Publications

Publications:

  • M. Behr and A. Munk (2022). Statistical Methods for Minimax Estimation in Linear Models with Unknown Design Over Finite Alphabets. SIMODS (Vol. 4, Issue 2) [here]
  • J. Brutsche and A. Rohde (2022). Sharp adaptive similarity testing with pathwise stability for ergodic diffusions. Annals of Statistics [here]
  • H. Dette and J. Tang (2022). An RKHS approach for pivotal inference in functional linear regression. Statistica Sinica [here]
  • N. Dörnemann and H. Dette (2022). Fluctuations of the diagonal entries of a large sample precision matrix. Statistics & Probability Letters (Vol. 198) [here]
  • N. Dörnemann (2022). Likelihood ratio tests under model misspecification in high dimensions. Journal of Multivariate Analysis (Vol. 193) [here]
  • S. Gaucher and A. Carpentier and C. Giraud (2022). The price of unfairness in linear bandits with biased feedback. Advances in Neural Information Processing Systems 35, pp. 18363–18376. [here]
  • M. Jirak and M. Wahl (2022). Quantitative limit theorems and bootstrap approximations for empirical spectral projectors. Probability Theory and Related Fields [here]
  • L. Jula Vanegas and M. Behr and A. Munk (2022). Multiscale quantile segmentation. Journal of the American Statistical Association 117 (539), 1384-1397 [here]
  • C. Butucea and A. Rohde and L. Steinberger (2023). Interactive versus non-interactive locally differentially private estimation: Two elbows for the quadratic functional. Annals of Statistics 51(2), 464-486. [here]
  • S. Dasgupta and S. Mukhopadhyay and J. Kieth (2023). G-optimal grid designs for kriging models. Scandinavian Journal of Statistics [here]
  • H. Dette and G. Dierickx and T. Kutta (2023). Testing covariance separability for continuous functional data. Journal of Time Series Analysis [here]
  • H. Dette and M. Kroll (2023). A Simple Bootstrap for Chatterjee’s Rank Correlation. Biometrika [here]
  • L. Hucker, M. Wahl (2023). A note on the prediction error of principal component regression in high dimensions. Theory of Probability and Mathematical Statistics 109, 37–53. [here]
  • M. Jirak and M. Wahl (2023). Relative perturbation bounds with applications to empirical covariance operators. Advances in Mathematics 412, 108808 [here]
  • M. Jirak (2023). Weak dependence and optimal quantitative self-normalized central limit theorems. Journal of the European Mathematical Society. [here]
  • T. Kocak and A. Carpentier (2023). Online Learning with Feedback Graphs: The True Shape of Regret. International Conference on Machine Learning. PMLR. 2023, pp. 17260–17282. [here]
  • S. Kovács and P. Bühlmann and H. Li and A. Munk (2023). Seeded binary segmentation: a general methodology for fast and optimal changepoint detection. Biometrika 110 (1), 249-256 [here]
  • E. Pilliat and A. Carpentier and N. Verzelen (2023). Optimal Permutation Estimation in CrowdSourcing problems. The Annals of Statistics 51.3, pp. 935–961. [here]
  • E. M. Saad and N. Verzelen and A. Carpentier (2023). Active Ranking of Experts Based on their Performances in Many Tasks. International Conference on Machine Learning. PMLR. 2023, pp. 29490–29513. [here]
  • L. Steinberger and H. Leeb (2023). Conditional predictive inference for stable algorithms. Annals of Statistics 51(1), 290–311. [here]
  • L. Steinberger (2023). Efficiency in local differential privacy. The Annals of Statistics, 52(5), 2139-2166 [here]
  • P. Bastian and H. Dette and J. Heiny (2024). Testing for practically significant dependencies in high dimensions via bootstrapping maxima of U-statistics. Annals of Statistics 52 (2), 628-653 [here]
  • P. Bastian and H. Dette (2024). Gradual changes in functional time series. Journal of Time Series Analysis [here]
  • G. Blanchard and A. Carpentier and O. Zadorozhnyi (2024). Moment inequalities for sums of weakly dependent random fields. Bernoulli 30(3): 2501-2520 [here]
  • A.v. Delft and H. Dette (2024). A general framework to quantify deviations from structural assumptions in the analysis of nonstationary function-valued processes. Annals of Statistics 52 (2), 550-579 [here]
  • S. Hundrieser and M. Klatt and A. Munk and T. Staudt (2024). A unifying approach to distributional limits for empirical optimal transport. Bernoulli 30 (4), 2846-2877. [here]
  • S Kovács and H. Li and L. Haubner and A. Munk and P. Bühlmann (2024). Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries. Journal of Machine Learning Research 25 (297), 1-64. [here]
  • E. Pilliat and A. Carpentier and N. Verzelen (2024). Optimal rates for ranking a permuted isotonic matrix in polynomial time. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). SIAM. 2024, pp. 3236–3273. [here]
  • E. M. Saad and A. Carpentier and T. Kocák and N. Verzelen (2024). On Weak Regret Analysis for Dueling Bandits. The Thirty-eighth Annual Conference on Neural Information Processing Systems. [here]
  • C. Strothmann and H. Dette and K. F. Siburg (2024). Rearranged dependence measures. Bernoulli 30(2),pp. 1055-1078 [here]
  • P. Beckedorf and A. Rohde (2025). Non-uniform bounds and Edgeworth expansions in self-normalized limit theorems. Journal of Theoretical Probability [here]

To Appear:

  • J. Chhor and A. Carpentier (2022). Goodness-of-fit testing for Hölder-continuous densities: Sharp local minimax rates. Bernoulli [here]
  • N. Dörnemann and H. Dette (2023). A CLT for the difference of eigenvalue statistics of sample covariance matrices. Bernoulli [here]
  • H. Chen and H. Dette, H. and J. Yu (2024). Multi-resolution subsampling for large-scale linear classification. Journal of the Royal Statistical Society, Series B [here]
  • S. Hundrieser and F. Heinemann and M. Klatt and M. Strulewa and A. Munk (2024+). Unbalanced Kantorovich-Rubinstein distance and barycenter for finitely supported measures: A statistical perspective. Journal of Machine Learning Research [here]
  • N. Kalinin and L. Steinberger (2024). Efficient estimation of a Gaussian mean with local differential privacy. Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS) [here]
  • J. Tang and H. Dette (2024). Simultaneous semiparametric inference for single-index models. Bernoulli [here]
  • C. Butucea and A. Meister and A. Rohde (2025). Asymptotic equivalence of locally stationary processes and bivariate Gaussian white noise. The Annals of Statistics [here]
  • A. Delaigle and A. Meister and Zhang, J. (2025). Nonparametric curve estimation in measurement error problems with conditionally heteroscedastic variances. Bernoulli

Preprints:

  • N. Dörnemann and J. Heiny (2022). Limiting spectral distribution for large sample correlation matrices (Preprint)
  • S. Kovács and H. Li and L. Haubner and A. Munk and P. Bühlmann (2022). Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries (Preprint)
  • M. Ai and H. Dette and Z. Liu and J. Yu (2023). A reinforced learning approach to optimal design under model uncertainty (Preprint)
  • S. Dasgupta and H. Dette (2023). Efficient subsampling for exponential family models (Preprint)
  • H. Dette and M. Kroll (2023). A Simple Bootstrap for Chatterjee’s Rank Correlation (Preprint)
  • A. Mösching and H. Li and A. Munk (2023). Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models. (Preprint)
  • N. Amann and H. Leeb and L. Steinberger (2024). Assumption-lean conditional predictive inference via the Jackknife and the Jackknife+ (Preprint)
  • P. Bastian and H. Dette (2024). Gradual changes in functional time series (Preprint)
  • B. Deitmar (2024). A recursion formula for mixed trace moments of isotropic Wishart matrices and the Gaussian unitary/orthogonal ensembles (Preprint)
  • B. Deitmar (2024). Marchenko-Pastur laws for Daniell smoothed periodograms without simultaneous diagonalizability (Preprint)
  • H. Dette and J. Tang (2024). New energy distances for statistical inference on infinite dimensional Hilbert spaces without moment conditions (Preprint)
  • H. Dette and A. Rohde (2024). Nonparametric bootstrap of high-dimensional sample covariance matrices (Preprint)
  • N. Döremann and D. Paul (2024). Detecting Spectral Breaks in Spiked Covariance Models (Preprint)
  • N. Dörnemann, H. Dette (2024). Detecting Change Points of Covariance Matrices in High Dimensions (Preprint)
  • M. Graf and A. Carpentier and N. Verzelen (2024). Optimal level set estimation for non-parametric tournament and crowdsourcing problems (Preprint)
  • H. Holzmann and A. Meister (2024). Multivariate root-n-consistent smoothing parameter free matching estimators and estimators of inverse density weighted expectation (Preprint)
  • L. Hucker and M. Reiß (2024). Early stopping for conjugate gradients in statistical inverse problems (Preprint)
  • M. Jirak and S. Minsker and Y. Shen and M. Wahl (2024). Concentration and moment inequalities for heavy-tailed random matrices (Preprint)
  • M. Jirak and G. Köstenberger (2024). Sharp oracle inequalities and universality of the AIC and FPE (Preprint)
  • G. Köstenberger and T. Stark (2024). Robust signal recovery in Hadamard spaces (Preprint)
  • Ö. Askin and H. Dette and M. Dunsche and T. Kutta and Y. Lu and Y. Wei and V. Zikas (2025) General-Purpose f-DP Estimation and Auditing in a Black-Box Setting (Preprint)
  • J. Brutsche and A. Rohde (2025). The level of self-organized criticality in oscillating Brownian motion: n-consistency and stable Poisson-type convergence of the MLE (Preprint)
  • B. Deitmar (2025). Eigen-inference by Marchenko-Pastur inversion (Preprint)
  • M. Graf and V. Thuot and N. Verzelen (2025). Clustering Items through Bandit Feedback: Finding the Right Feature out of Many (Preprint)
  • A. Hüselitz and H. Li and A. Munk (2025). Optimal and fast online change point estimation in linear regression (Preprint)
  • D. Kieffer and A. Rohde (2025). The weak-feature-impact effect on the NPMLE in monotone binary regression (Preprint)
  • H. Li and Z. Liu and A. Munk (2025). Adaptive monotonicity testing in sublinear time (Preprint)
  • Z. Liu and H. Li (2025). Multiscale Quantile Regression with Local Error Control (Preprint)
  • T. Stark and L. Steinberger (2025). Implicit vs. explicit regularization for high-dimensional gradient descent (Preprint)

Github Repositories: