most citedMetric Entropy of Analytic Function Classes via Ellipsoidal Methods

1 citations · 1 across the 4 of their papers we have counts for

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8 papers

stat.ML2026

Separation Capacity of Scattering Networks on Low-Dimensional Datasets

Konstantin Häberle, Helmut Bölcskei

We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension. The networks we consider employ a fixed monomial non…

stat.ML2026

Function-Counting Theory for Low-Dimensional Data Structures

Konstantin Häberle, Helmut Bölcskei

The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend to exhibit, despite their high-…

math.PR2026

Matrix Discrepancy for Representations of Finite Groups

Afonso S. Bandeira, Helmut Bölcskei

We prove the group version of the Matrix Spencer conjecture. For every finite group , there exist signs such that $$\left\| \sum_{g\in G} \varepsilon_…

math.FA20261 cited

Metric Entropy of Analytic Function Classes via Ellipsoidal Methods

Thomas Allard, Helmut Bölcskei

We present a systematic methodology for characterizing the metric entropy of infinite-dimensional ellipsoids with exponentially decaying semi-axes. The approach does not rely on th…

math.FA2026

Entropy and Minimax Risk of Hypoelliptic Pseudodifferential Operators

Thomas Allard, Helmut Bölcskei

We characterize the entropy and minimax risk of a broad class of compact pseudodifferential operators. Under suitable decay and regularity conditions on the symbol, we combine a We…

math.FA2026

Metric Entropy of Ellipsoids in Banach Spaces: Techniques and Precise Asymptotics

Thomas Allard, Helmut Bölcskei

We develop new techniques for computing the metric entropy of ellipsoids -- with polynomially decaying semi-axes -- in Banach spaces. Besides leading to a unified and comprehensive…