, \ldots, H_n\}$, and samples from an unknown distribution $P$, both over a domain $\mathcal{X}$.
arXiv Machine Learning
Oct 21
Quadratic Sample Complexity for Strictly Replicable Realizable Learning with Unknown Marginals
arXiv:2610. 26944v1 Announce Type: cross Abstract: Under the quantifier $\exists A\,\forall D$, where the realizable distribution and its marginal are unknown, we determine the finite-class sample-complexity exponent for strict replicability: two independent samples and one shared seed must produce identical functions.
arXiv Statistics ML
Oct 26
Strictly Replicable Realizable Learning: Unknown Marginals, Finite-Class Interpolation, and the Cost of Shared Randomness
arXiv:2610. 26944v2 Announce Type: replace Abstract: Strict replicability requires two independent training samples, processed with the same random seed, to produce exactly the same hypothesis.
arXiv Machine Learning
Oct 27
Near-Optimal List Replicability from Littlestone Dimension
arXiv:2610.32853v1 Announce Type: new Abstract: We study the list replicability number in the realizable PAC learning framework and prove that every concept class $H$ over a countable domain with Lit...
arXiv Machine Learning
3d ago
Approximating the Euclidean Ball by Polytopes: Surprising Rates in High Dimensions
arXiv:2610. 34651v2 Announce Type: cross Abstract: We provide sharp estimates on the number of vertices needed to approximate the Euclidean unit ball $B_d\subset\mathbb R^d$ with relative symmetric-difference volume error at most $\varepsilon\in(0,1)$.