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20242026
most citedConvergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

2 citations · 4 across the 11 of their papers we have counts for

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stat.ML20261 cited

Conditional Coverage Diagnostics for Conformal Prediction

Sacha Braun, David Holzmüller, Michael I. Jordan +1

Evaluating conditional coverage remains one of the most persistent challenges in assessing the reliability of predictive systems. Although conformal methods can give guarantees on…

stat.ML2026

Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks

David Holzmüller, Max Schölpple

In recent years, the neural tangent kernel (NTK) and neural network Gaussian process kernel (NNGP) have given theoreticians tractable limiting cases of fully connected neural netwo…

stat.ML20262 cited

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

David Holzmüller, Francis Bach

Sampling from Gibbs distributions and computing their log-partition function are fundamental tasks in statistics, machine learning, and statistical physics. While efficient algorit…

stat.ML2026

A Variational Estimator for Calibration Errors

Eugène Berta, Sacha Braun, David Holzmüller +2

Calibration$\unicode{x2014}$the problem of ensuring that predicted probabilities align with observed class frequencies$\unicode{x2014}$is a basic desideratum for reliable predictio…

stat.ML2024

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension

Moritz Haas, David Holzmüller, Ulrike von Luxburg +1

The success of over-parameterized neural networks trained to near-zero training error has caused great interest in the phenomenon of benign overfitting, where estimators are statis…