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Open access Sep 2026

Information-Preserving and Model-Aware Voronoi Dequantization of Weighted Discrete Laws

Continuous dequantization embeds discrete data into a continuous space, but relaxation can alter the statistical information carried by the original categories. We study a complementary regime in which a specified weighted discrete law is the target and the dequantizer is required to be lossless under a prescribed quan...

Maha Moussa, Khater A. E. Gad, H. Hamouda et al. · 0 citations
Preprint Aug 2026

Transport based embeddings with topological guarantees

The condensation method for recovering the circle of camera angles from the COIL image dataset is demonstrated, where a standard PCA pipeline produces spurious homology, and the quotient of views of a tetrahedron in the SYMSOL pose-estimation benchmark is demonstrated.

Erik Carlsson, J. Carlsson · 0 citations
Preprint Aug 2026

Absorption Probabilities for Random Convex Hulls: Distribution-Freeness via the Wall-Crossing Method

We consider the probability that the convex hull of the first $n$ partial sums of a $d$-dimensional random walk contains the origin. Under symmetric exchangeability of the increments and a general-position assumption, this absorption probability is distribution-free and admits an explicit formula, previously obtained b...

Z. Kabluchko, A. Tarasov · 0 citations
Preprint Sep 2026

Differentially Private Approximation of the John Ellipsoid

We study the problem of approximating the John ellipsoid (JE) of a given (centrally symmetric) polytope of $n$ constraints in a Euclidean space under differential privacy (DP). We give the first differentially private algorithm for this problem under the standard model, where neighboring datasets may differ arbitrarily...

Bar Mahpud, D. Omer, Or Sheffet · 0 citations
Preprint Aug 2026

Deterministic Johnson--Lindenstrauss Projections from Pisot $\beta$-Transformations for Zero-Knowledge Private Routing

It is proved that the induced squared-norm estimator is unbiased up to a term decaying geometrically with a sampling gap, and that its variance is a constant $V_0/m$ that is dimension-free in experiment and, under one stated concentration hypothesis, in theory.

I. Dey, I. Cherkaoui · 0 citations
#machine learning Preprint Sep 2026

Distribution-free inference on the number of changepoints

Suppose we are given an ordered sequence of independent data whose distribution changes $K$ times at unknown locations, for some unknown $K \geq 0$. In this paper, we study the problem of performing distribution-free inference on $K$. First, we show an impossibility result: any distribution-free upper confidence bound...

Rohan Hore, Aaditya Ramdas · 0 citations

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